Energy and Buildings 41 (2009) 169–174

Experimental verification of a method for estimating energy for domestichot 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

Article history:

Received 29 May 2008

Received in revised form 25 July 2008

Accepted 17 August 2008

Keywords:

Estimation of energy usage

Domestic hot water

Energy usage

District heating

A B S T R A C T

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%.

� 2008 Elsevier B.V. All rights reserved.

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1. Introduction

1.1. Background

Decreasing energy consumption in buildings is a priority inEurope. A building’s energy declaration is a means of decreasingenergy consumption [1], as it specifies levels of energy use forheating and hot water production. The energy expert that makesthe declaration also suggests how to decrease the building’s energyconsumption; today, energy consumption is usually not measuredseparately. Studies show that consumers change their behaviourwhen energy consumption for hot water production and heating ismeasured in apartment buildings and detached houses [2–5].These separate measurements give consumers the information andincentive (cost reduction) needed to decrease energy consump-tion. In some cases, energy consumption decreases by 30–40%, asreported by Gullev and Poulsen [3]. One study done in Japan byUeno et al. on detached houses with hot water energy measure-ment systems showed that energy consumption for heatingdomestic homes decreased by 20% on average and that the totalpower consumption in the buildings decreased by 18% on averagewhen information was made available to the users. The buildingsstudied had gas and electric heating.

Table 1 contains the expected/measured average decrease inenergy consumption. Bohm and Danning report a 15–20% decrease

* Corresponding author. Tel.: +46 70 222 4786.

E-mail address: kimmo@ltu.se (K. Yliniemi).

0378-7788/$ – see front matter � 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 andToode, Estonian apartment buildings decreased their domestic hotwater (DHW) consumption by more than 50% between 1975 and2004 [5]. Between 1999 and 2004, domestic hot water consump-tion dropped from 2.8 to 2.0 l/m2. The main reasons for thedecrease were consumption measurements, renovation of domes-tic hot water systems, and installation of low flow taps andshowers [5]. In other words, an efficient way to decrease energyconsumption is to keep users aware of their usage. This should bedone at the lowest possible cost and with high accuracy. We showhere that it is possible to provide this information using theexisting 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 energyconsumption for hot water production and compare theseestimates with empirical measurements. The study was carriedout at a 2-stage coupled district heating substation with hot watercirculation (see Fig. 1).

2. Theory

A brief description of the energy separation method (ECOS) isgiven here; more details can be found in the studies by Yliniemi et al.in [6–8]. ECOS estimates the energy consumption for domestic hotwater production and heating based on measurements of totalpower consumption. ECOS classifies sudden changes in total powerconsumption, changes related to domestic tap water usage. Themethod uses a combination of filtering and detection techniques.The time constants for heating a building are long (it takes hours for a

Nomenclature

DHS district heating substation

DHW domestic hot water

E energy consumption (kWh)

Heat building heat

HEX heat exchanger

HWC domestic hot water circulation

Loss losses

n sample number

{n} reference to items in a figure

P power consumption (kW)

R return

Rad radiator

S supply

TOT total heating + DHW + losses

Greek lettert sampling time (s)

Table 1Estimated decrease of energy consumption when measuring heating and hot water

separately

Source RAD DHW Total

Bohm and Danning [2] 15–20%

Gullev and Poulsen [3] 15–17%

Ueno et al. [4] 20% 18%

Koiv and Toode [5] 30%

K. Yliniemi et al. / Energy and Buildings 41 (2009) 169–174170

building to cool down or heat up), while the time constants are shortfor domestic hot water production (the water is heated instantlyfrom 10 to 55 8C). This means that power consumption for buildingheating and power consumption for domestic hot water productioncan be separated from the total power consumed.

The energy separation method was developed using MatlabSimulink. A Simulink model of a substation and a buildingconnected in parallel was used to test the algorithm [6,9,10]. Thecomputer simulations showed an uncertainty of 1–2% in theestimates. The simulations were carried out under ideal condi-tions: perfect heat meters and substations that were working well.As a result, we expected the uncertainty to be larger than what thesimulations indicate.

In order to employ this separation method, we need to be ableto detect when water tapping starts and stops. If this information is

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}.

available, we can get a very accurate estimate of the tap energyconsumption and the heating energy consumption. One way to getthis information is to monitor the valve that controls the tap waterheat exchanger or the radiator heat exchanger. Many districtheating systems in Sweden have self-acting valves (completelywithout electronics) for the tap water circuit. This makes it difficultto monitor the valve position without additional sensors. However,if the valve is electronically controlled, monitoring is easy. Themethod we propose can be used even when the valves are self-regulating.

The method works as follows. A tapping (start and stop) isdetected by looking at C (1) (the difference between two samples ofthe measured power). If this value is greater than a certainthreshold, or if the power consumption is greater than the powerthat can be produced by the radiator heat exchanger alone, then weclassify it as tapping.

