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Experimental verification of a method for estimating energy for domestic hot water production in a 2-stage district heating substation
Energy and Buildings 41 (2009) 169–174
Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Experimental verification of a method for estimating energy for domestic hot water production in a 2-stage district heating substation Kimmo Yliniemi *, Jerker Delsing, Jan van Deventer Lulea˚ University of Technology, Department of Computer Science and Electrical Engineering, Division EISLAB, 971 87 Lulea˚, Sweden
A R T I C L E I N F O
A B S T R A C T
Article history: Received 29 May 2008 Received in revised form 25 July 2008 Accepted 17 August 2008
In this paper we compare our estimate of energy consumption for domestic hot water production in a building with the measured value. The energy consumption for hot water production is estimated from the measured total power consumption. The estimation method was developed using computer simulations, and it is based on the assumption that hot water production causes rapid and detectable changes in power consumption. A comparison of our estimates with measurements indicates that the uncertainty in estimation of hot water energy consumption is 10%. Thus, the estimate is comparable to class 3 energy meter measurements, which have an uncertainty of 2–10%. ß 2008 Elsevier B.V. All rights reserved.
Keywords: Estimation of energy usage Domestic hot water Energy usage District heating
1. Introduction 1.1. Background Decreasing energy consumption in buildings is a priority in Europe. A building’s energy declaration is a means of decreasing energy consumption [1], as it specifies levels of energy use for heating and hot water production. The energy expert that makes the declaration also suggests how to decrease the building’s energy consumption; today, energy consumption is usually not measured separately. Studies show that consumers change their behaviour when energy consumption for hot water production and heating is measured in apartment buildings and detached houses [2–5]. These separate measurements give consumers the information and incentive (cost reduction) needed to decrease energy consumption. In some cases, energy consumption decreases by 30–40%, as reported by Gullev and Poulsen [3]. One study done in Japan by Ueno et al. on detached houses with hot water energy measurement systems showed that energy consumption for heating domestic homes decreased by 20% on average and that the total power consumption in the buildings decreased by 18% on average when information was made available to the users. The buildings studied had gas and electric heating. Table 1 contains the expected/measured average decrease in energy consumption. Bohm and Danning report a 15–20% decrease
* Corresponding author. Tel.: +46 70 222 4786. E-mail address: [email protected] (K. Yliniemi). 0378-7788/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2008.08.008
in total energy consumption in Denmark [2]. According to Koiv and Toode, Estonian apartment buildings decreased their domestic hot water (DHW) consumption by more than 50% between 1975 and 2004 [5]. Between 1999 and 2004, domestic hot water consumption dropped from 2.8 to 2.0 l/m2. The main reasons for the decrease were consumption measurements, renovation of domestic hot water systems, and installation of low flow taps and showers [5]. In other words, an efficient way to decrease energy consumption is to keep users aware of their usage. This should be done at the lowest possible cost and with high accuracy. We show here that it is possible to provide this information using the existing energy meter in the district heating substation. In this paper, we focus on testing the method developed in [6] and described in [7]. Using this method, we estimate the energy consumption for hot water production and compare these estimates with empirical measurements. The study was carried out at a 2-stage coupled district heating substation with hot water circulation (see Fig. 1). 2. Theory A brief description of the energy separation method (ECOS) is given here; more details can be found in the studies by Yliniemi et al. in [6–8]. ECOS estimates the energy consumption for domestic hot water production and heating based on measurements of total power consumption. ECOS classifies sudden changes in total power consumption, changes related to domestic tap water usage. The method uses a combination of filtering and detection techniques. The time constants for heating a building are long (it takes hours for a
K. Yliniemi et al. / Energy and Buildings 41 (2009) 169–174
170
Nomenclature DHS DHW E Heat HEX HWC Loss n {n} P R Rad S TOT
district heating substation domestic hot water energy consumption (kWh) building heat heat exchanger domestic hot water circulation losses sample number reference to items in a figure power consumption (kW) return radiator supply total heating + DHW + losses
Greek letter t sampling time (s)
building to cool down or heat up), while the time constants are short for domestic hot water production (the water is heated instantly from 10 to 55 8C). This means that power consumption for building heating and power consumption for domestic hot water production can be separated from the total power consumed. The energy separation method was developed using Matlab Simulink. A Simulink model of a substation and a building connected in parallel was used to test the algorithm [6,9,10]. The computer simulations showed an uncertainty of 1–2% in the estimates. The simulations were carried out under ideal conditions: perfect heat meters and substations that were working well. As a result, we expected the uncertainty to be larger than what the simulations indicate. In order to employ this separation method, we need to be able to detect when water tapping starts and stops. If this information is
Table 1 Estimated decrease of energy consumption when measuring heating and hot water separately Source Bohm and Danning [2] Gullev and Poulsen [3] Ueno et al. [4] Koiv and Toode [5]
RAD
DHW
Total 15–20% 15–17% 18%
20% 30%
available, we can get a very accurate estimate of the tap energy consumption and the heating energy consumption. One way to get this information is to monitor the valve that controls the tap water heat exchanger or the radiator heat exchanger. Many district heating systems in Sweden have self-acting valves (completely without electronics) for the tap water circuit. This makes it difficult to monitor the valve position without additional sensors. However, if the valve is electronically controlled, monitoring is easy. The method we propose can be used even when the valves are selfregulating. The method works as follows. A tapping (start and stop) is detected by looking at C (1) (the difference between two samples of the measured power). If this value is greater than a certain threshold, or if the power consumption is greater than the power that can be produced by the radiator heat exchanger alone, then we classify it as tapping. P TOT ðt 1Þ P TOT ðtÞ ¼ C
