Advanced Engineering Informatics 25 (2011) 688–698

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Advanced Engineering Informatics

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Virtual sensors for estimation of energy consumption and thermal comfortin buildings with underfloor heating

Joern Ploennigs a,⇑, Ammar Ahmed b,1, Burkhard Hensel a,2, Paul Stack b,1, Karsten Menzel b,3

a Dresden University of Technology, Institute of Applied Computer Science, D-01062 Dresden, Germanyb University College Cork, Department of Civil and Environmental Engineering, College Road, Ireland

a r t i c l e i n f o a b s t r a c t

Article history:Available online 17 August 2011

Keywords:Energy efficiencyBuilding performance analysisVirtual sensorsHybrid HVAC systems

1474-0346/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.aei.2011.07.004

⇑ Corresponding author. Tel.: +49 351 463 38066; fE-mail addresses: Joern.Ploennigs@tu-dresden.de

student.ucc.ie (A. Ahmed), pstack@student.ucc.ie(K. Menzel).

1 Tel.: +353 21 420 5453; fax: +353 21 427 6648.2 Tel.: +49 351 463 38376; fax: +49 351 463 38460.3 Tel.: +353 21 490 2523; fax: +353 21 427 6648.

Evaluating a building’s performance usually requires a high number of sensors especially if individualrooms are analyzed. This paper introduces a simple and scalable model-based virtual sensor that allowsanalysis of a buildings’ heat consumption down to room level using mainly simple temperature sensors.The approach is demonstrated with different sensor models for a case study of a building that contains ahybrid HVAC system and uses fossil and renewable energy-sources. The results show that, even with sim-ple sensor models, reasonable estimations of rooms’ heat consumption are possible and that rooms withhigh heat consumption are identified. Further, the paper illustrates how virtual sensors for thermal com-fort can support the decision making to identify the best ways to optimize building system efficiencywhile reducing the building monitoring cost.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Buildings contribute to 42% of western countries energyconsumption [1,2] and it is important to reduce their energy con-sumption and their related carbon foot-print. A building’s en-ergy-efficiency depends on two aspects [3]. First, the design of abuilding defines its minimal reachable carbon foot-print. It canbe improved by design changes such as adding insulation or mak-ing use of renewable energy sources. Second, it is the building’soperation and maintenance that defines how this potential is uti-lized. Several studies demonstrated that the energy consumptionof identical houses may vary by more than a factor of two depend-ing on the occupants’ behavior and the buildings’ operation [3,4].

Optimal building operation requires identifying the best trade-off between its operating costs, occupant comfort and its energy-efficiency [5,6]. Most important for a company managing a buildingare the operational costs such as costs for heating, cooling, hot water,electricity, and maintenance. Next comes occupant comfort, whichneeds to be provided at a sufficient level. Least important is oftenthe building’s energy efficiency that targets a minimal energy con-sumption and carbon foot-print. These three objectives are not nec-essarily related, i.e. it might be cheaper to heat a building over night

ll rights reserved.

ax: +49 351 463 38460.(J. Ploennigs), a.ahmed@

(P. Stack), k.menzel@ucc.ie

at low energy prices to avoid heating during the day, even if de-mand-oriented heating during the day would involve less heattransfer losses and provide more constant comfort to users.

The first step in optimizing a building’s operational consump-tion is a continuous monitoring of the building’s consumptionand conditions. This provides the necessary data for building per-formance analysis, continuous commissioning, optimizing theoperation and users guidance [5,7,8]. The large influences of occu-pant behavior and room usage show the need to analyze a build-ing’s energy consumption down to consumers at room level, inorder to identify energy leaks and optimize the building’s perfor-mance. New functional buildings are usually fitted with largebuilding automation systems (BAS) that contain several hundredsof devices controlled by a central building management system(BMS) [9]. The market is currently undergoing a change especiallyin central Europe and most construction contracts focus nowadayson retrofitting existing buildings instead of new construction.Wireless sensor networks are ideally fitted for this market as theyallow for easy installation without design changes [10]. This bene-fit in installation costs is often compensated by higher device costs[11].

Analyzing a building’s energy consumption down to room levelrequires a large amount of measurement equipment, such as sen-sors and meters, in each single room. Therefore, detailed buildingperformance analysis is often hampered by high monitoring cost,and practitioners often request alternative cost-efficient methods[12,13].

Virtual sensor and actuator approaches can reduce equipmentrequirements. They use a mathematical model of the process to

UnderfloorHeating

Manifolds

P-12

CoolingCircuits

SolarThermalArray

Gas-firedBoiler

HeatPump

MeterHPM

Meter FHM

HotWater

Water from culvert to river

UnderfloorHeatingPump TIN

Tout

Fig. 1. High level schematic of the mechanical system for the ERI.