PTOTðt � 1Þ � PTOTðtÞ ¼ C (1)

The total power consumption PTOT(t) = PDHW(t) + PHeat(t) ismeasured by a normal heat meter and then integrated to getQ(t). For a normal household, one can assume that hot waterconsumption is not constant. In reality, the tappings are donerandomly (with respect to start, duration, and flow). Let us assumethat a building’s heat consumption is constant for the duration of atapping unless the tapping is extremely long. We say PTOT(t) isequal to PHeat(t) when no tapping occurs, and PDHW(t) is equal toPTOT(t) � PHeat(t). If no tapping is detected, then PDHW(t) = 0 andPHeat(t) = P(t). The threshold for C is determined by the dynamicproperties of the DHS and varies depending on the size of thederivative (of measured power) when tapping starts and stops; wesay that the derivative is close to 0 for building heating and is largefor tap water heating. Choosing a small value for the thresholdgives a good separation, but a value that is too small will classifyevery change in energy consumption as tapping.

A 10-s long tapping would yield

PTOTð0Þ ¼ PHeatð0Þ

At t = 0 there is no tapping; tapping starts at t = 0+:

PTOTð1Þ ¼ PHeatð0Þ þ Ptapð1ÞPTOTð2Þ ¼ PHeatð1Þ þ Ptapð2Þ...

PTOTð10Þ ¼ PHeatð9Þ þ Ptapð10Þ

Assume that PHeat(t) remains constant for 0 < t < 10 (theduration 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. Away of improving this estimate is to measure PTOT(11) and PTOT(0),make a linearization between the points and use it to approximatethe value of PHeat between PTOT(0) and PTOT(11), since PTOT(11) canbe greater or smaller than PTOT(0).

Heating for tap water consumption in a building varies morerapidly than in a house. A domestic hot water HX needs to be ableto produce hot water on demand if no storage tank is used. Sincethe power consumption for building heating varies relativelyslowly and the power consumption for tap water heating varies

K. Yliniemi et al. / Energy and Buildings 41 (2009) 169–174 171

quickly and has greater magnitude, it is easy to separate the twotypes of events (Fig. 2).

2.1. Estimating how the energy is used in the building

Here we present how energy consumption is estimated forheating and hot water production. We start with measurementsand then describe estimates using the ECOS energy estimationmethod.

The total power consumption in kW, given in (2), is made up ofpower consumption from the radiators (PRad(t)), domestic hotwater PDHW(t) production, and distribution losses (PLoss(t)) insidethe building:

PTOTðtÞ ¼ PRadðtÞ þ PDHWðtÞ þ PLossðtÞ (2)

The building’s energy consumption in kWh is divided into thesame categories (3):

ETOTðtÞ ¼ ERadðtÞ þ EDHWðtÞ þ ELossðtÞ (3)

Assuming that all of the distribution losses contributeto building heating, the expressions for heating become (4)and (5):

PHeat ¼ PRadðtÞ þ PLossðtÞ (4)

EHeat ¼ ERadðtÞ þ ELossðtÞ (5)

Combining (4) and (2) we get (6):

PTOT ¼ PHeatðtÞ þ PDHWðtÞ (6)

The total energy consumption then simplifies to (7), which onlycontains building heating and DHW energy consumption:

ETOT ¼ EHeatðtÞ þ EDHWðtÞ (7)

The energy consumption for heating estimated using ECOS (8):

EHeatðtÞ ¼ ECOSHeatðPTOTðtÞÞ (8)

The energy consumption for DHW production estimated byECOS (9):

EDHWðtÞ ¼ ECOSDHWðPTOTðtÞÞ (9)

Fig. 2. The threshold used for separation of hot water energy consumption.

3. Comparison of estimates against experimentalmeasurements

Estimates of energy consumption for DHW performed using theECOS estimation method were compared to the measurements ofenergy consumption for domestic hot water production.

The measurements were performed on a 2-stage coupleddistrict heating substation.

3.1. Substation

Older substations are usually of the two stage type, whilenewer installations are usually of the parallel type. The differenttypes of substations (3-stage, 2-stage, and parallel) are describedin detail in [11–13]. Energy measurement in district heatingsubstations is covered in [14,15]. In Sweden, 2-stage substationsare very common in apartment buildings and other largebuildings. For this reason, the experiment was conducted on thistype of substation. Thanks to cooperation with HSB (a localapartment building owner), a 2-stage substation fulfilling ourrequirements was made available for the experiment. Thesubstation supplies heating and hot water to a small apartmentbuilding with 40 apartments.

Table 2 contains the technical specifications of the heatexchangers in the 2-stage coupled district heating substation usedfor our experiments. The substation components were manufac-tured in 1998–1999 and installed in the building in 1999. The heatexchangers are of the tube type, manufactured by Cetetherm AB. Thesubstation is equipped with a control system from Siemens.