(1)
The total power consumption PTOT(t) = PDHW(t) + PHeat(t) is measured by a normal heat meter and then integrated to get Q(t). For a normal household, one can assume that hot water consumption is not constant. In reality, the tappings are done randomly (with respect to start, duration, and flow). Let us assume that a building’s heat consumption is constant for the duration of a tapping unless the tapping is extremely long. We say PTOT(t) is equal to PHeat(t) when no tapping occurs, and PDHW(t) is equal to PTOT(t) PHeat(t). If no tapping is detected, then PDHW(t) = 0 and PHeat(t) = P(t). The threshold for C is determined by the dynamic properties of the DHS and varies depending on the size of the derivative (of measured power) when tapping starts and stops; we say that the derivative is close to 0 for building heating and is large for tap water heating. Choosing a small value for the threshold gives a good separation, but a value that is too small will classify every change in energy consumption as tapping. A 10-s long tapping would yield P TOT ð0Þ ¼ P Heat ð0Þ At t = 0 there is no tapping; tapping starts at t = 0+: PTOT ð1Þ ¼ PHeat ð0Þ þ P tap ð1Þ PTOT ð2Þ ¼ PHeat ð1Þ þ Ptap ð2Þ .. . PTOT ð10Þ ¼ P Heat ð9Þ þ P tap ð10Þ
Fig. 1. 2-Stage coupled district heating substation. A D-flow meter {1} measured the primary return flow and return temperature. The primary supply temperature was measured by a Pt 100 sensor {2} mounted on the supply pipe. The D-flow meter {3} measured the primary return temperature and flow of the radiator heat exchanger (HEX). The D-flow meter {4} was placed on the incoming domestic cold water pipe. The Kamstrup flow meter and one of its temperature sensors were placed on the return pipe of the domestic hot water circulator (DHWC) {5}. The other temperature sensor was placed on the supply pipe of the DHWC {6}.
Assume that PHeat(t) remains constant for 0 < t < 10 (the duration of the tapping). This gives Ptap(1) = PTOT(1) Pheat(0) and so on. PTOT(t) is measured by the system’s heat energy meter. A way of improving this estimate is to measure PTOT(11) and PTOT(0), make a linearization between the points and use it to approximate the value of PHeat between PTOT(0) and PTOT(11), since PTOT(11) can be greater or smaller than PTOT(0). Heating for tap water consumption in a building varies more rapidly than in a house. A domestic hot water HX needs to be able to produce hot water on demand if no storage tank is used. Since the power consumption for building heating varies relatively slowly and the power consumption for tap water heating varies
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quickly and has greater magnitude, it is easy to separate the two types of events (Fig. 2).
3. Comparison of estimates against experimental measurements
2.1. Estimating how the energy is used in the building
Estimates of energy consumption for DHW performed using the ECOS estimation method were compared to the measurements of energy consumption for domestic hot water production. The measurements were performed on a 2-stage coupled district heating substation.
Here we present how energy consumption is estimated for heating and hot water production. We start with measurements and then describe estimates using the ECOS energy estimation method. The total power consumption in kW, given in (2), is made up of power consumption from the radiators (PRad(t)), domestic hot water PDHW(t) production, and distribution losses (PLoss(t)) inside the building: P TOT ðtÞ ¼ P Rad ðtÞ þ PDHW ðtÞ þ P Loss ðtÞ
(2)
The building’s energy consumption in kWh is divided into the same categories (3): ETOT ðtÞ ¼ ERad ðtÞ þ EDHW ðtÞ þ ELoss ðtÞ
(3)
Assuming that all of the distribution losses contribute to building heating, the expressions for heating become (4) and (5): P Heat ¼ PRad ðtÞ þ PLoss ðtÞ
(4)
EHeat ¼ ERad ðtÞ þ ELoss ðtÞ
(5)
Combining (4) and (2) we get (6): P TOT ¼ PHeat ðtÞ þ PDHW ðtÞ
(6)
The total energy consumption then simplifies to (7), which only contains building heating and DHW energy consumption: ETOT ¼ EHeat ðtÞ þ EDHW ðtÞ
(7)
The energy consumption for heating estimated using ECOS (8): EˆHeat ðtÞ ¼ ECOSHeat ðP TOT ðtÞÞ
(8)
The energy consumption for DHW production estimated by ECOS (9): EˆDHW ðtÞ ¼ ECOSDHW ðPTOT ðtÞÞ
(9)
3.1. Substation Older substations are usually of the two stage type, while newer installations are usually of the parallel type. The different types of substations (3-stage, 2-stage, and parallel) are described in detail in [11–13]. Energy measurement in district heating substations is covered in [14,15]. In Sweden, 2-stage substations are very common in apartment buildings and other large buildings. For this reason, the experiment was conducted on this type of substation. Thanks to cooperation with HSB (a local apartment building owner), a 2-stage substation fulfilling our requirements was made available for the experiment. The substation supplies heating and hot water to a small apartment building with 40 apartments. Table 2 contains the technical specifications of the heat exchangers in the 2-stage coupled district heating substation used for our experiments. The substation components were manufactured in 1998–1999 and installed in the building in 1999. The heat exchangers are of the tube type, manufactured by Cetetherm AB. The substation is equipped with a control system from Siemens. 3.2. Measurement equipment Table 3 presents the measurement equipment used. The D-flow ultrasonic flow meters have built-in temperature sensors. All the sensors were connected to the measurement PC. The PC was in turn connected to the internet for easy access of the measurement data. The measurement equipment in Table 3 has the same or better performance as class 3 energy meters, according to the SS-EN1434 energy measurement standard. All sensors were calibrated by the supplier. 3.3. Uncertainty in the measurements We analysed the uncertainty in the measurements based on the performance of the class 3 energy meters as given by the standard EN1434:1 [14]. The method for calculating the combined uncertainty was taken from the book by Coleman and Steele [16]. To measure the total energy consumption ETOT(t), using one energy meter for heating EHeat(t) and one for domestic hot water Table 2 Substation heat exchanger data
Fig. 2. The threshold used for separation of hot water energy consumption.
