series publication University Aalto DISSERTATIONS OCTORAL D / 2018 for schedules building usage xtensive E simulation completed with nergy e hot

Transcription

1 D e p a r t m e n t o f C i v i l E n g i n e e r i n g E xtensive building usage s chedules for energy s imulation completed with d etailed consumption of domestic hot water Kaiser Ahmed D O C T O R A L D I S S E R T A T I O N S

2 series publication University Aalto DISSERTATIONS OCTORAL D / 2018 for schedules building usage xtensive E simulation completed with nergy e hot domestic etailed consumption of d water Ahmed Kaiser of Doctor of degree the for completed dissertation doctoral A the of permission the with defended, be to (Technology) cience S held examination public a at Engineering, of School University Aalto at 2018 December of 18th on school the of R1 hall lecture the t a 12:00. University Aalto Engineering of School Engineering Civil of Department Technology Environment Indoor

3 professor Supervising Finland University, Aalto Puttonen, Jari Professor advisor Thesis Finland University, Aalto Kurnitski, Jarek Professor Adjunct examiners Preliminary Denmark University, Aalborg Afshari, Alireza Professor Finland Finland, of Centre Research Technical VTT Scientist, Senior Hasan, Ala Doctor Opponent Denmark University, Aalborg Afshari, Alireza Professor series publication University Aalto DISSERTATIONS OCTORAL D / d Ahme Kaiser SBN I (printed) SBN I (pdf) SSN I (printed) SSN I (pdf) ttp://urn.fi/urn:isbn: h Oy Unigrafia elsinki H 2018 Finland book): (printed orders Publication kaiser.ahmed@aalto.fi

4 A alto University, P.O. Box 11000, FI Aalto Abstract Author Kaiser Ahmed Name of the doctoral E xtensive building usage of domestic hot water P ublisher U nit Series F ield dissertation schedules for School of Engineering Department of Civil Engineering energy simulation completed with detailed consumption A alto University publication series D OCTORAL DISSERTATIONS 242/ 2018 of research M anuscript P ermission Monograph submitted Building Usage Profiles, Energy Use in Building 7 September 2018 to publish granted (date) 12 November 2018 A rticle dissertation Date of the defence 18 December 2018 Language English Essay dissertation Abstract The main purpose of this study was to develop building usage profiles for usages such as domestic h ot water (DHW), occupancy, lighting, and appliances to support the knowledge-based energy estimation for low and nearly zero energy buildings. O ur study developed the monthly and hourly DHW usage profiles for Finnish apartment b uildings based on onsite measured data. The average daily DHW usage was 43 L/(person. day) w hereas the minimum and maximum were reported during July (37 L/(person. day)) and N ovember (47 L/(person. day)), respectively. The daily usage mostly varied between L /(person. day). Moreover, a specific selection procedure was used to develop the DHW hourly u sage profiles for multiple occupant groups. These profiles had two sharp usage peaks in the morning and evening. DHW usage was 2-4 times higher during peak hours compared to non-peak hours. Also, usage peak at morning shifted 2-3 hours later during weekends (WE), whereas evening u sage remained in the same period as on weekdays (WD). In addition, smaller occupant groups h ad higher DHW usages during peak hours than the larger groups. Furthermore, a bottom-up model was formulated to quantify the correlation of DHW usage patterns, which could predict the hourly usage of DHW for any unknown datasets. The developed equations can be used directly in simulation tools for estimating the DHW volume and corresponding energy use. T he development of hourly schedules of occupancy, appliances, and lighting for ten building c ategories are also reported separately for WD and WE, based on the synthesis of existing literature. This approach makes it possible to consider the static behaviors of occupancy, lighting, and appliances, which require limited input data. Heat emission modelling from occupant bodies in 10 building categories has shown the contribution of heat and humidity from the occupants. F inally, the developed profiles were used in a simulation tool with the intention of sizing the m onovalent ground source heat pump (GSHP) power for a Finnish single-family house. An a lternate control system for space and DHW heating was developed in a plant model, making it possible to provide 100% of heat for space and DHW heating. In addition, heating power equations were developed which are suitable for computing any type of heat pump sizing power that follows t he same alternate operation principle and hydronic heating facilities. The developed equations predicted the heating power of GSHP with variations of 0-2.2% compared to the simulated results. M oreover, DHW heating accounted for 13-26% and 21-41% of total heating power at design o utdoor temperatures of -26 C and -15 C, respectively. In contrast, internal heat gains from occupancy, lighting, and appliances reduced the total heating power by 3-19%. The results showed t hat internal heat gains reduced the GSHP power by 0.62 kw, which was equivalent to the contribution of DHW heating power (0.63 kw) for a single-family house with three occupants. K eywords I SBN (printed) I SSN (printed) L ocation P ages 170 DHW usage profiles; Building usage profiles; Heat gain; GSHP of publisher H elsinki L ocation I SBN (pdf) I SSN (pdf) of printing H elsinki Y ear 2018 u rn h ttp://urn.fi/urn:isbn:

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6 Preface First of all, I would like to thank Almighty Allah for giving me the opportunity, knowledge and strength to pursue this research. The work was carried out at the Department of Civil Engineering, Aalto University. Many projects provided financial support, such as the European Union s Horizon 2020 research and innovation program, TOXICPM and Ruukki Construction Oy Functional Building. This work was also supported by an Aalto School of Engineering scholarship, K.V. Lindholms Stiftelse personal grant and a Säätiö L.V.Y. personal grant. I am very grateful to all financial supporters. I would like to thank my Supervisor Prof. Jari Puttonen for giving me the opportunity to join the Indoor Environment Technology Group at the Department of Civil Engineering, Aalto University. I am extremely grateful to my Advisor Adj. Prof. Jarek Kurnitski for giving me the opportunity to work under his direct supervision. His knowledge, working and thinking strategies, and unique personality always encourage me to put my best effort into my thesis. I am very thankful to him for his advice, guidance and constructive comments on scientific writing during my studies. My deepest gratitude goes to my Master s thesis supervisor Dr. Piia Sormunen for her great support during the thesis period and in the transition to doctoral studies. I am also very grateful to Mika Vuolle for his unconditional advices even on his sick leave period in order to develop the simulation models. I would like to thank Prof. Alireza Afshari and Senior Scientist Dr. Ala Hasan for reviewing and commenting on this work. Very warm thanks belong to Petri Pylsy and Dr. Andrea Ferrantelli for valuable advices about scientific writing. Additionally, special thanks go to all my coauthors, colleagues and group members. Thanks to secretaries Elsa Nissinen Narbo and Kristiina Hallaselkä for all their support. I would like to thank Dr. Esko Sistonen and laboratory manager Jukka Piironen for their technical support during onsite measurements. I owe my gratitude to my friends Dr. Kashif Nizam Khan, Dr. Mazidul Islam, Suzan Miah, Mahbub Alam, Mostafizur Rahman, F M Mahafugur Rahman, Naimul Islam, Monjurul Haq, Anik Nath and many more for making my life easy and give their support at difficult times in Finland and Germany. Also, I would like to thank my best friend Rafiqul Islam Mitu for his unconditional care since childhood. 1

7 Preface Finally and most importantly, I would like to thank my parents, Rokeya Khatun and Mansur Ahmed, my brothers Maksud Miah and Sarwar Ahmed and my sister Shahana Akter for supporting my idea to pursue higher studies. I owe my gratitude to my lovely son Tawaf Ahmed and am very thankful to my wife Nazma Akter Nazu for her patience and persistent support during my studies. In addition, my parents is of the highest importance to me and I would like to dedicate this thesis to my parents. Espoo, Finland, November 08, 2018, Kaiser Ahmed 2

8 Contents Preface... 1 Contents... 3 List of Publications... 5 Author s Contribution...7 List of Figures... 9 List of Tables Notations Introduction Motivation Research questions, objectives and scope Contributions to the scientific field Dissertation outline Background Factors influencing DHW consumption Technique and tools used for DHW usages estimation Building usage profiles Profile application Sizing of heat pump power Methods Onsite data measurements Description of measurement data for a monthly DHW profile Description of measurement data for an hourly DHW profile Data analysis Heat load estimation and background of usage profiles Internal heat load Background information on profiles

9 Contents 3.4 Implementation of usage profiles for heating system sizing Simulation example Results and Analysis DHW usage at daily level DHW usage at building level DHW usage at apartment level DHW usage at occupant level DHW usage at hourly level Usage frequency at hourly level Hourly DHW usage profiles Example of DHW profiles setting for simulation tools Mathematical formulation of profiles Reference groups correlations and energy use formula Correlation between datasets and chosen structural dataset DHW usage ratio DHW usages ratio at daily level DHW usage ratio at hourly level Internal heat load Building usage schedule structure Application of profiles GSHP and control system model Operational principle of control system GSHP power comparison Sizing of GSHP power Conclusions References

10 List of Publications This doctoral dissertation consists of an overview of the following publications, which are referred to in the text by their Roman numerals. I. Kaiser Ahmed, Petri Pylsy, and Jarek Kurnitski. Monthly domestic hot water profiles for energy calculation in Finnish apartment building. Energy and Buildings, Volume 97, pp.77 85, June II. III. IV. Kaiser Ahmed, Petri Pylsy, and Jarek Kurnitski. Hourly consumption profiles of domestic hot water for different occupant groups in dwellings. Solar Energy, Volume 137, pp , November Andrea Ferrantelli, Kaiser Ahmed, Petri Pylsy, and Jarek Kurnitski. Analytical modelling and prediction formulas for domestic hot water consumption in residential Finnish apartments. Energy and Buildings, Volume 143, pp , May Kaiser Ahmed, Ali Akhondzada, Jarek Kurnitski, Bjarne Olesen. Occupancy schedules for energy simulation in New pren and ISO/FDIS standards. Sustainable Cities and Society, Volume 35, pp , November V. Kaiser Ahmed, Jevgeni Fadejev, and Jarek Kurnitski. Modelling alternate operation ground source heat pump for combined space heating and domestic hot water power sizing. Submitted for peer review in Building Simulation, August

11 List of Publications 6

12 Author s Contribution Publication I: Kaiser Ahmed was the key person who formulated the main concept and wrote the paper. The author developed the methodology, data processing, data analyses and performed the energy simulation tasks. Petri Pylsy collected the data and provided some observations on the tasks. Jarek Kurnitski supervised the work and commented on the manuscript. Publication II: Kaiser Ahmed was the key person who formulated the main concept and wrote the paper. The author developed the methodology, data processing, data analyses and performed the energy simulation tasks. Petri Pylsy collected the data and provided some observations on the tasks. Jarek Kurnitski supervised the work and commented on the manuscript. Publication III: Kaiser Ahmed developed the article plan, data analyses and wrote part of the manuscript. Andrea Ferrantelli developed the mathematical equations and wrote the manuscript. Petri Pylsy and Jarek Kurnitski commented on the work. Publication IV: Kaiser Ahmed was the key person who formulated the main concept and wrote the paper. The author developed the methodology, data collection, data analyses and performed the energy simulation tasks. Ali Akhondzada did a short part of the literature review on dynamic occupancy models. Jarek Kurnitski supervised the work and commented on the manuscript. Publication V: Kaiser Ahmed was the key person who formulated the main concept and wrote the paper. The author developed the simulation model, plant model, mathematical equations and performed the simulation tasks. Jevgeni Fadejev developed part of the plant model and gave some operational advices. Jarek Kurnitski supervised the work and commented on the manuscript. 7

13 Author s Contribution 8

14 List of Figures Figure 1.1. Dissertation outline Figure 2.1. Required data for simulation tools to generate the output results Figure 3.1. Similar candidates for a 3-person group with a factor of 10 (Publication II) Figure 3.2. Building model and views of front, left, rear and right (Publication V) Figure 4.1. Arithmetic mean, median, 25th and 75th percentile, standard deviation for average DHW usages at apartment level (182 apartments) (Publication I) Figure 4.2. Usage frequency of DHW during WD, WE and Total days (Publication I) Figure 4.3. Arithmetic mean, 25th, 50th and 75th percentile, standard deviation for average DHW usages at occupant level (full population set of 379 occupants a) WD, b) WE, and c) Total days (Publication I) Figure 4.4. Usage frequency of DHW at 8:00, 9:00, 20:00, 21:00 (peak hours) and at 14:00, 15:00 (non-peak hours) during November WD (Publication II) Figure 4.5. Hourly average usage profiles of DHW a) August, b) November, and c) January (Publication II) Figure 4.6. Similar DHW usage candidates of November for a) 31- person, b) 10-person, c) 3-person, and d) 1-person group (Publication II) Figure 4.7. Proposed DHW usage profiles for November a) WD, b) WE, and c) Total days (Publication II) Figure 4.8. DHW usages of occupant groups for November a) WD and b) WE (Publication II) Figure 4.9. Plotted hourly usage factors of DHW as a percentage scale for a more than 50-person profile (a) November (Total) and (b) August (Total) Figure DHW schedule for an apartment building a) November (Total) and b) August (Total)

