Domestic Hot Water Profiles for Energy Calculation in Finnish Residential Buildings Kaiser Ahmed 1, *, Petri Pylsy 1, 3, Jarek Kurnitski 1, 2 1 Aalto University, Department of Civil and Structural Engineering, Finland 2 allinn University of echnology, Faculty of Civil Engineering, Estonia 3 he Finnish Real Estate Federation, Finland *corresponding author: Rakentajanaukio 4 A, FI-215 Espoo, Finland; E-mail: kaiser.ahmed@aalto.fi ABSRAC he information of domestic hot water (DHW) consumption is important to identify the delivered energy for building sector. Daily basis hourly profile, daily profile, weekday and weekend profile, monthly profile might be helpful to optimize the required DHW consumption which further useful document for energy optimization and system designer. his paper has shown the investigated result of 182 Finnish residential apartments and draws a monthly, weekdays and weekend basis DHW profile. he finding also correlated with some factors such as occupant number, seasonal variation, behavioral approach, weather condition. he average consumption for January to December is noted 47 liter/person/day which are also varied based on monthly, weekdays and weekends weighted average factor. Higher consumption of DWH is observed during November to February whereas opposite behavior is observed during May to July. In addition, the hourly profiles for weekdays and weekend are different and DHW consumption is higher during weekdays. Most frequent DHW consumption is observed from to (L/person/day) and the ratio of hot to cold water is varied from.3 to.5. he obtained profiles will give an idea about the scenario of DHW consumption during the different months as well as variances of weekday and weekend. KEYWORDS Residential building; DHW; Monthly factor; Weekdays and weekend factor Abbreviation DHW, Domestic Hot Water RE, Renewable Energy WD, Week Day WE, Weekend SD, Standard Deviation 1. INRODUCION he building sector has consumed around % of world s energy (IEA, 8) whereas a large percentage of it has used for domestic hot water. Among this consumption, residential building shows significantly high percentage of consumption compare to small scale business users and large scale business users (Geudens, 8). he complexity of DHW has an involvement with user spectrums, local weather condition, energy cost, occupant density, end user behavior, local culture, etc. Other important parameters such as modern life style, vacation period, shower time, hygiene sense, and comfort life style might be added more DHW consumption. hat might be the reason of higher fluctuation of DHW consumption. Sometimes it might be difficult to find out the reason behind the higher fluctuation due to occupant s behavior, occupant density due to privacy problem etc. hus, some previous works have used the local statistical report on the occupant density, occupant type to predict the actual consumption. Occupancy is the key indicator of consumption (Parker, 3) which means that higher density increases the gross consumption. Merrigan (1988) has discussed the relation between occupant number and consumption rate which is nearly linear. He also added 45 l/d for each additional person where the occupant number is above 2. Many studies also found higher fluctuation as well as different consumption rate of DHW. Wiehagen and Sikora (2a) found the average DHW consumption was 236 l/d of 59 residences in Canada whereas per capita use was varied from 47 l/d to 86 l/d. Another North American study summarized the average consumption is 239 l/day which had a consideration of seasonal variation, ownership, occupant ages (Becker & Stogsdill, 199). V drew a conclusion of 6 Finnish households DHW consumption and found an average consumption is 135 l/day for every household and 43 l/day for individual occupant (IEA, 7).