Technical data
Heat exchanger data
DHW heat exchanger Temperature in Temperature out Flow Maximum pressure drop Capacity
Cetetube Primary 65 8C, secondary 10 8C Primary 24 8C, secondary 55 8C Primary 1.1 l/s, secondary 1 l/s 100 kPa 186 kW
RAD heat exchanger Temperature in Temperature out Flow Maximum pressure drop Capacity
Cetetube Primary 100 8C, secondary 45 8C Primary 48 8C, secondary 55 8C Primary 1.3 l/s, secondary 6.6 l/s 100 kPa 275 kW
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Table 3 Measurement equipment used in the experiment (the equipment has class 3 or better accuracy) Type
Sampling rate (Hz)
D-flow ultrasonic flow meter Seneca Z-4RTD and PT100 Kamstrup Multical Energymeter PC104 equipped with Linux
1 1 0.2
production EDHW(t), we used the following expression: ETOT ðtÞ ¼ EHeat ðtÞ þ EDHW ðtÞ
(10)
which states that the sampling frequency should be twice as high as the frequency of interest. During the measurement period, delta T was high and the flow varied between low and high rates. The total flow was high when there was tapping. This period in particular has many tapping events. This means that we can consider the total energy measurement to have an uncertainty close to 4%. The measured energy consumption for heating had a low flow and a high delta T, giving it an uncertainty of 7–8%. The combined uncertainty for the test period is estimated to be 6–11% for both heating and domestic hot water production; see Table 4. 3.4. Evaluating the algorithm
The combined uncertainty for the measurement is given by The algorithm is tested in three steps:
u2c ¼ u2Heat þ u2DHW
(11)
If we measure ETOT(t) and EDHW(t), we can get the energy consumption for heating using (12).: EHeat ðtÞ ¼ ETOT ðtÞ EDHW ðtÞ
(12)
The combined uncertainty for estimating EHeat(t) this way becomes 2
uˆ c ¼ u2TOT þ u2DHW
(13)
The calculated uncertainties are given in Table 4 for both low and high flows. We can see that the combined uncertainty is more than 14% in the worst case when both meters experience low flow. The combined uncertainty is 11% if one of the meters measures low flow rates and the other measures high flow rates. This indicates that measurement is not always the best option. The effect of sampling time was also studied in order to quantify dependence of uncertainty on sampling time. A period of 5.5 h with random tapping was sampled in intervals ranging from 1 to 60 s. The uncertainty in measuring energy consumption for heating using a sampling time of 60 s was less than 0.5%. The uncertainty in measuring domestic hot water energy consumption greatly depends on the length of tapping. We consider the case of 15 s tapping starting at random times: a 30 s sampling time would on average miss half of the tapping; and a 20 s tapping would be detected 2/3 of the time, meaning the energy consumption would be underestimated by 30%. As the tapping length increases, the uncertainty decreases. This is consistent with the sampling theorem, Table 4 Measurement uncertainty for class 3 energy meters and flow meters according to EN1434 Value (%)
Uncertainty variable
Flow and delta T
Class 3 energy meters 10 10 10 4 4 4 6 14 11
uHeat uDHW uTOT uHeat uDHW uTOT uc uc uc
Low Low Low High High High High/high Low/low Low/high
Value (%)
Uncertainty variable
Flow
Class 3 flow meters 5 2
uflow uflow
Low flow High flow
For the energy meters, the low or high values denote the magnitude of the temperature difference and flow. uc is the combined uncertainty for the two energy meters.
1. Data acquisition and logging to computer. The measured variables are total power and energy consumption, and power and energy consumption for hot water production. 2. Estimation of energy consumption using the algorithm. The energy consumption and the corresponding power consumption were estimated using measurements. Estimation was done offline using measurements collected in Step 1. The data were collected over several months; occasionally, we had data drop outs because of bad communication. For the estimation, data was checked for communication drop-outs, and we did not use such baddata in the evaluation. 3. Comparison of measured and estimated results. The estimated values for the energy consumption and the corresponding power consumption were compared with the measured values. 3.5. Test conditions We used two different consumption scenarios for our experiments. These were Winter time A very dynamic load consisting of both tapping and a decreasing space-heating demand. A decreasing need for heating creates disturbances, since the algorithm is made to act slowly on changes in heat demand. An 8-h period is studied here. Summer time No space-heating demand. The losses in the domestic hot water circuit heat the building. The estimation method calculates the energy losses in the domestic hot water circulation as part of building heating. The measurements show that the uncertainty in estimating the energy consumption for hot water production is similar to that in the winter case.