15 List of Figures Figure Plotted hourly usage factors of DHW as a percentage scale for 3-person profile (a) November (Total) and (b) August (Total) Figure DHW schedule for a house a) November (Total) and b) August (Total) Figure DHW schedule (average hourly usage factor) for the whole year a) more than 50-person profile, apartment building and b) 3-person profile, single-family house Figure Hourly DHW usages of all datasets for November s WD (Publication III) Figure Hourly DHW usages of common occupant groups for November s WD (Publication III) Figure Data (in black dotted) vs. structural curve (in red) vs. unconstrained (in blue) fit for 82 people for November s WD (Publication III) Figure Frequency of DHW usage ratio (for a full population of 379 occupants) (Publication I) Figure a) Average hourly usage profile of Cold (total) water, b) DHW ratio for November WD, WE and Total days [55] Figure An existing DM controlled GSHP operation system with the supply and return water temperature, temperature of outdoor air, and top-up electric heater power (Publication V) Figure Schematic diagram of a domestic GSHP with an alternate operation (Publication V) Figure A 4-occupant single-family house a) DHW supply and indoor temperatures, b) DM value and control signal of GSHP, c) DHW signal, and d) heating signal (Publication V) Figure A 6-occupant single-family house a) DHW supply and indoor temperatures, b) DM value and control signal of GSHP (Publication V) Figure Powers of GSHP at different occupant numbers and outdoor temperatures of a) -26 C and b) -15 C (Publication V) Figure Total heating powers of GSHP a) without internal heat gains, b) with internal heat gains (Publication V) Figure GSHP powers at a) variable usage rate of DHW (L/(person. day)) and b) variable usage peaks of DHW (L/(person. hour)) (Publication V)

16 List of Tables Table 2.1. Daily DHW usages in apartment buildings for different nationalities Table 3.1. Types of apartment in buildings A, B, C, and D (Publication I) Table 3.2. Different apartment types based on actual occupant number from buildings A, B, C, and D (Publication I) Table 3.3. Different apartment types, number, and family sizes (Publication II) Table 3.4. Input parameters for estimating heat emission from occupants bodies (Publication IV) Table 4.1. Monthly usage factors (MF) of arithmetic mean, 25th percentile, 50th percentile, and 75th percentile for WD, WE, Total days (full population of 379 occupants) (Publication I) Table 4.2. Hourly DHW usage factors for the month of November (Publication II) Table 4.3. Hourly DHW usage factors for the month of August (Publication II) Table 4.4. Summary of building types, metabolic rate, body surface area, and total heat losses [21] Table 4.5. Summary of operation hours, usage rate, and average loads for energy estimation (Publication IV)

17 Notations Abbreviations ASHP ASHRAE CAV COP DCV DCW DHW DM EPBD EU GSHP HVAC IDA-ICE MF NZEB SH SHW UK WD WE Air Source Heat Pump American Society of Heating, Refrigerating and Air- Conditioning Engineers Constant Air Volume Coefficient Of Performance Demand Controlled Ventilation Domestic Cold Water Domestic Hot Water Degree Minutes Energy Performance of Buildings Directive European Union Ground Source Heat Pump Heating, Ventilation and Air Conditioning IDA Indoor Climate and Energy Monthly Factors Nearly Zero Energy Building Space Heating Sanitary Hot Water United Kingdom Weekday Weekend Latin letters A Appliance unit load, W/m 2 A Body surface area, m A Heat exchanger s effective surface, m 2 A Area of envelope, m 2 A Appliance usage rate, dimensionless c Heat capacity of storage tank s water, kj/kgk C Constant, dimensionless Reference case constant, dimensionless C 1 12

18 Notations C 2 New constant obtained from new DHW peaks or usages, dimensionless C Heat capacity of water, kj/kgk D Duration of use, s DHW Domestic hot water usage rate, L/(person. day) DHW Average DHW usage rate of the whole year, L/(person. day) DHW Average DHW usage rate of the month i, L/(person. day) DHW Specific hour s hot water usage, L/(person. hour) DM Degree minutes, Cmin DTW Domestic total water (Cold + Hot) usage rate, L/(person. day) DTW Specific hour s total water usages, L/(person. hour) EE Energetic equivalent, Wh/liter of O 2 f Monthly DHW usage factor, dimensionless f, Average hourly usage factor, dimensionless f, Hourly DHW usage factor at hour t for August, dimensionless f, Hourly DHW usages of selected profile at hour t for group, L/person f, Hourly DHW usage factor at hour t for November, dimensionless f, Hourly usage factor as percentage of sum of all hourly factors for a given group, % F Sum of all hourly factors for a given group, dimensionless F DHW monthly usage factor, dimensionless H Body height, m i Month, dimensionless I Intensity of pulse, L/s j All user from 1 to N, dimensionless k All end uses from 1 to M, dimensionless k Usage rate, dimensionless L Lighting unit load, W/m 2 L Lighting usage rate, dimensionless m Storage water mass, kg M Metabolic rate or muscular activity, met Moisture occ Humidity generation from occupant, kg/s n Occupant number in a group, person N Sum of days in a month, day N Occupant number after elimination, person O Heat losses from body, W/person O Number of occupant at a apartment, person O Occupancy usage rate, dimensionless Q Heat loss through sweating, W P Emission of heat, W/m 2 P 1 Reference case s peak demand, L/(person. hour) P 2 Peak demand of DHW for new case, L/(person. hour) q Rate of infiltration, m 3 /s q Air leakage rate from building envelope, m 3 /(h.m 2 ) 13

19 Notations Q Energy use, kwh/m 2 Q Total water demand pattern over a day, (L/s) Q 1 Reference case DHW usages, L/(person. day) Q 2 Usage rate of new case, L/(person. day) Q Vapor Vapor heat loss, W RQ Respiratory quotient, dimensionless R Specific hour s usage ratio, dimensionless S, Scaling factor at m month for given group, dimensionless t Specific hour, hour t Elapsed time, minutes T Actual flow temperature, C T Set-point temperature of DHW flow, C U Heat exchanger s thermal transmittance, W/(m 2 K) v Average hourly DHW usages at m month, L/(person. hour) v Average DHW usages at t hour, L/person v, Average DHW usages at hour t of apartment a, Liter v,, Average DHW usages of individual at hour t from a apartment, L/person v, DHW usages at t hour of selected profile for given group, L/person v,, Hourly DHW usages at t hour of m month of year for group, L/(person. hour) v, DHW usages at hour t for occupant O, Liter V Annual daily average DHW usages, L/(person. day) V Generation rate of carbon dioxide, liter/hour V Oxygen consumption rate, liter/hour V Flow rate at design condition, L/s V, Average hourly DHW usages of given group, L/(person. hour) V Daily DHW usages of group, L/(person. day) V, Total DHW usages by a single-family house, L/day V, Hourly average DHW usage, L/(person. hour) V Daily DHW usages of each occupant, L/(person. day) W Weight, kg x Factor according to the building height, dimensionless Greek letters Φ Φ, Φ, Φ, Φ Φ Φ Φ Total heat losses from building, kw Effective power of water heating, kw Optional internal heat gain power for heated space of i, W Additional heating up power for the heated space of i, W Design heat losses of the heated space of i, W Infiltration heat losses, W Nominal power of heat generator, kw Design space heat load, W Transmission heat losses, W 14

20 Notations Φ, Φ Φ, Φ, Φ, ρ τ τ τ τ,,,,, Total design transmission heat losses from envelope of i, W Ventilation heat losses, W Design heat losses for ventilation of i, W Distribution heat losses, kw Storage tank heat losses at time t, kw Density of water, kg/l Opening time of water tap, s Time constant of storage tank during loading, minute Daily operational hours, hour Weekly operational days, day Temperature of cold water, C Supply temperature of heat generator at time t, C Average water temperature of storage tank at time t, C Average water temperature of storage tank when switched on the reheating, C Temperature of withdrawn water, C 15

21 Notations 16

22 1 Introduction European buildings account for 40% of total final energy consumption, so they are considered as the leading energy usage sector [1]. Residential buildings account for a good percentage of this, leading to the higher emission of greenhouse gases at European Union (EU) level [1]. A reduction in energy use at building level can improve building energy efficiency, helping to reach the 2030 climate and energy framework objectives of 27% energy efficiency improvement and 40% greenhouse emission reduction compared to 1990 [2]. In addition, concerned about high energy use in buildings, all member states of EU agreed to achieve the ambitious targets of the Energy Performance of Buildings Directive (EPBD). These targets state that all new public buildings need to fulfill the requirements for nearly zero energy buildings (NZEB) by Similarly, these targets apply to other new buildings by 2020 [3]. The key sectors of energy use in buildings are space conditioning, domestic hot water (DHW) use, lighting, appliances and ventilation systems. The predictions of energy use at early design stage improve building energy performance, leading to increased energy efficiency and reduced greenhouse emission. These predictions require detailed usage profiles of occupancy, DHW, lighting, and appliances. Mostly, the variations between predicted energy use of buildings at early design stage and onsite energy use are due to inappropriate usage profiles. Moreover, usage profiles depend on local climate conditions, occupants behavior regarding building service use, expected comfort and so on. For instance, DHW heating accounted for 20% of total domestic energy use in the United Kingdom (UK), but only 13% in Germany. Also, the percentage is expected to be higher in Nordic countries because of the shorter summer and severe winter conditions. Therefore, a concrete knowledge of usage profiles can capture the true energy use in buildings, making it possible to predict the actual energy use in buildings and size systems accurately. The main focus of this thesis is to present building usage profiles: DHW monthly and hourly, occupancy, lighting, appliances profiles. Monthly correction factors of DHW usages for different months of the year were developed and further extended to establish the DHW hourly usage profile based on onsite measured data. In addition, the predefined profiles of occupancy, lighting, and appliances based on empirical data can be used in energy simulation tools in order to determine the energy use in buildings and corresponding heat production from occupants, lighting and appliances. This thesis shows how the obtained profiles can be applied to develop heating power 17

23 Introduction sizing equations for ground source heat pump (GSHP) heating, that ensures 100% of heat for DHW and space heating (SH) for single-family houses at a given design outdoor temperature. 1.1 Motivation The EPBD targets presented in Chapter 1 reveal the importance of achieving energy efficiency in buildings. The implementation of building usage profiles at the design stage enables to take sufficient energy-efficient measures. In addition, building usage profiles of DHW, occupancy, lighting, and appliances need to be simple so that they can be easily implemented in energy simulation tools. More accurate building energy efficiency can be achieved onsite if precise building usage profiles are used in energy simulation. For instance, a precise occupancy profile could reduce the gaps between onsite measured energy use in buildings and energy prediction by simulation tools [4]. Few studies have discussed the development of monthly and hourly hot water usage profiles [5-8]. DHW monthly and hourly usage profiles were developed for apartment buildings and developed communities in South Africa in 1996 and 1998 [5, 6]. These studies were based on limited onsite data and assumed the number of occupants in apartment buildings. In a similar context, a new concept of demand model was reported in [8], which required detailed information such as the number of people and their ages, water flow rate and corresponding volume, frequency of water usages, occurrence number and duration, and household end use statistical data. Similarly, the model discussed in [7] aimed to develop the most realistic DHW profile instead of the most representative one. This required detailed information on age, socio-economic factors, hygienic and cooking activities, activity duration and frequency, working pattern, and housing condition. The present approaches and techniques require large input data, which increases the complexity of the energy estimation for DHW heating. Furthermore, occupant number is the key parameter of DHW use in buildings [9]. Therefore, the development of DHW profiles oriented on occupant number may require more attention. Many studies have already discussed the importance of precise usage profiles [4, 10-12]. Occupancy rates are estimated by deterministic and probabilistic approaches [10, 11], as discussed in Chapter 2. Predictability errors, lack of interaction, poor accuracy, and dependency on statistical data were found to be the key limitations in both approaches [10-12]. In a similar context, appliance profiles developed in [13] required statistical data and social random factors. In addition, lighting profiles mainly depend on the presence of occupant, shading, illumination level, building category and so on. Dynamic profiles need a massive amount of input datasets, which are difficult to apply in simulation tools. Therefore, static usage profiles are highly recommended at design phase. Building profiles have a significant impact on energy estimation and system sizing. DHW profiles were used for sizing of solar thermal systems, heat pump systems, and storage tanks [14-17]. Few studies consider the occupant, lighting and appliance profile in heating system sizing. In addition, no studies are 18