3.2. Measurement equipment

Table 3 presents the measurement equipment used. The D-flowultrasonic flow meters have built-in temperature sensors. All thesensors were connected to the measurement PC. The PC was in turnconnected to the internet for easy access of the measurement data.The measurement equipment in Table 3 has the same or betterperformance as class 3 energy meters, according to the SS-EN1434energy measurement standard. All sensors were calibrated by thesupplier.

3.3. Uncertainty in the measurements

We analysed the uncertainty in the measurements based on theperformance of the class 3 energy meters as given by the standardEN1434:1 [14]. The method for calculating the combineduncertainty was taken from the book by Coleman and Steele[16]. To measure the total energy consumption ETOT(t), using oneenergy meter for heating EHeat(t) and one for domestic hot water

Table 2Substation heat exchanger data

Technical data Heat exchanger data

DHW heat exchanger Cetetube

Temperature in Primary 65 8C, secondary 10 8CTemperature out Primary 24 8C, secondary 55 8CFlow Primary 1.1 l/s, secondary 1 l/s

Maximum pressure drop 100 kPa

Capacity 186 kW

RAD heat exchanger Cetetube

Temperature in Primary 100 8C, secondary 45 8CTemperature out Primary 48 8C, secondary 55 8CFlow Primary 1.3 l/s, secondary 6.6 l/s

Maximum pressure drop 100 kPa

Capacity 275 kW

Table 3Measurement equipment used in the experiment (the equipment has class 3 or

better accuracy)

Type Sampling rate (Hz)

D-flow ultrasonic flow meter 1

Seneca Z-4RTD and PT100 1

Kamstrup Multical Energymeter 0.2

PC104 equipped with Linux

K. Yliniemi et al. / Energy and Buildings 41 (2009) 169–174172

production EDHW(t), we used the following expression:

ETOTðtÞ ¼ EHeatðtÞ þ EDHWðtÞ (10)

The combined uncertainty for the measurement is given by

u2c ¼ u2

Heat þ u2DHW (11)

If we measure ETOT(t) and EDHW(t), we can get the energyconsumption for heating using (12).:

EHeatðtÞ ¼ ETOTðtÞ � EDHWðtÞ (12)

The combined uncertainty for estimating EHeat(t) this waybecomes

u2c ¼ u2

TOT þ u2DHW (13)

The calculated uncertainties are given in Table 4 for both lowand high flows. We can see that the combined uncertainty is morethan �14% in the worst case when both meters experience low flow.The combined uncertainty is �11% if one of the meters measures lowflow rates and the other measures high flow rates. This indicates thatmeasurement is not always the best option.

The effect of sampling time was also studied in order toquantify dependence of uncertainty on sampling time. A period of5.5 h with random tapping was sampled in intervals ranging from1 to 60 s. The uncertainty in measuring energy consumption forheating using a sampling time of 60 s was less than �0.5%. Theuncertainty in measuring domestic hot water energy consumptiongreatly depends on the length of tapping. We consider the case of 15 stapping starting at random times: a 30 s sampling time would onaverage miss half of the tapping; and a 20 s tapping would bedetected 2/3 of the time, meaning the energy consumption wouldbe underestimated by 30%. As the tapping length increases, theuncertainty decreases. This is consistent with the sampling theorem,

Table 4Measurement uncertainty for class 3 energy meters and flow meters according to

EN1434

Value (%) Uncertainty variable Flow and delta T

Class 3 energy meters

�10 uHeat Low

�10 uDHW Low

�10 uTOT Low

�4 uHeat High

�4 uDHW High

�4 uTOT High

�6 uc High/high

�14 uc Low/low

�11 uc Low/high

Value (%) Uncertainty variable Flow

Class 3 flow meters

�5 uflow Low flow

�2 uflow 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.

which states that the sampling frequency should be twice as high asthe frequency of interest.

During the measurement period, delta T was high and theflow varied between low and high rates. The total flow was highwhen there was tapping. This period in particular has manytapping events. This means that we can consider the total energymeasurement to have an uncertainty close to �4%. The measuredenergy consumption for heating had a low flow and a high delta T,giving it an uncertainty of �7–8%. The combined uncertainty for thetest period is estimated to be �6–11% for both heating and domestichot water production; see Table 4.

3.4. Evaluating the algorithm

The algorithm is tested in three steps:

1. D

TaTh

Va

�1

�8

ata acquisition and logging to computer. The measuredvariables are total power and energy consumption, and powerand energy consumption for hot water production.

2. E

stimation of energy consumption using the algorithm. Theenergy consumption and the corresponding power consumptionwere estimated using measurements. Estimation was done off-line using measurements collected in Step 1. The data werecollected over several months; occasionally, we had data dropouts because of bad communication. For the estimation, datawas checked for communication drop-outs, and we did not usesuch baddata in the evaluation.