4. Results 4.1. Uncertainty estimation The standard deviation for the 8-h test period is given in Table 5. The uncertainties are calculated using methods from [16]. Since the ECOS algorithm has start-up behaviour, we discarded a start-up period of 1.5 h in the calculations, thus improving the Table 5 The relative estimation uncertainty in percent Value (%)
Uncertainty variable
Type
1.6 8.6
uˆ Heat uˆ DHW
2 sˆ Heat 2 sˆ DHW
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uncertainty estimation as shown in Figs. 3 and 4. The relative uncertainty in percent for the estimates using ECOS is given by (14) and (15): uˆ DHW ¼
EˆDHW ðtÞ EDHW ðtÞ 100 EDHW ðtÞ
(14)
uHeat ¼
EˆHeat ðtÞ EHeat ðtÞ 100 EHeat ðtÞ
(15)
The data shown here are considered to be representative of the data obtained in the field test. In Fig. 3 the estimated power consumption for DHW is plotted for an 8-h period. For this period, the total energy consumption for hot water is 100 kWh, and the estimated total energy consumption for hot water is 97.5 kWh. Fig. 4 gives the relative uncertainty for both the estimated hot water consumption and the estimated space heating consumption. They are for obvious reasons opposites of each other. The ECOS algorithm goes through a start-up behaviour. Thus, the first hour could be considered the start-up time, a notion confirmed by the relatively large uncertainty shown there. This was caused by a small accumulation of energy, and thus the relative uncertainty was increased. 5. Discussion The measured total energy consumption did not increase or decrease with the estimation method. This means that if the installed meter did not measure all of the energy consumption (i.e., if it missed hot water tappings), the estimation method could not estimate this energy. In Figs. 3 and 4, we see that ECOS overestimated the energy consumption for DHW production. Between hours 1 and 3, the heating need dropped quickly and resulted in an overestimation of the heating requirement. Thus, the energy consumption for hot water production was underestimated. After 4 h, the heating need increased (in the evening) and the heat requirement was underestimated; thus, energy consumption for hot water production was slightly overestimated. When the heating requirement did not change during a tapping, the estimation uncertainty for tapping was lower. Uncertainty analysis of the measurements showed that the uncertainty can vary between 4–10% when using class 3 energy meters, according to the European measurement standard EN1434, if one energy meter measures the total energy consumption and another measures heating. We estimated the energy consumption for hot water production by deducting heating from the total. By doing
Fig. 4. Uncertainty in the estimation of energy consumption for DHW production and heating with their corresponding two sigma bounds. The uncertainty in estimating energy consumption for hot water production is below 10%.
so, we obtained a combined uncertainty that varies between 6% and 14% depending on the flow and temperature difference. The uncertainty in the DHW estimate for energy consumption for in this test was below 10%. It is not always possible to install energy meters. The cost of the meter,installation and space requirement have to be considered. Especially for smaller (compact) substations, such as those used in domestic homes, the space requirement is critical. Installation of additional sensors may require extensive re-piping, as they must be built as compactly as possible, with the fewest ‘‘straights’’ necessary to ensuring proper flow measurements [17,18]. Therefore, we believe that estimation is a very good option in many cases, considering both accuracy and cost effectiveness. In general, we conclude that a. The study indicates that the estimation uncertainty of ECOS is 10%, which is less than the combined uncertainty for the two energy meters. b. The estimation approach has the potential to be a useful tool for estimating energy consumption in buildings. c. The estimation uncertainty is comparable to that for class 3 energy meters. d. Implementing ECOS in energy meters lowers the cost compared to measuring hot water production using an additional energy meter. Future work will include experimental verification of the ECOS algorithm for parallel coupled substations. Acknowledgements We wish to express our gratitude to Svensk Fja¨rrva¨rme for funding this research and to HSB Norr for letting us use their building and Internet connection for the field test. A special thanks to Hans Engstro¨m for his help and Lulea˚ Energy for funding the installation of the measurement equipment. References
Fig. 3. Measured total power consumption, measured and estimated DHW power consumption. The peaks of estimated power consumption for hot water production have lower amplitudes and wider bases than the measured.
[1] F.J. Rey, E. Velasco, F. Varela, Building Energy Analysis (BEA): a methodology to asses building energy labelling, Energy and Buildings (July) (2007) 709–716. [2] B. Bohm, P.O. Danning, Monitoring the energy consumption in a district heated apartment buliding in Copenhagen, with specific interest in the thermodynamic performance, Energy and Buildings (36) (2004) 229–236.
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[3] L. Gullev, M. Poulsen, The Installation of Meters Lead to Permanent Changes in Consumer Behaviour, News from DBDH, No. 3, 2006. [4] T. Ueno, R. Inada, O. Saeki, K. Tsuji, Effectiveness of an energy consumption information system for residental buildings, Applied Energy (November) (2006) 868–883. [5] T.A. Koiv, A. Toode, Trends in domestic hot water consumption in Estonian apartment buildings, in: Proceedings Estonian Acad. Sci. Eng., 2006. [6] K. Yliniemi, Fault Detection in District Heating Substations, Lic. thesis, Lulea˚ University of Technology, Lulea˚, Sweden, November 2005. [7] K. Yliniemi, A Method and a Device for Measurement of Energy for Heating Tap Water Separated from the Buildings Heating Energy Consumption, World Intellectual Property Organization (WIPO), No. PCT/SE2006/001250, 2007 (PCT patent application). [8] K. Yliniemi, Y. Jomni, J. Delsing, J. van Deventer, Saber energy meter for district heating substations, in: Annual International District Energy Association Conference, Arizona, USA, June 19, 2007. [9] J. Gustafsson, J. van Deventer, J. Delsing, Thermodynamic simulation of a detached house with district heating subcentral, in: SysCon 2008—IEEE International Systems Conference, IEEE, 2008.