24 Introduction available which use both building profiles and occupant based DHW profiles to develop total GSHP heating power equations. Therefore, the effects of building profiles on heating system sizing need to be studied. This thesis aims to develop a detailed monthly DHW profile and an occupant oriented DHW hourly profile. An additional aim is to sort out the occupancy, lighting and appliance profiles that can be easily implemented in common building simulation tools. Moreover, the obtained profiles are used to develop total GSHP heating sizing power equations, to ensure 100% of heat for DHW and SH without any top-up heater for Finnish single-family houses. 1.2 Research questions, objectives and scope The existing literature has contributed a lot to both energy efficiency measures and usage profile application for energy estimation in buildings. However, building usage profiles such as occupancy, lighting and appliances required a large number of datasets, which increased the complexity of applying them in common simulation tools. In addition, building energy performance has improved gradually, however, the contribution of DHW heating energy looks uniform on overall energy use in buildings. Therefore, the thesis aimed to find reasonable answers to the following research questions: 1. What are the patterns and frequency of DHW use at occupant, apartment, and building level? How can these be integrated or implemented in common building simulation tools? 2. How can predefined hourly usage profiles be figured out for occupancy, lighting and appliances, which require limited input data and are easily implemented in building simulation tools? 3. What is the necessity of such profiles? How can these profiles be applied for estimating the sizing power of heating system? In order to answer these questions, the thesis had the following objectives: 1. Develop the monthly DHW usage factor for Finnish residential buildings accounting for the variation between weekdays (WD) and weekends (WE). This requires quantifying the variations in DHW usage at building and apartment level, and usage frequency. 2. Develop the hourly DHW usage profiles for different occupant groups, and, in addition, for different months of the year. Moreover, develop a predictive formula in order to estimate the hourly DHW profiles for different occupant groups without using detailed information on households and social conditions. This formula should be directly applicable in common building simulation tools. 3. Prepare the predefined hourly occupancy, lighting and appliance profiles for different building categories covered by EPBD. In addition, figure out the heat emission from occupants bodies during winter and summer in building categories covered by EPBD. 4. Develop the equations of total GSHP heating power by implementing the usage profiles of occupancy, lighting, appliances, and DHW in common building simulation tools. The calculated heating power should ensure 19

25 Introduction 100% of heat for DHW and SH at design outdoor temperature without any top-up heater for Finnish single-family houses. Our methodology specially focused on data collection from existing literature and building codes, onsite measured data, and simulated results. In order to develop the DHW profiles, onsite measured data was collected. The data was processed and from this, mathematical equations were derived that could explain the hourly DHW usage patterns. The second question was answered by using the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), Finnish, Estonian and other building codes [18-20]. To estimate the heat losses from occupants bodies, the detailed information on activity and ages were collected from [21]. Moreover, a dynamic simulation was performed, considering the building profiles, in order to estimate the total heating power of GSHP. Based on the obtained simulated results, total heating power equations were developed that considered need for DHW and SH, number of occupant, internal heat gains from occupancy, lighting and appliances. The detailed steps to obtain the results are discussed in Chapter 3. The research questions had a broad scope and in order to keep this realistic and achievable, this work left out the following aspects: 1. Due to privacy concerns, the effect of age, gender, and income details on DHW usages were ignored. Daily DHW usage rate and usage profile might vary from one country to another, which was out of scope. 2. The most representative hourly DHW usage profile was developed based mainly on occupant number, individual DHW usage, and water temperature only. The effect of other parameters such as usage frequency, flow intensity, volume flow, occurrence number and duration, or household end uses, were out of scope. 3. Dynamic models of occupancy, lighting and appliances were not considered. Instead, hourly profiles of occupancy, lighting and appliances were based on empirical data collected from different building codes. 4. The profiles were used to estimate the total GSHP power. The detailed design at component level of GSHP, optimum design solution of heating systems, and sizing power of other systems such as solar thermal or district heating were beyond the scope of this thesis. 1.3 Contributions to the scientific field This thesis is prepared based on five publications. The contribution of each publication is briefly discussed here. The detailed discussions of each publication are given in Chapter 3 and Chapter 4. Publication I developed the monthly DHW usage profile for Finnish apartment buildings considering the usage variations during weekdays and weekends. In addition, variations in DHW use at building level during two consecutive years, usage frequency at daily level, DHW ratio and corresponding usage frequency were reported. 20

26 Introduction Publication II developed the hourly DHW usage profiles in a Finnish residential apartment buildings considering the variations in DHW usage during weekdays and weekends. Five different hourly profiles for summer and winter were established based on the group sizes of occupants. In addition, DHW usage frequency, DHW ratio at hourly level, and domestic cold water (DCW) usage profiles were also reported. The proposed method could generate the profiles without any extensive information on end use appliances and consumers. Publication III provided the hourly DHW profiles for an extension group of occupants and detailed analytical modelling of a DHW usage profile, which was validated against the onsite measured datasets. The proposed method could predict the DHW hourly profile for any unknown datasets. The listed formulas could be used in simulation tools to determine the take-off pattern of DHW and sizing of heating systems. Publication IV presented building usage profiles for occupancy, lighting and appliances in different building categories, which can be applied in simulation tools. Heat emissions from occupants bodies corresponding to seasons, activities, and ages were calculated for ten building categories, which showed the contribution of heat emission from occupants bodies. Publication V developed the total GSHP heating power sizing equations at design outdoor temperature, which could ensure 100% of heat for DHW and SH in Finnish single-family houses. The usage profiles available in previous publications were used to develop the equations. The listed formulas forecasted the total heating power for different design temperatures and old buildings, which ensured acceptable thermal conditions and outlet temperatures of DHW. 1.4 Dissertation outline Chapter 2 presents an essential background and literature survey of the topic. Then Chapter 3 discusses the methods used to generate the results. The main findings and results are summarized in Chapter 4. A short discussion of the thesis as a whole and a few possible directions for further research are given in Chapter 5, followed by the journal publications. The dissertation outline is given in Figure

27 Introduction Background study of building usages profiles, influential factors, available technique and tools for obtaining profiles, and profile application (Chapter 2, Publication I - V). Onsite data measurement for monthly and hourly DHW profiles, data processing, data analysis (Sub section 3.1 and 3.2, Publication I - II). Internal heat load estimation and background information for obtaining usages profiles from building codes (Sub section 3.3, Publication IV). Profile application in GSHP system sizing, heat load estimation, simulation building model (Sub section 3.4 and 3.5, Publication V). All findings such as monthly DHW profiles and monthly factors, DHW hourly profiles and factors for different occupants groups, mathematical formulation of DHW profiles for extended number of occupants, DHW usages ratio, internal heat load from occupants bodies, CO2 generation, occupancy profiles, lighting profiles, appliance profiles, GSHP control system, total heating power sizing equations, etc. (Chapter 4, Publication I - V). Short discussion and possible direction for future research work (Chapter 5). Figure 1.1. Dissertation outline. 22

28 2 Background This thesis presents background information on building usage profiles, which have significant impacts on energy estimation. In addition, detailed information concerning the development and application of usage profiles are briefly discussed. This chapter deliberates on the background concerning the overall field of this work. As this work focuses especially on the development of DHW usage profiles for residential buildings and corresponding applications such as power sizing of a heating system, the discussion in this chapter is limited to residential buildings in order to improve the accuracy of energy estimation. As such, detailed approach to occupancy, lighting and appliance usage profiles, or component level discussion of heat pumps, are out of scope. This chapter starts with an overview of factors influencing DHW usage at daily level (Section 2.1), which is further extended to discuss the available techniques and tools for DHW usage estimation in Section 2.2. The overview of building usage profiles is given in Section 2.3. The following Section 2.4 discusses the application of usage profiles, ending with sizing estimation of heating power in cooperation with usage profiles, Section Factors influencing DHW consumption DHW has significant effects on overall energy use in residential buildings. Occupants in residential buildings use DHW for drinking, bathing, washing and cleaning. The daily usage rate and hourly usage variations strongly fluctuate with the number of occupant and behaviors regarding DHW use, individual lifestyle, personal comfort, personal income, age, activity level, ownership, education, cultural and social norms, climate conditions, and demographic location. Occupant number is the key determinant of DHW use [9]. A linear relationship between occupant number and DHW use was observed in [22], which added 45.0 L/day of DHW use for each additional occupant beyond 2 occupants. The estimated DHW use was high if a household was occupied by 3 people or fewer [23]. On the other hand, lower usages of DHW was observed if the household had 4 or more occupants [23]. Similar conclusions were drawn in [5], where 4 times higher DHW usages were reported in low-dense apartment buildings compared to high-dense apartment buildings. 23

29 Background Occupant behaviors and social norms also influence overall DHW use. Canadian seniors buildings used 44% less DHW than standard family apartment buildings [24] whereas apartments occupied by children had a higher reported DHW usage rate [25]. Also, owner occupied apartments used more DHW than rented apartments [26]. Moreover, differences in daily DHW use are also observed for different nationalities. Daily DHW usage rates for Swedish, Estonian, Finnish and Norwegian are shown in Table 2.1 [27]. In some cases the daily DHW use in the USA was 7 times higher than that in certain developed European countries [28]. In addition, seasonal variations have a significant impact on DHW use. DHW use increased by 10% in fall and by 13% in winter compared to the summer [25]. Similar findings are reported in [29], which found a 30% increment of DHW use during the coldest winter period compared to the mildest days in Florida. Also, variations in DHW use of 7.5% were noticed between WD and WE [25]. Table 2.1. Daily DHW usages in apartment buildings for different nationalities. Nationality L/(person. day) Finnish [30] 46.0 Estonian [31] 40.3 Norwegian [32] 40.0 Swedish [33] 33.0 DHW use at hourly level has a significant influence on sizing of a heating system. The variations in DHW use are observed during 24 hours of a WD and WE. A similar usage pattern of DHW with two visible peaks during WD were reported in [34], whereas usage of DHW was more uniform during WE in apartment buildings. Similar findings were found in traditional houses [6]. The peaks in DHW use during WE moved to 1-2 hours later than the WD DHW usage peaks. Moreover, hourly DHW use varies by country. Larger peaks of DHW use were reported during the morning than in the evening in Germany where as the opposite behaviors were found in Finland [35]. However, sometimes it is difficult to explain the unpredictable fluctuations of DHW use in households or apartments. 2.2 Technique and tools used for DHW usages estimation There are many methods such as forecasting method, standard profile method, bottom-up approach, probabilistic approach, etc. are available in literature that can be used for the prediction of usage profile or load profile. Some cases 2-3 methods are summed up to obtain the usage profile. The usage or load profile, obtained from the usage shape during the past, can predict the future usages pattern in forecasting method. This method can predict the usages load for long, medium and short terms ahead. Forecasting method requires the usages information that can be the onsite measured data or obtained from the local statistics. A large number of forecasting methods were reported in [36, 37]. In standard profile method, a typical representative usage profile is used for a typical day. This profiles is obtained from the average of usage profiles (at 24

30 Background typical day) of multiple people [38]. These two methods were used in [6], where one year of onsite measured data from traditional houses were used to estimate the usage pattern of DHW. This study considered 90 traditional houses, which had an electric water heating system in every house. These were evenly distributed between low, medium and high density houses (30 of each, respectively). One water meter was installed for each traditional house, which kept reading the monthly DHW use in volumetric units. The occupant density of each house was also considered. The average DHW use was shown in L/(person. day) as a function of different months of the year. The daily use of DHW in L/(person. day) for any month was obtained by making an average of 30 datasets. In addition, hourly usage profiles were developed based on the onsite measured hourly data from 10 medium-density traditional houses during one year [6]. The developed hourly DHW usage profiles were based on the hourly average of 10 datasets for each hour (one dataset for one house; each dataset contained 24 data readings for 24 hours). Similar approaches were used to obtain the average DHW use in L/(person. day) for each month as well as hourly DHW usages in L/(person. hour) for apartment buildings [5]. However, sample numbers were small and the process of obtaining occupant numbers were not briefly explained [5, 6]. Also, average usages of DHW at hourly level might not portray the actual peaks. Hourly usage profiles that obtained from a group of 62 occupants might not be suitable for medium-density traditional houses where the occupant number was 6.2 per house [6]. The DHW usage profile of a large occupant group can dampen the hourly peaks during the morning and evening. Bottom-up approach is the most frequent method to obtain the usage profile. This approach requires a detailed information of occupant, practices of occupant, available domestic appliance and corresponding technical details, etc. In bottom up approach, every single elements needs to be modelled first, which further combine of together in order to achieve the final objectives [39, 40]. In a similar context, a detailed model for water demand was reported in [8] where multiple input data such as statistical data of end-use, number and ages of people, flow rate and corresponding volume, occurrence number and duration, and frequency of water usage were required for calculating the usage volume at a time interval of one second. The water demand pattern was produced by the end use model that can be described by the following equations [8]: Q = B (I,D,τ ) (2.1) B I,D,τ = I τ <T<τ +D (2.2) 0 elsewhere Where, k, all end uses from 1 to M (dimensionless), j, all user from 1 to N (dimensionless), i, all busy times per end use from 1 to F, which refers as frequency for end uses k and end user j (dimensionless), I, intensity of pulse, (L/s), D, duration of use, (s), τ, opening time of tap, (s), B (I, D, τ), block function, which is equal to the pulse intensity (I) at time difference in between 25