Among the Nordic countries the individual consumption rate is fluctuated from l/day to 47 l/day. he DHW consumption values in Nordic building code are quite different which is shown in able 1. able 1: DHW consumption rate for Finnish, Estonian, Swedish, Norwegian, Danish residential apartment building l/person/day Source Finnish 46. Suomen rakentamismääräyskokoelma osa D3 (12): Rakennusten energiatehokkuus Estonian.3 Majandus- ja kommunikatsiooniministri määrus nr. 63 Swedish 33. Boverkets byggregler BBR19 Danish. Bygningsreglement 1 Norwegian. Byggteknisk forskrift EK 1 Furthermore, the hourly DHW consumption profiles are different to each other as well as German, Swiss, US, Canadian building s DHW hourly profile (IEA, 7). In another Dutch research work found that 1.5% of daily water use during the sleeping hours, 65% of water use during peak hours which is defined as the half hour after getting up and returning home and the half hour before leaving home and going to bed and 33.5% is used other parts of the days (Blokker 6). Climate zone and local temperature are correlated with the event schedule, duration, consumption volume. he consumption rate is increased around 1% and 13% from summer to fall and winter in New York City (Goldner, 1994). Additionally, more than 7.5% variation is observed during weekdays and weekends. he variation due to weather condition seems very high in Australian study and it is noted 3-48% of hot water and energy consumption (Hart and de Dear, 4). he energy consumption for DHW is increasing around 3% on the coldest winter days than on mildest days in Florida (Bouchelle and Parker, ). Other important issues are age, gender, occupation to build up a DHW consumption profile. Foekema and Engelsma (1) found the relationships in between age and frequency of use, age and shower duration, penetration rate and household size in Dutch building. he same study also drew strong relation between diurnal patterns and water consumption (Foekema and Engelsma, ). In another investigation on the Canadian senior s buildings found that the consumption is less than 44% compare to the family apartment building (he Canadian Mortgage and Housing Corporation, 1999). However, the total energy consumption is given vice versa result due to the higher required of heating energy. In another investigation by Goldner (1994), found the relation between DHW consumption and presence of children which indicate the proportional increment of DWH consumption due to the presence of children. Sometimes it also depends on other parameters such as ownership, income, age, occupant behaviors etc. Aydinalp et al. (4) found that lower consumption rate for renter occupied apartment compare to the owner occupied apartment. he main objective of this study wasto draw DHW use profiles for Finnish residential buildings taking into account monthly variation and with differentiation between weekdays and weekends for the whole year. he findings and profiles can be used as an input data for energy calculation, solar thermal and other DHW system design, district energy planning, evaluation of building energy concept etc. he profiles have been prepared to be included in the Finnish building code. 2. Methods his study is based on measurements of domestic hot and cold water consumption of 182 apartments. Apartments were situated in four different buildings and all apartments were rented flats. All buildings were located in Finland, City of ampere, and heating energy was produced by district heating. Basic information of buildings has mentioned in table 2. able 2: Basic information of buildings Building A Building B Building C Building D No. of apartment 27 42 56 57 No. of occupant 64 86 113 95 Consumption data was based on measurements of the individual smart water metering system in every apartment. Domestic hot and cold water were measured separately. he individual smart water meter system was used for billing purpose thus accuracy of measurement devices was good: the measurement accuracy was ± 2 % and starting sensitivity of the measuring sensor was 5 liter per hour. Nonetheless measurements were stored in a database only at resolution of 1 liter. he individual smart water metering system provided measurement for each apartment as follows Cumulative meter readings separately for domestic hot water and domestic cold water