[10] T. Persson, District Heating for Residential Areas with Single-Family Housing, PhD thesis, Lund Institute of Technology, Sweden, 2005. [11] P. Gummerus, Analys av Konventionella Abonnentcentraler i Fja¨rrva¨rmesystem, PhD thesis, Chalmers Tekniska Ho¨gskola, Go¨teborg, Sweden, October 1989. [12] J. Wollerstrand, District Heating Substations Performance, Operation and Design, PhD thesis, Lunds Tekniska Ho¨gskola, Sweden, 1997. [13] Svensk Fja¨rrva¨rme, Fja¨rrva¨rmecentralen utfo¨rande och installation, Tekniska besta¨mmelser F:101, Svensk Fja¨rrva¨rme, Sweden, November 2004. [14] Standard, Heat Meters. Part 1. General Requirements, Technical Report SS-EN 1434-1, Svensk Standard, Sweden, 1997. [15] Svensk Fja¨rrva¨rme, F:104 va¨rmema¨tare-tekniska branschkrav och ra˚d om ma¨tarhantering, Tekniska besta¨mmelser F:104, Svensk Fja¨rrva¨rme, Sweden, 2004. [16] H.W. Coleman, W.G. Steele, Experimentation and Uncertainty Analysis for Engineers, 2nd ed., Wiley Interscience, USA, 1999. [17] C. Carlander, Installation Effects and Self Diagnosis for Ultrasonic Flow Meters, PhD thesis, Lulea˚ University of Technology, Sweden, 2001. [18] J. Berrebi, Self-Diagnosis Techniques and Their Applications to Error Reduction for Ultrasonic Flow Measurement, PhD thesis, Lulea˚ University of Technology, Sweden, 2004.
Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Experimental verification of a method for estimating energy for domestic hot water production in a 2-stage district heating substation Kimmo Yliniemi *, Jerker Delsing, Jan van Deventer Lulea˚ University of Technology, Department of Computer Science and Electrical Engineering, Division EISLAB, 971 87 Lulea˚, Sweden
A R T I C L E I N F O
A B S T R A C T
Article history: Received 29 May 2008 Received in revised form 25 July 2008 Accepted 17 August 2008
In this paper we compare our estimate of energy consumption for domestic hot water production in a building with the measured value. The energy consumption for hot water production is estimated from the measured total power consumption. The estimation method was developed using computer simulations, and it is based on the assumption that hot water production causes rapid and detectable changes in power consumption. A comparison of our estimates with measurements indicates that the uncertainty in estimation of hot water energy consumption is 10%. Thus, the estimate is comparable to class 3 energy meter measurements, which have an uncertainty of 2–10%. ß 2008 Elsevier B.V. All rights reserved.
Keywords: Estimation of energy usage Domestic hot water Energy usage District heating
1. Introduction 1.1. Background Decreasing energy consumption in buildings is a priority in Europe. A building’s energy declaration is a means of decreasing energy consumption [1], as it specifies levels of energy use for heating and hot water production. The energy expert that makes the declaration also suggests how to decrease the building’s energy consumption; today, energy consumption is usually not measured separately. Studies show that consumers change their behaviour when energy consumption for hot water production and heating is measured in apartment buildings and detached houses [2–5]. These separate measurements give consumers the information and incentive (cost reduction) needed to decrease energy consumption. In some cases, energy consumption decreases by 30–40%, as reported by Gullev and Poulsen [3]. One study done in Japan by Ueno et al. on detached houses with hot water energy measurement systems showed that energy consumption for heating domestic homes decreased by 20% on average and that the total power consumption in the buildings decreased by 18% on average when information was made available to the users. The buildings studied had gas and electric heating. Table 1 contains the expected/measured average decrease in energy consumption. Bohm and Danning report a 15–20% decrease
* Corresponding author. Tel.: +46 70 222 4786. E-mail address: [email protected] (K. Yliniemi). 0378-7788/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2008.08.008
in total energy consumption in Denmark [2]. According to Koiv and Toode, Estonian apartment buildings decreased their domestic hot water (DHW) consumption by more than 50% between 1975 and 2004 [5]. Between 1999 and 2004, domestic hot water consumption dropped from 2.8 to 2.0 l/m2. The main reasons for the decrease were consumption measurements, renovation of domestic hot water systems, and installation of low flow taps and showers [5]. In other words, an efficient way to decrease energy consumption is to keep users aware of their usage. This should be done at the lowest possible cost and with high accuracy. We show here that it is possible to provide this information using the existing energy meter in the district heating substation. In this paper, we focus on testing the method developed in [6] and described in [7]. Using this method, we estimate the energy consumption for hot water production and compare these estimates with empirical measurements. The study was carried out at a 2-stage coupled district heating substation with hot water circulation (see Fig. 1). 2. Theory A brief description of the energy separation method (ECOS) is given here; more details can be found in the studies by Yliniemi et al. in [6–8]. ECOS estimates the energy consumption for domestic hot water production and heating based on measurements of total power consumption. ECOS classifies sudden changes in total power consumption, changes related to domestic tap water usage. The method uses a combination of filtering and detection techniques. The time constants for heating a building are long (it takes hours for a