31 Background (τ +D) and τ. Other hours of the day pulse intensity (I) is zero, Q, total water demand pattern over a day, (L/s). At initial phase, the model required information on available end uses (k) and the corresponding penetration rates of end uses, which were collected from [41]. User information such as age, diurnal pattern, usage frequency and duration, as well as household type, were considered as prime input variables, which were collected from Statistics Netherlands [42]. In addition, the usage frequency (F) of each end use was retrieved from the water use survey report, available in [43]. Afterward, the pulse intensity (I) and duration (D) of each end use were collected from survey reports and technical information on appliances [43]. Finally, diurnal pattern and water use time (τ) were retrieved from [41-44]. The obtained data correlated to the individual diurnal pattern used in the simulation model. Afterwards, water usages for multiple days were produced. The model used in [8] required comprehensive statistical information on household end uses, diurnal pattern, and occupant detail. A similar approach was used in [13] to develop an hourly usage profile of electricity. In addition with bottom up approach, the model used the probabilistic approach that gave the probability of occurrence of each activity from a set of usage profiles. At the first stage, the appliance stock of each household and fluctuation in diurnal usage level were defined. Information regarding the appliances used in the household were extracted from the statistical data. The fluctuation in diurnal usage level was obtained by estimating the social factors. At the following stage, simulations were performed in order to obtain the usage profile of electricity for each appliance or group of appliances for every household based on the usage cycle of appliance. The appliance s usage cycle took into account seasonal and hourly probability, active plus standby power usage, occurrence frequency, and social probability factors, which were extracted from the public reports, statistics, and appliance specifications. The proposed model was more dependent on statistical data and public reports [13]. Modelling of user behavior regarding energy use in a building seems complex. The daily activities of 7200 households during 3 days were collected at a resolution of 10 minutes for behavioral modelling [7]. The data contained the information of occupant age, working pattern and housing condition. The daily activities of the occupants alongside socio-economic factors were used to develop the foundation of the behavioral model. In addition, hygienic and cooking activities were used to develop the DHW usage model. The (hygienic or cooking) activity start time, duration, and daily frequency were used to generate 10 synthesis DHW usage profiles at a time interval of 15 minutes. The model aimed to develop the most realistic DHW profile rather than the most representative one. 2.3 Building usage profiles Buildings use nearly 40% of the total final energy in Europe; two thirds of this is consumed by residential buildings [1]. Residential buildings use energy for space conditioning, DHW, lighting, and appliances. In order to reduce energy 26

32 Background consumption and achieve energy efficiency targets at building level, EPBD announced a road map [3]. This requires a detailed investigation of energy use in buildings, especially to determine building usage profiles. These usage profiles can be applied in building energy simulation tools, which predict the energy performance of a building in the design phase. Energy usages in a building mainly depend on the presence schedule of its occupants and corresponding usage behavior regarding the building services. The energy data from schools and daycare centers were analyzed in [45]. Presence schedule of occupant was found to be the key parameter of energy use in buildings. In a similar context, a reduced ventilation rate of 1.0 L/(s m 2 ) instead of 2.0 L/(s m 2 ) was proposed in low-occupancy and low-polluting industrial buildings, which gave evident energy savings [46, 47]. User behavior regarding the building services has a significant impact on energy use for heating, ventilation, and air-conditioning (HVAC) systems, lighting, appliances and DHW. Energy use for space conditioning, lighting and appliances showed marginal savings due to the user comfort level [48]. Energy use for lighting, appliances and DHW depends on the presence of occupant in the space. A survey was conducted in four office spaces where one room was used by administrative employees and rest of the rooms were used as working space for postgraduate students [49]. Lighting energy increased by 50% in the rooms occupied by postgraduate students due to the long hours of occupancy. Similarly, user presence and behavior increased the use of lighting energy by 5-15% [50]. Moreover, energy use for plug loads follows the presence schedule of occupant. The correlation between occupant s presence in an office building and energy use for plug loads was developed in [51]. The total energy use for lighting and appliances can be estimated corresponding to the Finnish building code with Equation (2.3) [52]. Furthermore, energy use for DHW seems different in residential and commercial buildings due to the different usage behaviors and presence schedules of occupants. The energy use for DHW heating showed an evident variation during different months of the year [53]. In addition, hourly DHW consumption variation was reported in [54]. Q=k P τ τ τ (2.3) where, Q, energy use ( ), k., usage rate (dimensionless), P, emission of heat ( ), τ., weekly operational days (day), τ, daily operational hours (hour), τ, total estimating period (h) (a year equivalent to 8760 hours). The importance of usage profiles and corresponding correlation to the energy use in buildings were well discussed in [27, 55-57], as shown in Figure 2.1. Poor understanding of actual effect may cause higher variations between expected results at design stage and actual onsite performance. The presence schedule of occupants was found to be the key factor, which made evident differences between results obtained from onsite measurement and simulation [4]. As of today, many studies have proposed the occupancy prediction model. Deterministic and probabilistic approaches were the most common in the body of literature; these can predict the presence schedule of occupant and 27

33 Background corresponding usage behavior regarding energy use in buildings [58, 59]. Multiple drivers are linked and can generate one or more outputs in a deterministic approach, whereas a probabilistic approach predicts the probability of presence or occurrence of an event based on a statistical algorithm. A deterministic approach was proposed in [11], which recounted the detailed activity information for 6400 people from 3474 households. Seven occupancy schedules were developed using hierarchical clustering, according to three states: absent, sleeping, and at home and awake. Those occupancy schedules could distinguish common behavior and were suitable for application in simulation tools [11]. Similarly, variations of occupant number in UK households during WD and WE were analyzed for a time resolution of 10 minutes [10]. The proposed method estimated the number of active occupants for a defined time frame [10]. However, lack of predictability and interaction were found to be the key limitations of the deterministic approach [10, 11]. Figure 2.1. Required data for simulation tools to generate the output results. 28

34 Background In contrast, the statistical details of occupancy in a single office room were analyzed, also considering the intermediate time between presence and absence [12]. This probabilistic approach explained the presence sequences as well as occupant behaviors in a single office room. However, it failed to explain if the place was unoccupied for a longer duration [12]. Similarly, a mathematical model was developed based on the statistical data collected from 200 cubicle offices [4]. This model could explain the number of absences, absence durations and presence patterns of occupant in an office room occupied by 1 person. However, it could not explain the presence pattern of occupants in an open office where a good number of people were working together [4]. The presence profiles of occupant on premises such as schools, universities, or shopping malls are very complex due to different activities involved. Dynamic occupancy profiles require a large number of input datasets, which seem difficult to apply in the common building energy simulation tools. Similarly, an extensive amount of data is required to consider the dynamics of lighting and appliance profiles, as shown in Figure 2.1. Therefore, this work did not consider the dynamic model of occupancy, lighting and appliances but collected the data from existing literature and national building codes [18, 20, 60]. Based on this collected data, hourly occupancy profiles, lighting profiles, and appliance profiles for different building categories were prepared (Publication IV), which could be used in simulation tools. Heat load estimation from occupants bodies for different building categories can ensure greater accuracy in predicting the need for space conditioning energy. 2.4 Profile application Building usage profiles have a significant impact on energy use in buildings. An actual occupancy profile ensures that the energy use and system sizing can be estimated with greater accuracy. Also, it minimizes the difference between onsite measured and simulated data [4]. The indoor climate conditions and energy performance of a low-energy office building are presented in [61]. In order to compare the effect of Constant Air Volume (CAV) and Demand Controlled Ventilation (DCV) systems on overall primary energy use, the average occupancy rate in an open-plan office was considered to be With this occupancy rate, a DCV system saved 7-8% of primary energy compared to a CAV system. In the similar context, the required ventilation rate for two industrial buildings were discussed in [47]. The study suggested reducing the ventilation rate by 50% of design value due to the lower occupancy rate and presence of low-polluting materials in the buildings, which reduced the energy use. Energy use for lighting was influenced by occupant behaviors and outdoor illumination level [62]. Lighting profiles at different time scales throughout the day were used to develop a model that estimated the energy use for lighting [62]. Similarly, the hourly schedule of lighting and appliances for an Italian residential building and office building were used to estimate the yearly electricity consumption and related internal heat gains [63]. Hourly profiles of 29

35 Background refrigerators, dishwashers, clothes washers and dryers during WD and WE were developed for residential buildings in [64]. The contribution of each appliance to the home electricity consumption was shown and further combined to develop the representative profile for appliance. The appliance profiles were used in building energy simulation to represent energy consumption behaviors 24 hours a day [64]. DHW usage profile needs to be considered during the sizing of heating systems. The draw-off timing of DHW and corresponding volume were studied during the sizing of a solar thermal system [14]. The draw-off timing of DHW was found to be critical for sizing of a solar heating system [14]. Similarly, number of occupant and hourly DHW profile were used to provide DHW for an apartment building by using a GSHP with multiple storage tanks [15]. In addition, a dynamic profile was used to size a DHW storage system for 202 Spanish inhabitants [17]. Moreover, a realistic DHW profile instead of uniform DHW could prevent poor heating system sizing [16]. 2.5 Sizing of heat pump power Power sizing of a heat pump depends on the demand for DHW and SH. The predefined percentage of total heating load covered by heat pump also has a significant impact. The SH need guidelines are given in the Finnish building code [65] and European standard [66]. Heat losses from buildings, bodies, and ventilation facilities are estimated using the following equations [65]: Φ =Φ + Φ +Φ (2.4) q, = q A (2.5) 3600 x Where, Φ, design space heat load (W), Φ, transmission heat losses (W), Φ, infiltration heat losses (W), Φ, ventilation heat losses (W), q, envelope s air leakage rate (m 3 /(h.m 2 )), A, area of envelope (m 2 ), q, rate of infiltration (m 3 /s), x, building s height based factor. The factors are 15, 20, 24, and 35 for 5 or more storied, 3-4 storied, double and single story building, respectively [65]. European standards take a similar approach to compute the heat load for a heated space, as shown in Equation (2.6) [66]. Φ, =Φ, + Φ, +Φ, Φ, (2.6) Where, Φ,, design heat losses of the heated space of i (W), Φ,, total design transmission heat losses from envelope of i (W), Φ,, design heat losses for ventilation of i (W), Φ,, additional heating up power for the heated space of i (W), Φ,, optional internal heat gain (W). The Finnish building code calculates the heating need for DHW at static conditions [60] whereas the European standard (FprEN ) considers the dynamic behavior of the storage tank, DHW loading factor, tank, and circulation losses [67], which give more precise results. According to the FprEN :2016 standard [67], the energy need curve (demand) is estimated by measuring the volume flow rate based on DHW load profile, temperatures of 30

36 Background hot and cold water. In addition, the energy supply curve (supply) is estimated by accounting for the available sources of heat, losses due to the distribution system, thermal losses from storage, system efficiency, and corresponding power [67]. The following equations are used to estimate the energy need for DHW heating [67]: Φ =Φ 1, (t), (t) Φ, Φ, (2.7) Φ =ρ C V (, ) (2.8), (t) =,, +,,, (1 e ) (2.9) τ = m c U A (2.10) Where, Φ, water heating effective power (kw), Φ, heat generator nominal power (kw),, (t), storage tank s average water temperature at time t ( C),, temperature of cold water ( C),, (t), heat generator s supply temperature at time t ( C), Φ,, storage tank s heat losses at time t (kw), Φ,, distribution heat losses (kw), ρ, density of water (kg/l), C, heat capacity of water (kj/kgk), V, design flow rate (L/s),,, temperature of withdrawn water ( C),,,, storage tank s average water temperature when switched on the reheating ( C), τ, storage tank s time constant during loading (min), t, time (min), m, storage water mass (kg), c, heat capacity of storage tank s water (kj/kgk), U, heat exchanger s thermal transmittance (W/(m 2 K)), A, heat exchanger s effective surface (m 2 ). Furthermore, the Finnish building code estimates the total heat pump power as alternately heating domestic water and space [60]. In contrast, the EN standard estimates the total heat pump power as simply the sum of the heating need for domestic water and space [68]. However, heat pump sizing power depends on the heat pump types: monovalent, mono-energetic, or bivalent. A monovalent heat pump ensures 1oo% of heat for space heating and DHW heating. Such a type of heating system does not require any additional top-up heater. The sizing needs to be more precise; then it can ensure 100% of heat for DHW and SH in extreme scenarios, too. Sizing needs to consider the influential factors such as design outdoor temperature and DHW hourly profile very precisely because of the high investment cost of each additional power unit. In addition, occupancy, lighting and appliance profiles can be used during heat pump power sizing, which reduces the SH need and overall sizing power. Many power sizing solutions for heat pump are available in the literature, which mainly discusses the percentage of heating load that could be covered by the heat pump. Some sizing solutions are obtained only for either SH or DHW heating need. A ground source heat pump with a multiple storage tank system was used for DHW heating in an apartment building [15]. Occupant number and hourly DHW profile were used to estimate the volume flow rate of DHW. However, the number of occupants per apartment was assumed and DHW usage profile was not defined in sufficient detail. The hourly DHW usage profile 31