Consumption rate of domestic hot water and domestic cold water (measured separately) per household during certain time period (e.g. daily consumption rate) In study two different kind of time resolution of measurements was realized: monthly and daily based. he consumption data was monthly based from 1 st of January 12 to end of April 13. Daily based consumption data was available from May 13 to end of January 14. In analysis basic measures of central tendency of data is utilized; mean, median, first quartile and third quartile. Also standard deviations are calculated. he consumption rate of domestic hot water per occupant per day is calculated according to the number of occupants at the building level. In this paper, daily based consumption data is used to form DHW monthly profiles (monthly factors) and taking into account deviation between consumption rate of weekdays and weekends. Also daily consumption data is utilized to define consumption ratio of DHW. Monthly factor MF i is defined by (1) Where, i is month which is concerned is the average domestic hot water consumption rate of the month i is the average domestic hot water consumption rate of the whole year If monthly factors are defined to weekdays and weekends separately, the consumption of the month includes only consumptions of weekdays or weekends whereas in yearly average consumption both weekdays as well as weekends are taken into account. he consumption ratio of DHW is defined as follows (2) Where, DHW is consumption rate of domestic hot water DCW is consumption rate of domestic cold water Due to quality of measurements data, some justification has been done. Daily consumption data contained nine months measurements. hus, missing month s data (February, March, and April) are assumed to be equivalent to November, October and September respectively to create whole 12 months dataset. Also consumption data of apartment is not taken into account if the apartment was vacant for two consecutive months. he daily consumption data is validated based on comparison of monthly consumption rates of two consecutive years 12 and 13. 3. Result he result might be more informatics of having more consecutive year s data. In addition, daily basis data for February, March and April might draw better scenario of DHW consumption of two consecutive years. he comparison in term of average consumption per person per day is presented in figure 1. Avg. 12-13 : 42 l/person/day 13-14 : 41 l/person/day Avg. 12-13 : 47 l/person/day 13-14 : 45 l/person/day Feb'13 to Jan'14 Feb'12 to Jan'13 Feb'13 to Jan'14 Feb'12 to Jan'13 (a) (b)
F Su M W M W F Su M W F S h M W F S h Avg. 12-13 : 39 l/person/day 13-14 : 37 l/person/day Avg. 12-13 : 56 l/person/day 13-14 : 56 l/person/day Feb'13 to Jan'14 Feb'12 to Jan'13 Feb'13 to Jan'14 Feb'12 to Jan'13 (c) (d) Figure 1: Monthly comparison of DHW consumption for two consecutive years a) A, b) B, c) C, d) D In Figure 2 is shown the comparison of four different buildings. he thick and dark lines indicate the consumption rate during year 13 whereas thin and light lines indicate the consumption rate during 12. Building D showed higher consumption rate compare to other buildings. 7 5 3 A 13 B 13 C 13 D 13 A 12 B 12 C 12 D 12 Figure 2: Comparison of DHW consumption data for two years he impact of calendar month reveled to be very important. During summer time the consumption rate was going down whereas it was increased during winter time. In June, the consumption rate was 42 l/person/day whereas November s consumption rate was 53 l/person/day. In figure 3 is shown daily basis average consumption of 182 apartments in November and June. It also indicates the weekday (blue line) and weekend (red line) variation. (a) (b) Figure 3: Daily basis average consumption of 182 apartments a) November and b) June
Different consumption rate was observed during weekdays and weekend. he consumption rate during weekday was little bit higher compare to weekend. he annual average DHW consumption for WD and WE were 47 and 46 l/person/day. herefore individual daily basis factor for weekdays, weekend and total were obtained. In addition, total consumption factor for weekday, weekends and total days were also calculated as shown in figure 4. 1.3 1.1.9.7 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Weighted average factor Weighted average factor (Week days) Weighted average factor (Weekend days) Figure 4: Weighted average factors of DHW consumption for weekday, weekends and total days. In figure 5 is indicated first and third quartiles, median, mean, standard deviation for weekdays, weekend, total consumption, (weekend and weekdays are all included). he horizontal number indicates the months of the year. hese figures clearly identify the higher standard deviation (SD), difference between median and mean value. All of these values may be used to find out the optimal configuration of DHW system. 1 1 2 3 4 5 6 7 8 9 1 11 12 Months of Year -SD 25 % Mean Median 75 % +SD (a) 1 1 2 3 4 5 6 7 8 9 1 11 12 Months of Year - SD 25 % Median Mean 75 % +SD (b)