K. Yliniemi et al. / Energy and Buildings 41 (2009) 169–174
170
Nomenclature DHS DHW E Heat HEX HWC Loss n {n} P R Rad S TOT
district heating substation domestic hot water energy consumption (kWh) building heat heat exchanger domestic hot water circulation losses sample number reference to items in a figure power consumption (kW) return radiator supply total heating + DHW + losses
Greek letter t sampling time (s)
building to cool down or heat up), while the time constants are short for domestic hot water production (the water is heated instantly from 10 to 55 8C). This means that power consumption for building heating and power consumption for domestic hot water production can be separated from the total power consumed. The energy separation method was developed using Matlab Simulink. A Simulink model of a substation and a building connected in parallel was used to test the algorithm [6,9,10]. The computer simulations showed an uncertainty of 1–2% in the estimates. The simulations were carried out under ideal conditions: perfect heat meters and substations that were working well. As a result, we expected the uncertainty to be larger than what the simulations indicate. In order to employ this separation method, we need to be able to detect when water tapping starts and stops. If this information is
Table 1 Estimated decrease of energy consumption when measuring heating and hot water separately Source Bohm and Danning [2] Gullev and Poulsen [3] Ueno et al. [4] Koiv and Toode [5]
RAD
DHW
Total 15–20% 15–17% 18%
20% 30%
available, we can get a very accurate estimate of the tap energy consumption and the heating energy consumption. One way to get this information is to monitor the valve that controls the tap water heat exchanger or the radiator heat exchanger. Many district heating systems in Sweden have self-acting valves (completely without electronics) for the tap water circuit. This makes it difficult to monitor the valve position without additional sensors. However, if the valve is electronically controlled, monitoring is easy. The method we propose can be used even when the valves are selfregulating. The method works as follows. A tapping (start and stop) is detected by looking at C (1) (the difference between two samples of the measured power). If this value is greater than a certain threshold, or if the power consumption is greater than the power that can be produced by the radiator heat exchanger alone, then we classify it as tapping. P TOT ðt 1Þ P TOT ðtÞ ¼ C
(1)
The total power consumption PTOT(t) = PDHW(t) + PHeat(t) is measured by a normal heat meter and then integrated to get Q(t). For a normal household, one can assume that hot water consumption is not constant. In reality, the tappings are done randomly (with respect to start, duration, and flow). Let us assume that a building’s heat consumption is constant for the duration of a tapping unless the tapping is extremely long. We say PTOT(t) is equal to PHeat(t) when no tapping occurs, and PDHW(t) is equal to PTOT(t) PHeat(t). If no tapping is detected, then PDHW(t) = 0 and PHeat(t) = P(t). The threshold for C is determined by the dynamic properties of the DHS and varies depending on the size of the derivative (of measured power) when tapping starts and stops; we say that the derivative is close to 0 for building heating and is large for tap water heating. Choosing a small value for the threshold gives a good separation, but a value that is too small will classify every change in energy consumption as tapping. A 10-s long tapping would yield P TOT ð0Þ ¼ P Heat ð0Þ At t = 0 there is no tapping; tapping starts at t = 0+: PTOT ð1Þ ¼ PHeat ð0Þ þ P tap ð1Þ PTOT ð2Þ ¼ PHeat ð1Þ þ Ptap ð2Þ .. . PTOT ð10Þ ¼ P Heat ð9Þ þ P tap ð10Þ
Fig. 1. 2-Stage coupled district heating substation. A D-flow meter {1} measured the primary return flow and return temperature. The primary supply temperature was measured by a Pt 100 sensor {2} mounted on the supply pipe. The D-flow meter {3} measured the primary return temperature and flow of the radiator heat exchanger (HEX). The D-flow meter {4} was placed on the incoming domestic cold water pipe. The Kamstrup flow meter and one of its temperature sensors were placed on the return pipe of the domestic hot water circulator (DHWC) {5}. The other temperature sensor was placed on the supply pipe of the DHWC {6}.
Assume that PHeat(t) remains constant for 0 < t < 10 (the duration of the tapping). This gives Ptap(1) = PTOT(1) Pheat(0) and so on. PTOT(t) is measured by the system’s heat energy meter. A way of improving this estimate is to measure PTOT(11) and PTOT(0), make a linearization between the points and use it to approximate the value of PHeat between PTOT(0) and PTOT(11), since PTOT(11) can be greater or smaller than PTOT(0). Heating for tap water consumption in a building varies more rapidly than in a house. A domestic hot water HX needs to be able to produce hot water on demand if no storage tank is used. Since the power consumption for building heating varies relatively slowly and the power consumption for tap water heating varies
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quickly and has greater magnitude, it is easy to separate the two types of events (Fig. 2).
3. Comparison of estimates against experimental measurements
2.1. Estimating how the energy is used in the building
Estimates of energy consumption for DHW performed using the ECOS estimation method were compared to the measurements of energy consumption for domestic hot water production. The measurements were performed on a 2-stage coupled district heating substation.