37 Background used was for large occupants group of 75 where DHW peaks might dampen. This hourly DHW usage profile cannot be used for DHW heating estimation in a single-family house due to the limited number of occupants. Sharper DHW usage peaks during morning and evening are expected for a smaller occupants group. In addition, the GSHP power estimation was accomplished only for DHW heating [15]. Similarly, heat pump optimal power was estimated only for sanitary hot water (SHW) use [69]. The proposed optimal solution was based on system operation hours and lifespan, investment cost, and coefficient of performance (COP) [69]. However, the heat pump sizing power did not account for the SH need. In another context, optimal power for air source heat pump (ASHP) in cooperation with an electric back-up heater, thermal storage, electric boiler and photovoltaic panel for residential buildings were discussed in [70]. The optimal power was estimated based on operational strategy and investment cost of the heat pump system. This mono-energetic heat pump covered a good percentage of DHW and SH demand and remaining heating need during peak hours was covered by the top-up heater [70]. In another context, a comparison was made between on-off and modulating air to water heat pump in terms of delivered heating [71]. This on-off heat pump delivered 95-98% of the annual heat, which also required an additional heating system to meet 100% of heating need. The sizing of a monovalent heat pump system that could ensure 100% of heat for DHW and SH has not been figured out in literature. Also, no direct power sizing equations for GSHP are available that consider design temperature, a precise hourly profile of DHW and internal heat gains from occupants, lighting, and appliances. 32

38 3 Methods The research findings were established in the following steps: 1. Developing the monthly usage factors of DHW for Finnish apartment buildings, which was constructed from 379 occupants measured daily DHW usages for two consecutive years. 2. Developing the hourly usage profiles of DHW for 1-person, 3-person, 10- person, 31-person, and more than 50-person groups, further extended by introducing the prediction formulas for analytical DHW usage modelling. 3. Investigating the importance of building usage profiles in energy estimation. The background information on occupancy, appliance, and lighting profiles for building types covered by EPBD and the estimation of internal heat losses from occupants bodies were fully elaborated. 4. Describing the application of profiles in order to calculate the total GSHP s power in Finnish single-family houses that ensured 100% of heat for DHW and SH without any top-up heater. The study results were generated from onsite measured data of DHW usages, available empirical occupancy, lighting, and appliance data from building codes and simulated data. The method chapter begins with the development of DHW usage profiles, which further explains the occupancy, lighting, and appliance profiles for different building categories and estimated heat loss from occupants bodies. The Sections 3.1 and 3.2 discuss the detailed information on data collection and data processing of DHW usages. The following Section 3.3 shows the background of other profiles. The Sections 3.4 and 3.5 discuss the application of profiles. A simulated building model is used to estimate the total heating need of a single-family house and develop the power equations of heating need. 3.1 Onsite data measurements Two sets of data were used: daily and hourly usages of DHW. The daily usage data was collected from 182 apartments occupied by 379 people and used to develop the monthly DHW profiles (Publication I). In addition, hourly DHW profiles were developed based on hourly DHW usage data, collected from 86 apartments occupied by 191 people (Publication II). The central district heating system provided the heating energy for those rented apartments. In addition, 33

39 Methods each apartment was equipped with its own washing machine, dishwasher, shower, kitchen sink, and bathroom basin facilities. Hot water was used for shower, kitchen sink, and bathroom basin facilities whereas cold water was used in dishwasher and washing machine operation. Afterward, information from register papers was used to determine the actual number of people in every apartment (Publication I, II). However, age, gender, and annual income were not considered out of concern for individual privacy. Very large percentages of the Finnish population are located in climate zones 1 and 2, including the largest cities, Helsinki, Turku and Tampere. The annual average outdoor temperatures are 5.3 and 4.6 C for zone 1 and 2, respectively. Therefore, this study assumed that the data well represented the majority of the Finnish population and described the whole country with reasonable accuracy. However, different conditions are expected to apply in northern Finland, due to the small population and severe winter. Moreover, supply temperature of DHW was not measured on site for this study. But the maintenance person ensured that the supply water temperature was regulated according to the Finnish building code requirement of 55 C [30] Description of measurement data for a monthly DHW profile The detailed usages of DHW and DCW were collected from 182 apartments with 379 occupants in four different buildings. Around 24% of these occupants were under the age of 18. Occupant number has significant impact on daily consumption and the exact number of occupant in every apartment were considered. Apartment types and social structures of the tenants are reported in Table 3.1 and Table 3.2 (Publication I). Table 3.1. Types of apartment in buildings A, B, C, and D (Publication I). Apartment type Building A Building B Building C Building D One room Two rooms Three rooms Four rooms Five rooms Number of apartments Table 3.2. Different apartment types based on actual occupant number from buildings A, B, C, and D (Publication I). Occupancy in apartment Building A Building B Building C Building D One person Two people Three people Four people Five people Six people Total

40 Methods Every apartment had an individual smart water meter, which was used for billing. This system measured DHW and DCW usages separately with an accuracy of ± 2 %. At 24 hour intervals, the measurement data were stored in the database per 100 liters. The measuring system showed the following information in liters per apartment: Cumulative amount of DHW usages in liters; Cumulative amount of DCW usages in liters; Daily usages of DHW and DCW in liters. The onsite measured data were available from 1 January 2012 to 30 June 2014 (Publication I). The data from 1 January 2012 to 30 April 2013 showed the DHW usages of each apartment, which were based on monthly, quarterly or fixed duration. The daily onsite data were measured from 1 May 2013 to 30 June The monthly datasets were used to compare DHW usages for two consecutive years, further extended to compare the arithmetic mean of daily DHW usages between these two years. The unit considered was L/(person. day). Furthermore, the data from apartments that were vacant for two consecutive months was ignored Description of measurement data for an hourly DHW profile The hourly usage profiles of DHW were developed based on hourly usage data of DHW and DCW from 86 Finnish apartments, which belonged to one company (Publication II). Those apartments were owner-occupied and belonged to one building situated in Helsinki. Apartment details are listed in Table 3.3. Table 3.3. Different apartment types, number, and family sizes (Publication II). Apartment type No Occupancy in apartment No One room 2 One person 24 Two room 20 Two people 31 Three room 27 Three people 15 Four room 30 Four people 15 Five room 7 Five people 1 The data were collected with a 1-hour interval period from 17 May 2014 to 5 February Each apartment had its own water meter, enabling to store separate reading of DHW and DCW with an accuracy of ± 2 %. The database stored the usage readings with a resolution of 1.0 liter. The measuring system showed the following information (Publication II). Cumulative amount of DHW hourly usages in liters; Cumulative amount of DCW hourly usages in liters. The hourly DHW usage pattern was found to be similar to a different usage volume [54]. This study considered the DHW usage pattern of November as representative for all winter months, however, it would have been possible to 35

41 Methods choose the usage pattern from October, January, or February. Similarly, the usage pattern for August was considered as the representative summer month. Moreover, June and July are commonly summer vacation months in Finland, which may not portray a reliable usage profile (Publication II). This study did not consider the data for empty apartments, for instance, no DHW usage during a month or a person used less than 20 L/day of DHW. In addition, it ignored the data from a 5-occupant apartment due to the limited sample size. Furthermore, for both datasets occupant numbers higher than two were assumed to represent children who would have equal DHW usage volume to one adult. 3.2 Data analysis In the first phase of data analysis, monthly profiles of DHW were developed from the daily data reading from water meters and actual number of occupants in each apartment (Publication I). The obtained data was split into two datasets, June 2012 to May 2013 and June 2013 to May 2014, in order to compare the DHW usages between two consecutive years. In addition, the arithmetic mean, standard deviation, 25th, 50th, and 75th percentile at a monthly level were also estimated from daily-basis datasets. The registered occupant number and the total DHW usages of each apartment were used to obtain an average specific usage in L/(person. day). The usages of each occupant from the same apartment were assumed to be equal. For instance, if the daily DHW usage of an apartment was 200 L/day for 4 occupants, the daily DHW usage of each occupant was 50 L/(person. day). Moreover, a comparison of the DHW usage variations at building level, at apartment level, and over two consecutive years were discussed in Publication I. The second daily-basis dataset (June 2013 May 2014) was used to obtain WD, WE and total week DHW monthly profiles (Publication I). The arithmetic mean values were estimated from the daily DHW usages of 379 occupants (full population sets) of 182 apartments (all buildings) in L/(person. day). This resulted in daily average DHW usage for the whole year. Similarly, DHW usage rate in L/(person. day) of individual months, WD, WE were discussed in Publication I. Monthly factors (MF) for DHW usages were obtained by using the following equation (Publication I). The MF for WD is defined as the ratio of average DHW usage rate during WD of the month i to the average DHW usage rate of the whole year (Publication II). MF = DHW (3.1) DHW Where, i, month (dimensionless), DHW, average DHW usages rate of the month i (L/(person. day)), DHW, average DHW usages rate of the whole year (L/(person. day)). Furthermore, DHW ratio was estimated using the following equation (Publication II): 36

42 Methods DHWR = DHW DTW (3.2) Where, DHW, domestic hot water usages rate (L/(person. day)), DTW, domestic total water (Cold + Hot) usages rate (L/(person. day)). In the second phase of datasets, hourly DHW usage profiles were developed from the hourly DHW usage data of 191 occupants from 86 apartments (Publication II). The hourly DHW usage data for November, August, and January were separated from the total measurement period of 17 May 2014 to 5 February Cumulative DHW and DCW hourly usage readings of all apartments were prepared for November, which gave the same number of datasets as the total number of apartments (86 datasets). Dataset (for each apartment) contained detailed information on DHW usage during 24 hours for 30 consecutive days (a month). From this single apartment dataset, the hourly DHW usage profile for an apartment was obtained, and extended to the occupant level by using the following equations (Publication II): v, = v,, (3.3) N v,, = v, O (3.4) Where, t, specific hour (h), n, days of a month (day), a, specific apartment, v,, average DHW usages at hour t of apartment a (Liter), N, sum of days in a month, v,,, average DHW usages of individual at hour t from a apartment (L/person), O, number of occupant at a apartment. This study considered the DHW usage data from November 2014, where the total number of days, weekdays and weekend days were 30, 20, and 10, respectively. The hourly DHW usages of each occupant were obtained at apartment level during a day. A similar procedure was applied for all 82 apartments, occupied by 191 people, which generated 191 hourly DHW usage profiles. Moreover, hourly usage profiles were overlooked if an occupant s daily average DHW usage was less than 20 L/day. Therefore, the numbers of occupant profiles for November WD, November WE and November total were 164, 163 and 164 respectively (Publication II). Each hour average (1.0 hour to 24 hours) from 164 profiles represented the hourly DHW usage profile for November as shown in Equation (3.5) (Publication II). v = v, (3.5) N Where, v, average DHW usages at t hour (L/person), v,, occupant O DHW usages at hour t (Liter), N, number of occupant after elimination. The obtained profiles ignored the DHW usage peaks due to the large dataset or occupant number. However, this could give a general idea of DHW usage pattern. This study also assessed the frequency of DHW usages during a day. Furthermore, occupant number has substantial effects on DHW usage and it was used to develop the DHW usage profiles, as discussed in Publication II. Five different profiles were developed for 5 occupant groups (1 person, 3 people, 10 people, 31 people, 164 people), which explained the hourly DHW usage 37