J. Ploennigs et al. / Advanced Engineering Informatics 25 (2011) 688–698 689

estimate a projected sensor value from other measurements, orconvert a virtual control value into another actuator command. Vir-tual sensor is an established term in process modeling and controlfor a long time [14,15]. Virtual sensors are usually used when thetargeted monitoring or control value is not directly or only expen-sively measureable (e.g. hostile environments), or only measure-able with large delays (e.g. dead-time processes).

This paper develops an estimation algorithm for a virtual sensorin Section 3 to analyze a building’s energy consumption down toroom level. The algorithm is adjustable in its information intaketo different sensing and metering equipment available in a build-ing. Therefore, the algorithm offers a flexible usage with rough esti-mations using few sensors to detailed computations using a highdensity sensing deployment. The algorithm can be used for offlinedata analysis and for online monitoring to estimate the heat flowsin rooms. Section 4 compares different algorithm variances anddiscusses their outcome for a real building. The archived user com-fort level is also compared, such that a broad set of performancemetrics is provided to support building performance analysis,monitoring, and optimization.

The approach is validated using the Environmental ResearchInstitute (ERI) building, located on the campus of the UniversityCollege Cork, as an existing low-energy building with a hybridHVAC (heating ventilation air conditioning) system that is intro-duced in the next section.

2. The Environmental Research Institute building

Environmentally-friendly buildings combine concepts for en-ergy-efficient buildings with the usage of renewable energysources to create the energy needed for heating, cooling or domes-tic hot water [16]. This enables the buildings to operate energy-efficiently and aids in the efforts to preserve a clean environment.In Europe such buildings first need to meet the energy-efficiencystandards implementing the European Union’s EPBD (Energy Per-formance of Buildings Directive) [17]. Additionally, environmen-tally-friendly buildings utilize renewable energy resources [16,18]to minimize the usage of fossil fuels and related air pollution.Renewable energy can be regenerated by natural conservation pro-cesses [19] from wind, sunlight, and geothermal heat. These re-sources are often not always available, e.g. when there isinsufficient sunlight for solar collectors on a cloudy winter day.Therefore, regenerative systems are usually backed-up by conven-tional units such as gas boilers, which results in hybrid systems.

However, conventional back-up systems also reduce the building’senvironmentally-friendliness as they burn fossil fuels.

The Environmental Research Institute (ERI) is a three-storey,4500 m2 low-energy building. The building is used as ‘‘Living Lab-oratory’’ by the Informatics Research Unit in Sustainable Engineer-ing (IRUSE) of the University College Cork and the Irish strategicresearch cluster ITOBO [20] to serve as a full-scale test bed forIntelligent Buildings demonstrating building performance concepts[21].

The building is equipped with a wireless sensor network ofabout 100 devices and a wired Building Management System(BMS) consisting of about 180 sensors and meters that monitor in-door and outdoor conditions. The ERI covers various HVAC require-ments for laboratories, clean rooms, cold stores, offices, openoffices, and seminar rooms used by multiple research groups frombiologists, chemists, engineers, and computer scientists.

The building is heated by an underfloor heating system that isprimarily supplied by a geothermal heat pump that taps into awater supply fed from a culvert running adjacent to a nearby river.The water is preheated by heat recovered from the cold stores andheat generated by the solar thermal array. The underfloor heatingoperates at a maximum temperature of 40 �C. A condensing gasboiler is sized to act as a complete back-up system. The hot wateris provided by an 84 m2 solar thermal collector array, while therest of the requirement is provided by the boiler. See Fig. 1 for aschematic of the mechanical system. The heat pump and boilerare separately controlled. Both have timetable dependent set-points to schedule their activity. They ensure that heating anddomestic hot water are provided over office hours.

3. Estimating the room heat consumption

3.1. Assumptions about the room heat consumption and discussion

To identify rooms with unusual high heat usage the room’s heatconsumption needs to be analyzed. But, instead of installing heatmeters in each room to measure the individual heat consumption,the room’s heat intake is estimated from the overall heat consump-tion of the underfloor heating system by creating a virtual sensormodel using knowledge about the room heating controls.

In the case of the ERI, each room has an individual temperatureclosed loop control consisting of a temperature sensor, a controllerand one to four on/off-valves operating separate underfloor heat-ing circuits. The temperature values are logged by the BMS and

690 J. Ploennigs et al. / Advanced Engineering Informatics 25 (2011) 688–698

exported to a data warehouse for analysis [22]. However, exactinformation about the control values at the valves is not availablein the BMS system. This is not uncommon as often only importantmeasurements at time of deployment are logged, but it is the con-trol values that define the room’s and building’s heat consumption.The closed loop controls of the room’s air temperature are acti-vated at all times in the ERI; but, the central heating system pro-vides heat only at specific times.