1 - SD 1 2 3 4 5 6 7 8 9 1 11 12 Months of Year 25 % Median Mean 75 % + SD (c) Figure 5: First quartile, median, mean, third quartiles, standard deviation a) Weekdays, b) Weekend, c) otal consumption (Weekend and weekdays are all included) In table 3 is reported the summary of consumption according to Figure 4 and Figure 5. In the DHW and energy calculations mean, median or percentile consumption values may be used, depending on the purpose of the calculation. he annual average value needs to be multiplied with monthly profile value to obtain the monthly consumption value. otal profile values are for the case not taking into account variations between weekdays and weekends. In more detailed calculations with differentiation between weekdays, WD value is used for weekdays and WK value for weekends to calculate respective consumption rates. able 3: Mean, Median, 25 percentile, 75 percentile annual DHW consumption values and monthly factors for calculation of monthly consumption values with (WD and WK) and without (otal) differentiation between weekdays. Annual Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. avg Mean, otal 47 1.9 1.12.98.97.96.9.88.98.98.98 1.12 1.5 Mean,WD 47 1.1 1.11.99.99.97.91.9.98 1..99 1.12 1.4 Mean, WK 47 1.8 1.14.96.94.96.86.84.98.94.96 1.14 1.7 Median, otal 42 1.11 1.16 1.1.94.97.84.86.96.95 1.1 1.16 1.1 Median, WD 42 1.1 1.8.98.97.94.86.86.94.97.98 1.8 1.1 Median, WK 42 1.5 1..9.94.9.83.75.94.94.9 1. 1.9 25%, otal 23 1.17 1.21 1..99.97.93.92.96.99 1. 1.22 1.8 25%, WD 23 1.15 1.21 1.5.97.92.91.95.97.97 1.5 1.22 1.7 25%, WK 23 1.19 1.15.83.95.93.89.82.98.95.83 1.16 1.15 75%, otal 63 1.4 1.11.97.98.97.88.9.99.98.97 1.11 1.4 75%, WD 63 1.6 1.4.97.98.95.92.9.98.98.97 1.4 1.5 75%, WK 63 1.8 1.16 1.3 1.4.97.87.91.95 1.4 1.3 1.16 1.11 he consumption ratio was obtained by DHW consumption and total water consumption. he value was different for weekdays and weekend as well as variation was observed during the different months of the year. However, the average for the whole month was nearly similar. In figure 6 is presented the ratio of DHW and total water consumption per person per day..5.45 Annual WD avg..378 Annual WK avg..377 Annual otal avg..378..35.3 WD WK otal Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec. Figure 6: Ratio of DHW and total water consumption
1 3 5 7 9 1 11 1 13 1 15 175 25 >25 Frequency he average consumption was 47 l/person/day. However, the range of consumption in between to 25 l/person/day was observed during data processing. he deviation was 5 times more compare to the average consumption in some cases. he most common range was noted in between to 7 l/person/day. In figure 7 is shown the yearly basis frequency of DHW consumption for 182 apartments (weekday, weekend, and total). 16% 12% 8% 4% WD WK otal % Figure 7: Yearly basis frequency of DHW consumption for 182 apartments (weekday, weekend, and total) 4. Discussion he analysis was represented the DHW user profiles which might be integrated to the renewable energy production. As mentioned before that the DHW consumption rate depended on many factors which made it more difficult to draw a conclusion within few words. he variation of DHW consumption rate might be differed from apartment to apartment as well as building to building. his statement was proven when we investigated individual data set for each apartment. However, same type of apartments within different buildings A, B, C, and D (figure 1) showed the different consumption rate. he occupants of building D showed higher consumption rate whereas occupant of building C consumed lower rate among the four buildings. he average consumption rate of 182 apartments was 47 l/person/day which are nearly similar to the Finnish standard (46 l/person/day). Author had made a -test between the consumption rates of 4 buildings. he P value for building A, B, C, D were greater than 5% during weekday and weekend which indicated the higher fluctuation of consumption rate. Nevertheless, there was a common behavior observed such as low consumption rate during June to July for all apartments. he average consumption for June and July were 42 l/person/day and 41 l/person/day whereas 53 l/person/day and 49 l/person/day were observed for November and December. It might be the reason of higher outdoor temperature, summer vacation etc. Another important observation was lower consumption rate during weekend compare to weekday. he variation was found in between.5 to 2.5 l/person/day. It might be reason of different hourly profile of consumption during weekends and user behavior. his conclusion had drawn based on the average consumption rate of daily basis data (182 apartments). On the other hand, the different scenario was observed in figure 3. his figure had shown the daily basis consumption rate for specific month November and June. From that figure, it was quite difficult to draw a same conclusion. It had already discussed that the consumption rate was not similar for weekday and weekend. In figure 4 is shown the different weighted factors for weekdays, weekend and total month. he weekday and weekend weighted average factor were 1.7 and.989 which gave nearly one liter consumption variation between these two. In addition, the average weighted