43 Methods variations from individual to group. The selection of one representative profile (for each group) out of 164 was hard and based on the following circumstances (Publication II). Firstly, the selected profiles used a similar daily DHW (L/(person. day)) to the average DHW usage (L/(person. day)) of 164 profiles; Secondly, the hourly usage pattern of the selected profiles followed a similar trend to that of the average profile of 164 occupants; Thirdly, it was expected that smaller groups had a larger DHW usage in peak hours compared to that of large groups; Finally, selected profiles were the most representative profiles of the respective groups. A large number of datasets were considered to develop the DHW hourly usage profile (Publication II). For instance, this study considered 164 hourly profiles although the detailed information on single-occupant apartments was available (Table 3.3). Fifteen profiles with average daily DHW usages similar to the average daily DHW usage of 164 profiles were chosen. Four suitable candidates among 15 profiles were chosen dependent on the closeness of DHW usage pattern to this average one. Any of those 4 candidates could be used as the representative hourly DHW usage profile for a 1-person group (Publication II). For the 3-person DHW usage profile, the Excel RAND function was used in order to randomly select 3 profiles from 164 and established an hourly DHW usage profile for a 3-person group. These gave a total combination of 54 and each one had a particular hourly DHW usage profile, the average of DHW usages at hourly level. The procedure was repeated at least 3 times, which provided the total combination of 162, and each one had particular hourly DHW usage profile. Of 162 profiles, 15 profiles with daily average DHW usages similar to the daily average of DHW usages of 162 profiles were chosen. Four good profiles were selected from these 15 candidates, which could be used as the representative hourly DHW usage profile for a group of 3 people (Publication II). A similar procedure was repeated to develop the hourly DHW usage profile for groups of 10 and 31 people. These gave a total combination of 48 and 15 corresponding to the group of 10 and 31 people, respectively. However, it seemed challenging to choose the most representative one due to the availability of many similar candidates in some cases. DHW usages at peak hours have a significant impact on the sizing of heating system. Considering the importance of peak hour usages, it were multiplied (at 7:00 9:00 and 20:00 22:00) with weighting factors. Three weighting factors of 3, 5 and 10 were used. For instance, peak hours and non-peak hours of DHW usage profiles were multiplied by factor 3 and 1, respectively, and these were applicable to 164 profiles. Afterward, a similar selection procedure was repeated for the groups of 10 and 31 people, to find the most representative one (Publication II). This approach resulted in more visible peaks. In some cases 3- person and 10-person profiles resulted in nearly similar DHW usages to the average one (average hourly DHW usage profile of 164 occupants), however, the hourly DHW usage patterns were not nearly similar. Similar candidates for a 3- person group with a factor of 10 are shown in Figure

44 Methods Figure 3.1. Similar candidates for a 3-person group with a factor of 10 (Publication II). Again, the Excel RAND function was used to randomly select 3 profiles from 164 and established an hourly DHW usage profile for a 3-person group, which gave 54 combinations. This procedure was repeated 3 times, which gave 162 combinations, and each combination had its own profile. The hourly average of 3 profiles generated the hourly profile of each combination. Among 162 profiles (each combination had own profile), 10 profiles closest to the new average one (new average of 164 profiles with factor 3) were chosen. Profiles were selected using the usage ratio (Δx, the ratio of peak DHW usages between evening and morning). Of those 10 profiles, the best fitted profile was selected, which followed a similar DHW usage pattern to the new average one. The same procedure was followed to find some good candidates with a factor of 5 and 10 for groups of 3 and 10 people (Publication II). The peak hours of good candidates were divided by assigned factors. At least 4 usage profiles were selected for every group, either considering the factors or not. Of these, one candidate for each group was chosen by assuming that the large group had a low DHW usage at peak hours compared to the small group. The nominated candidate of each group could represent the hourly usage profile for the respective group. Moreover, introducing the weighting factors of 3, 5, and 10 could result in selecting the same candidate as without weighting, which could provide an additional assurance in the cases of many similar profiles. The same procedure was used to generate the hourly usage profile for November WD and WE. The selected profiles for the respective groups also generated the profiles for different months while accounting for the monthly DHW usage factor. Moreover, it was assumed that the DHW usage pattern during the winter months of October, January and February followed the DHW usage pattern for November. The following equation were used to generate the hourly DHW usages for different months (Publication II): V, = V f 24 V, = V 24 (3.6) (3.7) S, = V, V, (3.8) 39

45 Methods f, =S, v, (3.9) Where, V,, hourly average DHW usages (L/(person. hour)), V, annual daily average DHW usages (L/(person. day)), f, monthly DHW usages factor (dimensionless), V, daily DHW usages of group, (L/(person. day)), V,, average hourly DHW usages of group (L/(person. hour)), S,, scaling factor at month m for group (dimensionless), v,, DHW usages of selected profile at hour t for group (L/person), f,, hourly DHW usages factor of selected profile at hour t for group (L/person). The same procedure was followed to develop the hourly DHW usages of January and August (Publication II). A DCW usage profile for November was also developed in Publication II, which was used to determine the usage ratio of DHW to total water, as shown in Equation (3.10). R = DHW DTW (3.10) Where, R, specific hour s usages ratio (dimensionless), DHW, specific hour s hot water usages (L/(person. hour)), DTW, specific hour s total water usages (L/(person. hour)). Furthermore, hourly DHW usage profiles for 20, 41, 54, and 82 people were developed, providing an extended insight into occupant and hourly DHW usages as well as increasing the correctness of the analytical model, as discussed in Publication III. 3.3 Heat load estimation and background of usage profiles This section introduces the methodology of heat load estimation from occupants bodies as well as discussing the generation of CO 2 and humidity. In addition, the detailed background information on occupancy profiles, lighting profiles, and appliance profiles for different building categories are thoroughly discussed Internal heat load Body surface area is considered as the most significant parameter for calculating the dry and total heat losses from occupants bodies. Body surface area may be responsible for different heat losses despite equal muscular activities. The Du Bois formula was used to estimate the body surface area [72]. A = W. H. (3.11) Where, A, body surface area (m ), W, weight (kg), H, body height (m). Heat losses from occupants are released in the indoor environment by convection, radiation, and skin diffusion. In addition, ambient and mean radiant temperature, air humidity and velocity, muscular activity, and insulation from wearing clothes have significant effects. The set-points of different parameters, considering the seasonal variations, are reported in Table

46 Methods Table 3.4. Input parameters for estimating heat emission from occupants bodies (Publication IV). Range [73] Summer Winter Temperature, C Mean radiant temperature, C Operative temperature, C Relative humidity, % Clothing insulation, clo Air velocity, m/s Metabolic rates or muscular activity associated with activities for different occupant groups in a building were calculated in [21]. Body muscular activity is estimated in Met units. The high heat production from the body due to greater physical activity must be drained out in order to reach thermal equilibrium. The body loses heat through convection, radiation, vapor, and sweat. Total heat loss constitutes all these losses whereas dry heat loss consists of convection and radiation loss. The detailed equations for heat loss estimation were discussed in [56], which were derived from the general format of [73, 74]. Internal heat load is also associated with solar heat, appliances, and lighting. Due to the dynamic behaviors of solar heat gains and substantial dependency on different factors such as climate, geometrical dimensions of the building, openings and envelope properties, this study overlooked the effects of solar heat gains. Moreover, heat loads from appliances and lighting for a given period were estimated according to Equation (2.3) [52] Background information on profiles The ASHRAE, Finnish, Estonian and some other building codes were used to get the idea of usage factor and operation hours of building [18, 31, 52]. In addition, information regarding room temperature set-points, ventilation rate, and relative humidity were extracted from the pren standard [75]. Likewise, CO 2 and humidity generation in an indoor environment were calculated according to [76]. RQ = (3.12) EE = {(0.23 RQ ) 5.88} (3.13) M =EE V (3.14) Humidity = Q + Q (3.15) Where, RQ, respiratory quotient (dimensionless), EE, energetic equivalent ( ), V., oxygen consumption rate ( ), V, generation rate of carbon dioxide ( ), A, body surface area (m ), M, metabolic rate or muscular activity (met), Moisture, humidity generation from occupant ( ), Q, vapor heat losses (W), Q, heat losses through sweating (W). 41

47 Methods 3.4 Implementation of usage profiles for heating system sizing Monthly and hourly usage profiles of DHW, occupancy profile, appliance and lighting profiles were used for sizing of a heating system in a Finnish singlefamily house. Among many heating solutions, GSHP seemed the most convenient one for single-family houses. The sized power of GSHP covered 100% of heat for DHW and SH at a given design outdoor temperature without any top-up heater. Parameters corresponding to the building SH need such as heat transfer coefficient (U value), air change rate, thermal bridge, and ventilation rate were estimated according to the Finnish building code [65]. Heat losses from building, bodies, and ventilation facilities were estimated according to [65, 66]. The occupant number in a Finnish single-family house varied from 3 to 6. The average daily DHW usages (L/(person. day)) were multiplied by the occupant number and DHW monthly usage factors, Equation (3.16) (Publication V). The obtained total DHW (L/person) in a Finnish single-family house was distributed according to the hourly DHW usage profile of 3 people. V, =n F V (3.16) Where, V,, total DHW usages by a single-family house (L/day), n, number of occupant (-), F, DHW monthly usages factor (dimensionless), V, daily DHW usages of each occupant (L/(person. day)). FprEN :2016 was used to estimate the required heat for DHW heating [67] where the loading factor of DHW, losses of the storage system, and dynamic behaviors are well defined. Afterward, sizing guidelines followed the European standard FprEN [68]. A monovalent GSHP with a reciprocating compressor, providing sufficient heat for space and DHW heating, was considered. The temperature difference between evaporator and brine was found to be 8 C. In addition, the same temperature difference was used between the water and condenser. The minimum evaporation and maximum condensation temperature were -50 and 70 C, respectively. Moreover, an existing control system was implemented in a plant model in order to find the heat deficit for DHW and SH. The control system estimated the heat deficit in degree minutes (DM) according to the heating curve and the set-point temperature of delivered DHW, as discussed in Publication V. The DM value is a cumulative number of the difference between actual flow temperature (T ) and the flow temperature set-point (T ) for a given elapsed time in minutes (t ), Equation (3.17) (Publication V). The DM value starts to count if T T. If this cumulative number was less than the given number (default number), then the compressor was in ON status and GSHP had started producing heat. (3.17) DM = ( T T ) t Where, DM, degree minutes ( Cmin), T, set-point temperature of DHW flow ( C), T, actual flow temperature ( C), t, elapsed time (minutes). 42

48 Methods 3.5 Simulation example A well validated IDA Indoor Climate and Energy (IDA-ICE, version 4.7.1) simulation tool was used, developed by EQUA Simulations AB [77]. The reliability, performance, and accuracy of this simulation tool have been well validated in [15, 78-81]. The reference single-family house, shown in Figure 3.2, is typical for Finland. The building has a heated net floor area of m 2. The building followed the two building regulations, namely the old building regulation of 1976 and modern low-energy (passive) building regulation. To account for the dynamic behaviors of building model and SH need, IDA-ICE with a maximum time interval of one hour was chosen. The design outdoor temperature and indoor heating set-points were -26 C and 21 C, respectively, following the Finnish building code [60]. A constant design outdoor temperature of -26 C and no solar heat gains were considered for computational analysis. The detailed building information is given in Publication V. Figure 3.2. Building model and views of front, left, rear and right (Publication V). In the building model according to the modern low-energy (passive) building regulation the heat recovery unit and ventilation system were well balanced. The heat exchanger and heating coil heated the supply air to the set-point. The heated space was equipped with floor heating where supply and return water temperature were 35 and 28 C, respectively. In contrast, only an exhaust ventilation system was considered in the building model following the old building regulation of The heated space was equipped with radiator heating where supply and return water temperature were 55 and 48 C respectively. For both cases, the PI controller was used. Furthermore, the building model also considered a 200-liter stratified tank, which was highly insulated. All losses due to storage and distribution were also accounted for. Building structural details and system specifications are briefly elaborated in Publication V. 43

49 Methods 44

50 4 Results and Analysis This chapter reports the outcomes of the research. All the findings are summarized in the following steps: 1. Daily DHW usages (L/(person. day)) at building level, apartment level and occupant level, usage frequency, and DHW ratio are discussed. In addition, monthly DHW usage factors for different months of the year are also presented in tabulated form. 2. Hourly DHW usage profiles for different occupant groups during weekdays, weekends and total days are comprehensively presented. Usage frequency at hourly level and the effect of occupant number on developing the hourly DHW profiles are discussed. In addition, the mathematical formulation of profiles to be implemented in the simulation tools are elaborated. 3. The estimated value of internal heat load, CO 2 emission, and humidity generation are conveyed. In addition, building operation hours, usage rate of occupancy, appliances, and lighting are thoroughly explained for building types covered by EPBD. 4. The sizing power equations of GSHP at design outdoor temperature, which covers 100% of heat for DHW and SH, are reported. The accuracy of power sizing equations was tested in different scenarios such as different design outdoor temperatures, different occupant groups, or buildings built according to Finnish building regulation of All findings are reported in the published articles. The summary of all these findings is discussed in this chapter. 4.1 DHW usage at daily level The energy need for DHW heating seems significant for annual energy use in a building. DHW usage seems very unpredictable; it may change from one weather condition to another, one building to another, one apartment to another, or one occupant to another. The daily DHW usage variations on building, apartment, and occupant level were revealed in [53] and Publication I. 45