For comparing rooms’ consumption, a thermal room model isneeded. Such models are typically based on conservation of energy[23–26]. That means that the change of the heat _Q store stored in theroom’s air and envelopes equals the difference between the heatflow brought into the room by heating _Q in, the heat _Q exch ex-changed with neighbor rooms, and the heat which leaves the roomthrough its envelope _Qout.

_Q in þ _Qexch � _Q out ¼ _Q store: ð1Þ

Several heat emitters account for the heat consumption _Q in such asheating, sun light, persons, and equipment such as computers. Theheat losses _Qout are mainly losses via the room’s envelope such asthe walls, windows, ceilings, and floors. The heat exchanged withneighboring rooms _Q exch depends on the size and heat transfer coef-ficients of the walls and the temperature difference of both rooms.As the building is naturally ventilated there is also a consistent heattransfer through air exchange. The capacity of storable heat _Q store inthe room’s air, walls, and equipment depends mainly on the mate-rial used, especially if high capacitive materials such as PhaseChange Materials [27] are used.

Due to the high number of influences on the room heat, the de-tailed computation of the room heat would now require a complexsimulation model [28], that models the rooms not only individu-ally, but considers heat exchange with adjacent rooms down tomodeling the air flow and the heating system on computationalfluid dynamics (CFD) level.

Simulation tools which can be used for such an analysis arebroadly available and an overview can be found in [29]. Basically,it is possible to create virtual sensor models from such simulations.This is usually done by training Artificial Neural Networks (ANN) tocreate abstracted predictors from the simulation results [30,31].Unfortunately, the effort is very high to create the simulation mod-els manually, parameterize and run them in various configurationsto train the ANN models. This is not efficient for practice,especially, since much information about the system is often un-known, beginning with the pipe layout to the materials used forwalls, floor, pipes, and windows. The situation may change in fu-ture with the increasing support of the simulators for the importof available design information from Building Information Models(BIMs) [26,32,33]. BIMs are designed as exchange format for thedesign information relevant to a building’s life cycle. BIMs are cur-rently gaining momentum as standard exchange format for newbuildings, but they are often not available for older buildings[34–36]. As long as BIMs are not yet broadly available, the usageof simulations to create virtual sensor models requires too mucheffort. Virtual sensor models that are created from data mining[37,38] or self-train from online data are easier to [39,40], but re-quire some reference measures for modeling.

The approach followed in this paper is to create virtual sensormodels from statistical calculation models. Statistical models aresimplified models used in building design for estimating the en-ergy demand or for dimensioning components such as the under-floor heating system [41]. The approach, therefore, is related toapproaches for building performance assessment [26,42] with thedifferent goal of creating a virtual sensor model to estimate aroom’s heat consumption. The simple virtual sensor model is cre-

ated based on simplification of the building physics using theassumptions:

(A1) The heat metered centrally is consumed by the rooms andnot lost to other reasons such as leaky pipes.(A2) The rooms consume heat if a positive temperature differ-ence exists between the heating system medium and the room’sair temperature.(A3) The rooms consume heat only if the valves are open and awater flow exists.

Based on these assumptions, the room’s heat consumption _Q in

can be estimated in a simplified way, without modeling in detailthe whole building and heating system. Various effects are ne-glected that mainly define the balance between _Qout and _Q store.

A heating system can never be ideally isolated so that the con-tained hot water will always loose heat on its way through thepipes. Assumption A1 results, therefore, in incorrect estimations,as heat is also lost to rooms that are passed by supply pipes suchas service rooms in the basement. This loss is negligible as supplypipes are usually well isolated nowadays.

Assumption A2 leads to an important simplification as it allowsneglecting the heat storage in and heat exchange between rooms.Heat storage basically results in a delayed behavior between heatconsumption and heat loss that continually occurs. Based onassumption A2, it does not matter when the heat is lost, but whenit is consumed by the room. The ERI building is a good example toillustrate this difference, as it is mainly heated over night and usesthe storage effect over the day. The heat is produced in early morn-ing hours and stored in the building’s concrete mass. The buildingwill release the stored heat over the day which results in a moreslowly degrading room temperature on a cold day. The large timedifference between heat consumption at night and the heat lossduring the day is now neglected, assuming that the room that con-sumed the heat also releases it.