3. C

omparison of measured and estimated results. The estimatedvalues for the energy consumption and the correspondingpower consumption were compared with the measured values.

3.5. Test conditions

We used two different consumption scenarios for our experi-ments. These were

� W

inter time

A very dynamic load consisting of both tapping and adecreasing space-heating demand. A decreasing need for heatingcreates disturbances, since the algorithm is made to act slowly onchanges in heat demand. An 8-h period is studied here.

� S ummer time

No space-heating demand. The losses in the domestic hotwater circuit heat the building. The estimation method calculatesthe energy losses in the domestic hot water circulation as part ofbuilding heating. The measurements show that the uncertaintyin estimating the energy consumption for hot water productionis similar to that in the winter case.

4. Results

4.1. Uncertainty estimation

The standard deviation for the 8-h test period is given inTable 5. The uncertainties are calculated using methods from [16].Since the ECOS algorithm has start-up behaviour, we discarded astart-up period of 1.5 h in the calculations, thus improving the

ble 5e relative estimation uncertainty in percent

lue (%) Uncertainty variable Type

.6 uHeat 2 � sHeat

.6 uDHW 2 � sDHW

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%.

K. Yliniemi et al. / Energy and Buildings 41 (2009) 169–174 173

uncertainty estimation as shown in Figs. 3 and 4. The relativeuncertainty in percent for the estimates using ECOS is givenby (14) and (15):

uDHW ¼EDHWðtÞ � EDHWðtÞ

EDHWðtÞ� 100 (14)

uHeat ¼EHeatðtÞ � EHeatðtÞ

EHeatðtÞ� 100 (15)

The data shown here are considered to be representative of thedata obtained in the field test.

In Fig. 3 the estimated power consumption for DHW is plottedfor an 8-h period. For this period, the total energy consumption forhot water is 100 kWh, and the estimated total energy consumptionfor hot water is 97.5 kWh. Fig. 4 gives the relative uncertainty forboth the estimated hot water consumption and the estimatedspace heating consumption. They are for obvious reasons oppositesof each other.

The ECOS algorithm goes through a start-up behaviour. Thus,the first hour could be considered the start-up time, a notionconfirmed by the relatively large uncertainty shown there. Thiswas caused by a small accumulation of energy, and thus therelative uncertainty was increased.

5. Discussion

The measured total energy consumption did not increase ordecrease with the estimation method. This means that if theinstalled meter did not measure all of the energy consumption (i.e.,if it missed hot water tappings), the estimation method could notestimate this energy.

In Figs. 3 and 4, we see that ECOS overestimated the energyconsumption for DHW production. Between hours 1 and 3, theheating need dropped quickly and resulted in an overestimation ofthe heating requirement. Thus, the energy consumption for hotwater production was underestimated. After 4 h, the heating needincreased (in the evening) and the heat requirement was under-estimated; thus, energy consumption for hot water production wasslightly overestimated. When the heating requirement did notchange during a tapping, the estimation uncertainty for tappingwas lower.

Uncertainty analysis of the measurements showed that theuncertainty can vary between �4–10% when using class 3 energymeters, according to the European measurement standard EN1434, ifone energy meter measures the total energy consumption andanother measures heating. We estimated the energy consumption forhot water production by deducting heating from the total. By doing

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.

so, we obtained a combined uncertainty that varies between 6% and14% depending on the flow and temperature difference. Theuncertainty in the DHW estimate for energy consumption for in thistest was below 10%.

It is not always possible to install energy meters. The cost of themeter,installation and space requirement have to be considered.Especially for smaller (compact) substations, such as those used indomestic homes, the space requirement is critical. Installation ofadditional sensors may require extensive re-piping, as they mustbe built as compactly as possible, with the fewest ‘‘straights’’necessary to ensuring proper flow measurements [17,18]. There-fore, we believe that estimation is a very good option in manycases, considering both accuracy and cost effectiveness.

In general, we conclude that

a. T

he study indicates that the estimation uncertainty of ECOS is�10%, which is less than the combined uncertainty for the twoenergy meters.

b. T

he estimation approach has the potential to be a useful tool forestimating energy consumption in buildings.

c. T

he estimation uncertainty is comparable to that for class 3energy meters.

d. Im

plementing ECOS in energy meters lowers the cost comparedto measuring hot water production using an additional energymeter.

Future work will include experimental verification of the ECOSalgorithm for parallel coupled substations.

Acknowledgements

We wish to express our gratitude to Svensk Fjarrvarme forfunding this research and to HSB Norr for letting us use theirbuilding and Internet connection for the field test. A special thanksto Hans Engstrom for his help and Lulea Energy for funding theinstallation of the measurement equipment.

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