51 Results and Analysis DHW usage at building level The onsite measured data from four different buildings during two consecutive years yielded DHW usage variations of 39 to 47 L/(person. day). In addition, usage variations of 15-18% were noted from one building to another and one year to another. However, some common behaviors were observed; DHW usage during summer was low compared to the DHW usage during winter. Reasons may include higher outdoor temperature or summer vacation (Publication I) DHW usage at apartment level The onsite data were collected from four different buildings, which had 182 apartments. The daily arithmetic mean, first and third quartiles, and median DHW usages (L/(person. day)) at apartment level are shown in Figure 4.1. Arithmetic mean values were higher compared to the 50th percentile or median values. In addition, the 25th percentile (first quartile) and 75th percentile (third quartile) followed the seasonal gradients. The arithmetic mean of 182 apartment populations was 51.8 L/(person. day), which could not be representative daily DHW usage due to the different occupant numbers in apartments (Publication I). Consumption, L/(person. day) SD Months of Year 25 % Median Mean 75 % + SD Figure 4.1. Arithmetic mean, median, 25th and 75th percentile, standard deviation for average DHW usages at apartment level (182 apartments) (Publication I) DHW usage at occupant level These four buildings had 379 occupants and the arithmetic mean of the full set of 379 people yielded the daily average DHW usage. From individual datasets of 379 occupants, DHW usage variation was found from L/(person. day), as shown in Figure 4.2. However, almost 70% of daily DHW usage ranged from 20 to 70 L/(person. day) during WD, WE, and total days. In addition, the distribution shape was visually seemed as lognormal. Moreover, usage variations from 0.5 to 2.5 L/(person. day) were also found during WD and WE (Publication I). 46

52 Results and Analysis Frequency 24% 20% 16% 12% 8% 4% 0% >250 Consumption, L/(person. day) WD WE Total Figure 4.2. Usage frequency of DHW during WD, WE and Total days (Publication I). The average DHW usages were 43 and 42 L/(person. day) during WD and WE, as reported in Publication I. The usage variations emphasized the importance of individual profiles for WD, WE and total days. The daily arithmetic mean of the full set of 379 occupants, first and third quartiles, median DHW usages (L/(person. day)) for WD, WE and total days are shown in Figure 4.3. The daily arithmetic mean and median of the full set of 379 occupants were 43.4 and 35.4 L/(person. day), respectively Consumption, L/(person. day) SD Months of Year Mean 25 % Median 75 % + SD (a) Consumption, L/(person. day) SD Months of Year Mean 25 % Median 75 % + SD (b) Consumption, L/(person. day) Months of Year -SD Mean 25 % Median SD (c) Figure 4.3. Arithmetic mean, 25th, 50th and 75th percentile, standard deviation for average DHW usages at occupant level (full population set of 379 occupants a) WD, b) WE, and c) Total days (Publication I). A full set of monthly usage factors of arithmetic mean, 25th percentile, 50th percentile, and 75th percentile for WD, WE, total DHW usages are shown in Table

53 Results and Analysis Table 4.1. Monthly usage factors (MF) of arithmetic mean, 25th percentile, 50th percentile, and 75th percentile for WD, WE, Total days (full population of 379 occupants) (Publication I). Annual average specific consumption, L/(person. Day ) Monthly DHW usages factor (MF) Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Arithmetic mean, Total Arithmetic mean, WD Arithmetic mean, WE Median, Total Median, WD Median, WE th percentile, Total th percentile, WD th percentile, WE th percentile, Total th percentile, WD th percentile, WE

54 Results and Analysis These monthly factors can be used to estimate annual DHW usage and size a heating system such as GSHP or solar thermal. The daily usages for any month are obtained by multiplying the annual average specific usages (L/(person. day)) by the respective month s usage factor. The highest and lowest DHW usages are 47 and 37 L/(person. day) for the corresponding months of November and July. WD and WE factors can be used for detailed calculation. 4.2 DHW usage at hourly level The hourly DHW usages have a significant impact on load match analysis and sizing of a heating system. The usage volume of DHW varies over the 24 hours in a day with some usage peaks. This study narrowed down from daily DHW usages in L/(person. day) to hourly DHW usages in L/(person. hour) Usage frequency at hourly level The usage frequency of DHW at hourly level seems highly significant for estimating the power of a heating system. Hourly usage frequency or DHW withdrawal volume may play a vital role in overall system performance. In order to understand the importance of hourly DHW profile, hourly DHW usage data of 191 occupants were analyzed in Publication II where morning and evening DHW usage peaks were reported. The morning and evening peaks during WD were observed between 7:00 to 9:00 and 20:00 to 22:00, respectively. DHW usage variations were found during peak hours on WD, i.e L/(person. hour), whereas during non-peak hours the usage variations showed nearly uniform behaviors. The frequency of DHW for WD and WE in November were tabulated in Publication II. The usage frequency of DHW during peak and nonpeak hours on WD are shown in Figure :00 9: Frequency Frequency Consumption, L/(person. hour) : Consumption, L/(person. hour) :00 Frequency Consumption, L/(person. hour) Frequency Consumption, L/(person. hour) 49

55 Results and Analysis : : Frequency Frequency Consumption, L/(person. hour) Consumption, L/(person. hour) Figure 4.4. Usage frequency of DHW at 8:00, 9:00, 20:00, 21:00 (peak hours) and at 14:00, 15:00 (non-peak hours) during November WD (Publication II). The morning s usage peak shifted 2-3 hours later during WE, whereas evening usages kept to the same period as on WD. The average of DHW usages at peak hours during November WD and WE were 4.1 and 3.6 L/(person. hour), respectively. Similarly, the average of DHW usages at non-peak hours were 1.1 and 1.4 L/(person. hour) for November WD and WE, respectively. Hourly average DHW usage profiles are shown in Figure 4.5. Morning and evening peaks are clearly visible and the movement of morning peaks during WE is well illustrated. In addition, the highest daily DHW usages were reported in November followed by January and August (Publication II), which shows good agreement with the results in Table 4.1. The daily average DHW usages (L/(person. day)) for WD and WE varied by 2-7% and 3-7%, respectively compared to the daily average usages (L/(person. day)), as given in Table 4.1. Moreover, the average hourly DHW usage patterns, obtained from 164 profiles, were nearly alike for the months of January, August and November. Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour WD WE Total (a) Consumption, L/(person. hour) :00 3:00 Hour WD WE Total (b) Hour WD WE Total (c) Figure 4.5. Hourly average usage profiles of DHW a) August, b) November, and c) January (Publication II). Consumption, L/(person. hour) :00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 50

56 Results and Analysis Hourly DHW usage profiles The average of 164 DHW usage profiles dampened and smoothened the peaks, making it impossible to illustrate the actual hourly peaks. Thus, the average hourly profiles were not considered as representative hourly profiles. Occupant number was ranked as the most significant variable in DHW usage [9] and it could dampen the DHW hourly peaks. Larger groups had a steady usage pattern whereas more turbulence was observed for the smaller groups [54]. The method chapter has comprehensively discussed procedure for selecting good candidates for hourly DHW profiles. In each group there were a couple of good candidates that could be used as representative profiles for the corresponding group, as reported in Publication II. A set of good candidates is shown in Figure 4.6. Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour P1 P2 P3 P4 (a) Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour P1 P2 P3 P4 (b) Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 P1 Hour P2 P3 P4 (c) Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour P1 P2 P3 P4 (d) Figure 4.6. Similar DHW usage candidates of November for a) 31-person, b) 10-person, c) 3- person, and d) 1-person group (Publication II). A large number of datasets were analyzed in order to propose the most representative hourly profile for each group. These were not reported as the exact hourly DHW usage profile of the groups but as the representative one. It was assumed that the smaller groups would have larger DHW usages in peak hours than the larger groups (Publication II). Based on this assumption, the most precise profiles for each group is shown in Figure 4.6. The representative profiles of 5 different groups for November are shown in Figure 4.7. Some common features were reported: the two peaks in each profile, shifted morning peaks in WE profiles compared to the WD profiles, more steady DHW usages during non-peak hours, and turbulence of DHW usages during the evening. A similar procedure was followed to develop the representative profiles for 5 different groups for August, as reported in Publication II. 51

57 Results and Analysis Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour 191 Per. 31 Per. 10 Per. 3 Per. 1 Per. (a) Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour 191 Per. 31 Per. 10 Per. 3 Per. 1 Per. (b) Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour 191 Per. 31 Per. 10 Per. 3 Per. 1 Per. (c) Figure 4.7. Proposed DHW usage profiles for November a) WD, b) WE, and c) Total days (Publication II). This study also developed the hourly DHW usage profiles for other groups. DHW usage profiles for the large occupant groups of 54, 82 and 164 people were reported in Publication II, as shown in Figure 4.8. Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour Hour 164 Per. 82 Per. 54 Per. 164 Per. 82 Per. 54 Per. (a) (b) Figure 4.8. DHW usages of occupant groups for November a) WD and b) WE (Publication II). The hourly factors for November and August were tabulated in Publication II, and are shown in Table 4.2 and Table 4.3. These representative profiles of 5 groups can be used with the monthly DHW usage profile, as shown in Table 4.1. For instance, annual average specific DHW usage (43 L/(person. day)) needs to be multiplied by the usage factor of respective month, and further distributed according to the hourly DHW usage factors during the 24 hours of the day. Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 52

58 Table 4.3. Hourly DHW usage factors for the month of August (Publication II). 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 Weekday 50 Per Per Per Per Per Weekend 50 Per Per Per Per Per Total 50 Per Per Per Per Per Table 4.2. Hourly DHW usage factors for the month of November (Publication II). 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 Weekday 50 Per Per Per Per Per Weekend 50 Per Per Per Per Per Total 50 Per Per Per Per Per

59 Results and Analysis The monthly DHW factor (Table 4.1) and hourly DHW factor (Table 4.2 and Table 4.3) can be used in the following equations to get the hourly DHW profile of August and November (Publication II). v = V f 24 (4.1) v,, = v f, (4.2) Where, V, annual daily average DHW usages (L/(person. day)), f, monthly DHW usages factor (dimensionless), v, average hourly DHW usages at m month (L/(person. hour)), v,,, hourly DHW usages at t hour of m month of year for group (L/(person. hour)), f,, hourly DHW usages factor (dimensionless) (Table 4.2 and Table 4.3). As discussed in the method (Chapter 3), the other winter months from October to March could follow the hourly DHW usage factors of 5 groups for November (Table 4.2). Similarly, the August hourly factor (Table 4.3) could be used for the other summer months from April to September Example of DHW profiles setting for simulation tools Two examples illustrate how to use the results of this study in order to set up the DHW profiles in simulation tools. One example is for a 3-occupant house and the other is for a 50-occupant apartment building. Simulation tools require three types of input data: average DHW use in L/(person. day), occupant number and DHW usage profile. The simulation tool multiplies the average DHW use of each occupant (L/(person. day)) and number of occupants (people), which gives flow rates in L/day. Afterward, this flow rate is distributed according to the hourly usage factor. In addition, the default values of DHW supply temperature and cold-water temperature are considered for energy estimation. To determine the profiles for simulation tools, the following steps are needed: Step 1: collect the annual average specific consumption in L/(person. day) (arithmetic mean, Total) from Table 4.1, which can be directly used in the simulation tools. Step 2: enter the occupant number in the simulation tool (two cases were considered; 3 people and 50 people). Step 3: plot the DHW hourly usage factor (Table 4.3 and Table 4.2) as a percentage, as shown in Figure 4.9. The profile for more than 50 people (Total) for November and August was considered. The following equation is used to determine the usage factors as a percentage: f, = f, (4.3) F Where, f,, hourly usage factor as percentage of sum of all hourly factors for a given group (%), f,, hourly DHW usages factor (dimensionless), F, sum of all hourly factors for a given group (dimensionless). 54