Here we present how energy consumption is estimated for heating and hot water production. We start with measurements and then describe estimates using the ECOS energy estimation method. The total power consumption in kW, given in (2), is made up of power consumption from the radiators (PRad(t)), domestic hot water PDHW(t) production, and distribution losses (PLoss(t)) inside the building: P TOT ðtÞ ¼ P Rad ðtÞ þ PDHW ðtÞ þ P Loss ðtÞ
(2)
The building’s energy consumption in kWh is divided into the same categories (3): ETOT ðtÞ ¼ ERad ðtÞ þ EDHW ðtÞ þ ELoss ðtÞ
(3)
Assuming that all of the distribution losses contribute to building heating, the expressions for heating become (4) and (5): P Heat ¼ PRad ðtÞ þ PLoss ðtÞ
(4)
EHeat ¼ ERad ðtÞ þ ELoss ðtÞ
(5)
Combining (4) and (2) we get (6): P TOT ¼ PHeat ðtÞ þ PDHW ðtÞ
(6)
The total energy consumption then simplifies to (7), which only contains building heating and DHW energy consumption: ETOT ¼ EHeat ðtÞ þ EDHW ðtÞ
(7)
The energy consumption for heating estimated using ECOS (8): EˆHeat ðtÞ ¼ ECOSHeat ðP TOT ðtÞÞ
(8)
The energy consumption for DHW production estimated by ECOS (9): EˆDHW ðtÞ ¼ ECOSDHW ðPTOT ðtÞÞ
(9)
3.1. Substation Older substations are usually of the two stage type, while newer installations are usually of the parallel type. The different types of substations (3-stage, 2-stage, and parallel) are described in detail in [11–13]. Energy measurement in district heating substations is covered in [14,15]. In Sweden, 2-stage substations are very common in apartment buildings and other large buildings. For this reason, the experiment was conducted on this type of substation. Thanks to cooperation with HSB (a local apartment building owner), a 2-stage substation fulfilling our requirements was made available for the experiment. The substation supplies heating and hot water to a small apartment building with 40 apartments. Table 2 contains the technical specifications of the heat exchangers in the 2-stage coupled district heating substation used for our experiments. The substation components were manufactured in 1998–1999 and installed in the building in 1999. The heat exchangers are of the tube type, manufactured by Cetetherm AB. The substation is equipped with a control system from Siemens. 3.2. Measurement equipment Table 3 presents the measurement equipment used. The D-flow ultrasonic flow meters have built-in temperature sensors. All the sensors were connected to the measurement PC. The PC was in turn connected to the internet for easy access of the measurement data. The measurement equipment in Table 3 has the same or better performance as class 3 energy meters, according to the SS-EN1434 energy measurement standard. All sensors were calibrated by the supplier. 3.3. Uncertainty in the measurements We analysed the uncertainty in the measurements based on the performance of the class 3 energy meters as given by the standard EN1434:1 [14]. The method for calculating the combined uncertainty was taken from the book by Coleman and Steele [16]. To measure the total energy consumption ETOT(t), using one energy meter for heating EHeat(t) and one for domestic hot water Table 2 Substation heat exchanger data
Fig. 2. The threshold used for separation of hot water energy consumption.
Technical data
Heat exchanger data
DHW heat exchanger Temperature in Temperature out Flow Maximum pressure drop Capacity
Cetetube Primary 65 8C, secondary 10 8C Primary 24 8C, secondary 55 8C Primary 1.1 l/s, secondary 1 l/s 100 kPa 186 kW
RAD heat exchanger Temperature in Temperature out Flow Maximum pressure drop Capacity
Cetetube Primary 100 8C, secondary 45 8C Primary 48 8C, secondary 55 8C Primary 1.3 l/s, secondary 6.6 l/s 100 kPa 275 kW
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Table 3 Measurement equipment used in the experiment (the equipment has class 3 or better accuracy) Type
Sampling rate (Hz)
D-flow ultrasonic flow meter Seneca Z-4RTD and PT100 Kamstrup Multical Energymeter PC104 equipped with Linux
1 1 0.2
production EDHW(t), we used the following expression: ETOT ðtÞ ¼ EHeat ðtÞ þ EDHW ðtÞ
(10)
which states that the sampling frequency should be twice as high as the frequency of interest. During the measurement period, delta T was high and the flow varied between low and high rates. The total flow was high when there was tapping. This period in particular has many tapping events. This means that we can consider the total energy measurement to have an uncertainty close to 4%. The measured energy consumption for heating had a low flow and a high delta T, giving it an uncertainty of 7–8%. The combined uncertainty for the test period is estimated to be 6–11% for both heating and domestic hot water production; see Table 4. 3.4. Evaluating the algorithm
The combined uncertainty for the measurement is given by The algorithm is tested in three steps:
u2c ¼ u2Heat þ u2DHW
(11)
If we measure ETOT(t) and EDHW(t), we can get the energy consumption for heating using (12).: EHeat ðtÞ ¼ ETOT ðtÞ EDHW ðtÞ
(12)
The combined uncertainty for estimating EHeat(t) this way becomes 2
uˆ c ¼ u2TOT þ u2DHW
(13)
The calculated uncertainties are given in Table 4 for both low and high flows. We can see that the combined uncertainty is more than 14% in the worst case when both meters experience low flow. The combined uncertainty is 11% if one of the meters measures low flow rates and the other measures high flow rates. This indicates that measurement is not always the best option. The effect of sampling time was also studied in order to quantify dependence of uncertainty on sampling time. A period of 5.5 h with random tapping was sampled in intervals ranging from 1 to 60 s. The uncertainty in measuring energy consumption for heating using a sampling time of 60 s was less than 0.5%. The uncertainty in measuring domestic hot water energy consumption greatly depends on the length of tapping. We consider the case of 15 s tapping starting at random times: a 30 s sampling time would on average miss half of the tapping; and a 20 s tapping would be detected 2/3 of the time, meaning the energy consumption would be underestimated by 30%. As the tapping length increases, the uncertainty decreases. This is consistent with the sampling theorem, Table 4 Measurement uncertainty for class 3 energy meters and flow meters according to EN1434 Value (%)
Uncertainty variable
Flow and delta T
Class 3 energy meters 10 10 10 4 4 4 6 14 11
uHeat uDHW uTOT uHeat uDHW uTOT uc uc uc
Low Low Low High High High High/high Low/low Low/high
Value (%)
Uncertainty variable
Flow
Class 3 flow meters 5 2
uflow uflow
Low flow High flow
For the energy meters, the low or high values denote the magnitude of the temperature difference and flow. uc is the combined uncertainty for the two energy meters.