average factor for June and July was.891 which indicated the average consumption was nearly 42 l/person/day. On the other hand the average consumption for November and December were 51 l/person/day. It concluded that the consumption rate was higher during winter period and going lower at summer period. he statement wasalso applicable for all three different weighted factors too. Another important observation was consumption rate during December did not follow the winter gradient. Usually, it should follow the similar gradient from November to February. he changing behavior was obtained due to the Christmas vacation and New Year eve. hough the apartment was designed for multiple numbers of occupants, the average consumption by each person was considered as equal. Additional person beyond the two adult was considered as children. In this case, the consumption rate for all occupants was same. Usually, consumption rate of children is higher compare to the adult (Goldner, 1994). hat might be one reason of higher value of standard deviation. In Figure 5 is shown the first quartile, median, mean, third quartile, standard deviation of DHW consumption for weekdays, weekend and total days. It also illustrates annual average consumption which seemed a significant variance from the median value. he mean value was 47 l/person/day whereas the average median value was 42 l/person/day which showed a significant variance. he same behaviors were also observed in weekdays and weekend data set too. Another important observation was the difference between median and 75 percentile value. Higher variation indicates higher fluctuation of consumption rate. able 3 has mentioned the tabular format of mean, median, first quartile, third quartile value of DHW consumption for the whole year. he standard deviation has shown the difference range of consumption. In fact, there are some data cleaning was happened before the data processing otherwise the standard deviation might be shown higher range. Consumption ratio is important to find out the heat up water quantity. Figure 6 has shown different consumption ratio for weekday and weekend. he consumption ratio was higher during winter period and lower during summer period. he annual WD and WE average ratio were.378,.377. he value was quite similar to the total average. Around 7% data had shown the ratio in
between.4 to.5 for the whole year around. he ratio was going down during summer time. During summer time the mixing amount of cold water was higher compare to winter which might be the reason. Figure 7 shows the frequency of consumption rate per person per day. Most of the consumption rate in between to L/person/day. he higher and lower value beyond the range might be the reason of user behavior, occupant absence, losses on dramatic seasonal changes. 5. Conclusion Based on this study following conclusions can be drawn: he average (mean value) DHW consumption is 47 l/person/day when individual water metering system is used. Result is nearly similar to the value of the Finnish building code. he mean value of the consumption ratio of DHW is.38. he mean value of consumption is higher than the median value and there is a lot of variation between different apartments as well as different buildings. 7% of consumption rates are from to L/person/day. he consumption ratio of DHW is varied from.2 to.6 for most of the cases. Consumption rates during winter and summer time are different thus it is reasonable to utilize monthly factors During weekdays higher consumption rates are realized compared to the weekends. If more accurate DHW profile is needed the individual profiles of weekdays and weekends should be considered. Results of this study should be taken into account when e.g. solar collectors are considered. All the findings can use further as an input value to estimate e.g. the system energy and top-up heating energy. Different building simulation tools can be integrated with these monthly domestic hot water profiles. Some further research would be needed to take into account more precisely background variables (e.g. exact number of occupants, age and gender) effect on profiles. Also shorter time scale of the measurements might be increase the accuracy of DHW consumption and profiles. Acknowledgements he work has been done under the supervision of civil and structural engineering department of Aalto University, Finland. he authors appreciate the grant from K.V Lindholms stiftelse as a research support as well as thank to VS - Kodit for their cooperation. Reference 1. Aydinalp M., Ugursal V.I., and Fung A.S. (4). Modeling of the Space and Domestic Hot-Water Heating Energy-Consumption in the Residential Sector Using Neural Networks, Applied Energy, 79, 159-178. 2. Becker B., Stogsdill K., (199) A Domestic Hot Water Use Database, ASHRAE Journal 21 25. 3. 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