60 Results and Analysis Usages percentage 12% 10% 8% 6% 4% 2% 0% (a) Figure 4.9. Plotted hourly usage factors of DHW as a percentage scale for a more than 50-person profile (a) November (Total) and (b) August (Total). Step 4: for the hourly profile of more than 50 people, the monthly usage factor (MF) (Table 4.1) was multiplied by the DHW hourly usage factor (obtained from Step 3 as a usage percentage). For instance, the November monthly DHW usages factor of (Table 4.1), was multiplied by the usage percentage (Figure 4.9a) to get the November hourly profile, as shown in Figure 4.10a. Similarly, the August monthly DHW usages factor of (Table 4.1) was multiplied by the usage percentage (Figure 4.9b) to get the August hourly profile, as shown in Figure 4.10b. Usages % with MF 12% 9% 6% 3% 0% 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour (a) (b) Figure DHW schedule for an apartment building a) November (Total) and b) August (Total). In order to illustrate the additional profiles for a house of 3 people, same steps of 3 and 4 were followed. DHW hourly usage factors as percentage scale for 3- person profile and DHW schedule for a house are shown in Figure 4.11 and Figure 4.12, respectively. Usages percentage Usages % with MF 12% 9% 6% 3% 0% 12% 9% 6% 3% 0% 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour (b) 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour 55

61 Results and Analysis 18% 18% 15% 15% Usages percentage 12% 9% 6% 3% 0% 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour Usages percentage 12% 9% 6% 3% 0% 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour (a) Figure Plotted hourly usage factors of DHW as a percentage scale for 3-person profile (a) November (Total) and (b) August (Total). (b) 18% 18% 15% 15% Usages % with MF 12% 9% 6% 3% 0% 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour Usages % with MF 12% 9% 6% 3% 0% 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour (a) Figure DHW schedule for a house a) November (Total) and b) August (Total). The earlier part of Subsection showed the hourly usage profiles for the summer and winter months. It is also possible to generate one profile corresponding to each group for the whole year, which can be used in the simulation tools. For that purpose, the following steps are needed: Step 1: collect the annual average specific consumption in L/(person. day) (arithmetic mean, Total) from Table 4.1, which can be directly used in the simulation tools; Step 2: enter the occupant number in the simulation tool; Step 3: consider the average monthly usage factor of 1.0 (Table 4.1); Step 4: obtain the hourly profile from the average of the hourly factors of November and August using Equation (4.4), multiply this by the average monthly usage factor of 1.0, as shown in Figure f, = f, + f, (4.4) 2 Where, f,, average hourly usage factor (dimensionless), f,, hourly DHW usages factor at hour t for November (dimensionless), f,, hourly DHW usages factor at hour t for August (dimensionless). (b) 56

62 Results and Analysis Usages % with MF 18% 15% 12% 9% 6% 3% 0% 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour (a) Figure DHW schedule (average hourly usage factor) for the whole year a) more than 50- person profile, apartment building and b) 3-person profile, single-family house. Usages % with MF 18% 15% 12% 9% 6% 3% 0% 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour (b) 4.3 Mathematical formulation of profiles The key variables of DHW usage profile are shown and then it is explained how they fit the DHW usage data. Only November WD data is presented in detail. The other three datasets were thoroughly reported in Publication III. The obtained formulas can be used directly in the energy simulation tools in order to get information about DHW take-off pattern, sizing of heating systems and other applications Reference groups correlations and energy use formula The representative profiles for occupant groups consisting of 1 person (1p), 3 people (3p), 10 people (10p), 20 people (20p), 31 people (31p), 41 people (41p), 54 people (54p), 82 people (82p), and 191 people (191p) were reported in Publication III, as shown in Figure Of these, hourly DHW profiles for 1p and 3p were not found to be statistically relevant due to the high deviation from the DHW trend both quantitatively and qualitatively. In addition, a common pattern of DHW usage profile for any group of people larger than 50 was observed, as shown in Figure The dataset for 82p seemed the most representative and was considered as the main structural or fit curve. The following three criteria were considered while selecting the dataset for 82p (Publication III): Qualitative: two maxima were found at 8:00 and 21:00 that followed the general characteristics for all datasets; Quantitative: average daily DHW usages of and L/person were found for the whole group of representatives and the group of 82p, respectively. The value for the group of 82p was the closest one to the whole group daily DHW usage; Applicative: this could suit of an average apartment block as it accounts for a large occupant number of 82p. 57

63 Results and Analysis Consumption, L/(person. hour) Per. 82 Per. 54 Per. 41 Per. 31 Per. 20 Per. 10 Per. 3 Per. 1 Per Hour Figure Hourly DHW usages of all datasets for November s WD (Publication III). Consumption, L/(person. hour) Per. 82 Per. 54 Per Hour Figure Hourly DHW usages of common occupant groups for November s WD (Publication III). The structural curve t (1 to 24 hours) was split into three segments: segment 1 (00:00 8:00), segment 2 (8:00 21:00) and segment 3 (21:00 24:00). The equations were reported for these three segments, as shown in below Publication III: t t t t, 1 t[h]< 8 (4.5) E (t) = t t t t,8 t[h] < t 0.539t 21 t[h] < 24 A constrained least squares method was used for data interpolation, using the R software [82], as discussed in [83]. By minimizing the energy use, differences between the observed and fitted values were expressed as the constraint. The average daily DHW usage of L/person was calculated using Equation (4.5), which showed a variation of 0.022% compared to the average measured DHW usage of L/person. For a case without constraint, the variation of 3.7% compared to the average measured results was found. The structural curve, measured data curve and unconstrained fit curve are shown in Figure

64 Results and Analysis Consumption, L/(person. hour) DATA 82p. Fit 1-8 Fit 8-21 Fit Unconstrained Hour Figure Data (in black dotted) vs. structural curve (in red) vs. unconstrained (in blue) fit for 82 people for November s WD (Publication III) Correlation between datasets and chosen structural dataset A correlation between the chosen structural dataset of 82p and each dataset is reported in Publication III. Each correlation interpreted as: E (t) = f [ E (t) ] = A(n) + B(n) E (t) (4.6) Where, t, time (1:00-24:00), A(n), structural coefficient (dimensionless), B(n), structural coefficient (dimensionless), n, number of occupant in group. Explicit expression of the structural coefficient for November WD, November WE, August WD and August WE were tabulated in Publication III. For instance, a least squares fit of the A(n) and B(n) structural coefficients was given for E41, as shown in Equation (4.7). The difference between the measured data and predicted formula was approximately 2.3%, which indicated that the structural formula had a good predictive power E (t), 1 t[h] < 8 (4.7) E (t) = E (t), 8 t[h] < E (t). 21 t[h] < DHW usage ratio The DHW ratio, defines as the ratio of hot water to total water usage, depends on factors such as the supply water temperature, seasonal variations, and occupant practices. The cold-water temperature has substantial effects on overall energy need for DHW heating DHW usages ratio at daily level The DHW ratios for WD, WE, and total days (all days in a week) were , and , respectively (Publication I). These followed the seasonal gradients. Higher and lower DHW usage ratios were observed during November to February and during May to August, respectively. Moreover, nearly 80% of the DHW usage ratio was from 0.3 to 0.5, as presented in Figure

65 Results and Analysis Frequency 50% 40% 30% 20% 10% 0% Consumption ratio of hot to cold water WD WE Total Figure Frequency of DHW usage ratio (for a full population of 379 occupants) (Publication I) DHW usage ratio at hourly level The hourly usage trend of DHW during WD and WE are not alike, as shown in Table 4.2 and Table 4.3. Thus, it is expected to get different DCW profiles during a week. Higher DHW ratios were found during peak hours whereas the ratio was very low from 1:00 to 6:00 at night. The average DHW ratio for November was reported as between 0.30 and 0.33 (Publication II). The average DCW profile and DHW ratio for the month of November are shown in Figure Consumption, L/(person. hour) :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour WD WE Total DHW ratio :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Hour WD WE Total (a) (b) Figure a) Average hourly usage profile of Cold (total) water, b) DHW ratio for November WD, WE and Total days [55]. 4.5 Internal heat load Occupancy rate and corresponding heat load from bodies have shown dynamic behaviors. Body surface variations and different activity levels may change body heat generation. Higher variations in body surface area are observed in buildings occupied by children and young people such as schools, kindergartens, and daycare centers compared to the other building categories such as detached houses, apartment buildings, office buildings, department stores, hotels, restaurants, sports halls, and hospitals, which are mainly occupied by adults [21]. The detailed information on metabolic rate, body surface area, and input parameters required to estimate the body heat loss during winter and summer were reported in [21], and are presented in Table

66 Results and Analysis Table 4.4. Summary of building types, metabolic rate, body surface area, and total heat losses [21]. Building type Body Summer Winter Average Metabolic surface rate. M area. Q Q Q Q Q Q A met m 2 W W W W W W Detached house Apartment building Office building Department store Hotel Restaurant Sport, terminal, theatre School Daycare center ( yr.) Kinder garden ( yr.) Hospital Meeting room Classroom Computerclassroom The basic input parameters for the simulation tools are occupancy rate, installed lighting and appliance powers, dry and total heat load. The building operation hours followed the ASHRAE standard [18]. The usage rate of a building is defined as the average usages of the building whereas CO 2 emission and humidity generation were estimated values. Usage rate and load values were empirical data as shown in Publication IV, which could be applied to energy estimation for all rooms and zones. In addition, information on CO 2 emission and humidity generation can be used for the sizing of ventilation system. Moreover, lighting unit load depends on daylight utilization factor, control system and installed power, which can be used as input values for energy calculation in cases where detailed lighting information is not available. A summary of operation hours, usage rate, and average loads for energy estimation is shown in Table

67 Results and Analysis Table 4.5. Summary of operation hours, usage rate, and average loads for energy estimation (Publication IV). Building type Operation hours Occupancy Appliances Lighting Usage Total Dry Hum- CO2 Occup. Usage Unit Usage Unit rate idity Gene- rate rate load rate load gener- ration ation Time h/24h d/7d W/m 2 W/m 2 g/(m 2.h) l/(m 2.h) m2/per. W/m 2 W/m 2 Detached house 00:00-24:00 Apartment 00:00- building 24:00 Office 07:00- building 18:00 Department 08:00- store 21:00 Hotel 00:00-24:00 Restaurant 06:00-00:00 Sport, 08:00- terminal, 22:00 theatre School 08:00-17:00 Daycare 07:00- center 19:00 Hospital 00:00-24: Building usage schedule structure The hourly building usage profiles were not alike for all building categories, as discussed in Publication IV. In addition, the variations were reported during weekdays and weekends. The schedules for occupancy, lighting, and appliances were empirical data obtained from ASHRAE, Finnish, Estonian and some other building codes [18, 20, 52]. A remarkable difference in occupancy, lighting and appliance hourly schedule were observed in residential buildings. However, similar schedules for occupancy, lighting, and appliances could be used for nonresidential buildings. The unit load of occupancy, lighting, and appliances could be multiplied with the hourly usage schedules for energy calculation, as reported in Publication IV. Input parameters and the format of schedules for pren were discussed in Publication IV. 62

68 Results and Analysis 4.7 Application of profiles This section shows the application of profiles in heating system sizing. DHW usage profiles, occupancy profiles, lighting and appliance profiles are used to develop the sizing power equation of a GSHP system that required to supply 100% of heat for DHW and SH at design outdoor temperature without any topup heater. The GSHP control system, operation principle, and GSHP s power equations are comprehensively addressed GSHP and control system model The existing operation principle of GSHP is shown in Figure The system is used to heat domestic water and space. An electric heater was connected to the GSHP, aiming to provide additional heat if needed. Heat energy required for SH followed the heating curve and need for DHW heating followed the set-point temperature for supply DHW. The heat deficiency was estimated in degree minutes (DM). The default DM value was assigned in the control system with a couple of degrees as dead band. For instance, the default DM value was - 60 Cmin. If the estimated DM value was less than the default value, the heat pump would switch on immediately. However, if the heat pump failed to supply sufficient heat for SH and DHW, then the electric heater would start according to the following default steps: Step 1 (1 kw, 120<DM<180); Step 2 (2kW, 180<DM<240); Step 3 (3kW, 240<DM<300); Step 4 (4kW, 300<DM<360); Step 5 (5kW, 360<DM<420); Step 6 (6kW, 420<DM) (Publication V). The detailed steps are marked in Figure Figure An existing DM controlled GSHP operation system with the supply and return water temperature, temperature of outdoor air, and top-up electric heater power (Publication V). Supply and return water temperatures are marked with red and green lines, respectively. The supply water temperature ranged between C and C during space and DHW heating, respectively. Heat pump was switched off when the DM was less than -60 Cmin. Top-up electrical heater power (marked with a black line) was raised according to the default steps. The GSHP control system was implemented in an IDA-ICE simulation. The simplified plant model is shown in Figure Two heat pumps and two storage tanks were considered. One set of GSHP and storage tank were used for 63