1. Data acquisition and logging to computer. The measured variables are total power and energy consumption, and power and energy consumption for hot water production. 2. Estimation of energy consumption using the algorithm. The energy consumption and the corresponding power consumption were estimated using measurements. Estimation was done offline using measurements collected in Step 1. The data were collected over several months; occasionally, we had data drop outs because of bad communication. For the estimation, data was checked for communication drop-outs, and we did not use such baddata in the evaluation. 3. Comparison of measured and estimated results. The estimated values for the energy consumption and the corresponding power consumption were compared with the measured values. 3.5. Test conditions We used two different consumption scenarios for our experiments. These were Winter time A very dynamic load consisting of both tapping and a decreasing space-heating demand. A decreasing need for heating creates disturbances, since the algorithm is made to act slowly on changes in heat demand. An 8-h period is studied here. Summer time No space-heating demand. The losses in the domestic hot water circuit heat the building. The estimation method calculates the energy losses in the domestic hot water circulation as part of building heating. The measurements show that the uncertainty in estimating the energy consumption for hot water production is similar to that in the winter case.
4. Results 4.1. Uncertainty estimation The standard deviation for the 8-h test period is given in Table 5. The uncertainties are calculated using methods from [16]. Since the ECOS algorithm has start-up behaviour, we discarded a start-up period of 1.5 h in the calculations, thus improving the Table 5 The relative estimation uncertainty in percent Value (%)
Uncertainty variable
Type
1.6 8.6
uˆ Heat uˆ DHW
2 sˆ Heat 2 sˆ DHW
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uncertainty estimation as shown in Figs. 3 and 4. The relative uncertainty in percent for the estimates using ECOS is given by (14) and (15): uˆ DHW ¼
EˆDHW ðtÞ EDHW ðtÞ 100 EDHW ðtÞ
(14)
uHeat ¼
EˆHeat ðtÞ EHeat ðtÞ 100 EHeat ðtÞ
(15)
The data shown here are considered to be representative of the data obtained in the field test. In Fig. 3 the estimated power consumption for DHW is plotted for an 8-h period. For this period, the total energy consumption for hot water is 100 kWh, and the estimated total energy consumption for hot water is 97.5 kWh. Fig. 4 gives the relative uncertainty for both the estimated hot water consumption and the estimated space heating consumption. They are for obvious reasons opposites of each other. The ECOS algorithm goes through a start-up behaviour. Thus, the first hour could be considered the start-up time, a notion confirmed by the relatively large uncertainty shown there. This was caused by a small accumulation of energy, and thus the relative uncertainty was increased. 5. Discussion The measured total energy consumption did not increase or decrease with the estimation method. This means that if the installed meter did not measure all of the energy consumption (i.e., if it missed hot water tappings), the estimation method could not estimate this energy. In Figs. 3 and 4, we see that ECOS overestimated the energy consumption for DHW production. Between hours 1 and 3, the heating need dropped quickly and resulted in an overestimation of the heating requirement. Thus, the energy consumption for hot water production was underestimated. After 4 h, the heating need increased (in the evening) and the heat requirement was underestimated; thus, energy consumption for hot water production was slightly overestimated. When the heating requirement did not change during a tapping, the estimation uncertainty for tapping was lower. Uncertainty analysis of the measurements showed that the uncertainty can vary between 4–10% when using class 3 energy meters, according to the European measurement standard EN1434, if one energy meter measures the total energy consumption and another measures heating. We estimated the energy consumption for hot water production by deducting heating from the total. By doing
Fig. 4. Uncertainty in the estimation of energy consumption for DHW production and heating with their corresponding two sigma bounds. The uncertainty in estimating energy consumption for hot water production is below 10%.
so, we obtained a combined uncertainty that varies between 6% and 14% depending on the flow and temperature difference. The uncertainty in the DHW estimate for energy consumption for in this test was below 10%. It is not always possible to install energy meters. The cost of the meter,installation and space requirement have to be considered. Especially for smaller (compact) substations, such as those used in domestic homes, the space requirement is critical. Installation of additional sensors may require extensive re-piping, as they must be built as compactly as possible, with the fewest ‘‘straights’’ necessary to ensuring proper flow measurements [17,18]. Therefore, we believe that estimation is a very good option in many cases, considering both accuracy and cost effectiveness. In general, we conclude that a. The study indicates that the estimation uncertainty of ECOS is 10%, which is less than the combined uncertainty for the two energy meters. b. The estimation approach has the potential to be a useful tool for estimating energy consumption in buildings. c. The estimation uncertainty is comparable to that for class 3 energy meters. d. Implementing ECOS in energy meters lowers the cost compared to measuring hot water production using an additional energy meter. Future work will include experimental verification of the ECOS algorithm for parallel coupled substations. Acknowledgements We wish to express our gratitude to Svensk Fja¨rrva¨rme for funding this research and to HSB Norr for letting us use their building and Internet connection for the field test. A special thanks to Hans Engstro¨m for his help and Lulea˚ Energy for funding the installation of the measurement equipment. References
Fig. 3. Measured total power consumption, measured and estimated DHW power consumption. The peaks of estimated power consumption for hot water production have lower amplitudes and wider bases than the measured.
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