This has relevance in conjunction with the heat exchange be-tween rooms, which is also neglected focusing again on the heatconsumption of a room, not its heat loss. A room that is centrallylocated in a building without exterior walls gives a good exampleto point out the difference. As the central room has no exteriorwalls it has basically no heat loss to the outside, but it still con-sumes heat that can only be released via heat exchange with theneighboring rooms. Assumption A2 considers this heat exchangeindirectly as this results in a higher room temperature of the adja-cent rooms, while again storage and time delay effects areneglected.

Also, for the same reason, other heat sources, besides the heat-ing, such as persons, sun light, and equipment can be neglectedbased on assumption A2. If they exist they influence the room tem-perature and are indirectly considered.

Assumption A3 simplifies effects in the heating system such asthat the room may still be heated after closing the valves until thewater in the pipe has cooled down to room temperature. This,however, is a storage behavior and neglected as discussed for A2.

3.2. Estimating the room heat consumption

Based on the assumptions, a room’s heat consumption is esti-mated, which does not necessarily have to equal the room’s heatloss. Let _QFH be defined as the absolute heat flow of the underfloorheating system that is centrally measured by a heat meter con-nected to the underfloor heating system’s inlet and outlet. Thisis, for example, the heat meter FH in Fig. 1 in the case of the ERI.The heat flow is computed in the heat meter from the mass flowrate _mFH in the pipes, the specific heat capacity of the water cw

and the temperature difference of the inlet and outlet of the

J. Ploennigs et al. / Advanced Engineering Informatics 25 (2011) 688–698 691

underfloor circuits as _QFH ¼ _mFHcwðT in; FH � Tout; FHÞ. The mass flowrate _mFH is controlled in the case of the ERI by the central under-floor heating pump in Fig. 1. The temperature difference dependson the heat consumption (flow) of the rooms. Let _Q r be the heatconsumption of any room r in the set of rooms R connected to theheating system. Based on A1 the underfloor heating system hasno further losses beside in the rooms and the overall heat flow_Q FHðkÞ in time step k 2 Nþ0 is the sum of all room’s heat flows_Q rðkÞ and each room takes a percentaged share pr ¼ _Qr= _QFH in the

overall consumption, thus

_Q FHðkÞ ¼ _mFHðkÞcwðT in; FHðkÞ � Tout; FHðkÞÞ �Xr2R

_Q rðkÞ

¼Xr2R

prðkÞ _Q FHðkÞ: ð2Þ

Due to assumptions A3, a room’s heat consumption depends on thevalve opening 0 6 v r 6 100%. This is insofar correct, as the valveopening limits the individual mass flows _mr in the underflow cir-cuits of each room. Hence, if the valves in a room are closed(v r = 0%) then mass flow and heat consumption will be zero, as_Qr ¼ _mrcwðT in; r � Tout; rÞ. If the valves are open (v r > 0%) and a flow_mr > 0 exists, then the room may consume heat. The issue is that the

precise mass flow _mr and the individual temperatures T in; r; Tout; r ofeach room are unknown. As a result, the share pr of a room in thebuilding’s heat consumption is not computable via the first partof Eq. (2).

Therefore, it is assumed that for each room a relative heatingcoefficient4 Cr can be defined, which can be interpreted as an esti-mation of the room’s heating capacity in relation to other roomsfor the case that the valves in all rooms are open. For example, thiscan be the sizes of the room’s underfloor heating circuits. Let theactual heating coefficient CFHðkÞ of a building be the sum of allroom’s coefficients linearly depending on their valve openingv rðkÞ so that

CFHðkÞ ¼Xr2R

v rðkÞ � CrðkÞ ð3Þ

CFHðkÞ can then be interpreted as a measurement of the building’sactual active heating capacity. For example, if Cr represents theroom’s underfloor heating circuit’s size, then CFHðkÞ is the currentsize of active circuits in time step k. As Cr is defined as the room’sheating capacity in relation to other rooms, Eq. (3) also allows esti-mation of the percentaged share pr of a room in the overall heatflow from Eq. (2) so that

_Q rðkÞ_Q FHðkÞ

¼ prðkÞ �v rðkÞ � CrðkÞ

CFHðkÞ: ð4Þ

This equation allows the estimation of the room heat flow from therelative heating capacity of a room. For example, this will mean thata room utilizes the same proportion of the current heat flow as itsheating circuit size is in relation to the building’s active circuitsCFHðkÞ in the abovementioned example of using the underfloor heat-ing circuit size. In addition, it easily scales to different levels ofavailable knowledge by modeling v rðkÞ and CrðkÞ with availableknowledge as demonstrated in the next sections.

Summarizing, a building’s room heating consumption can beestimated in each time step k 2 Nþ0 by the following algorithm:

(1) Compute the valve opening v rðkÞ for each room r 2 R based

4 The name relative heating coefficient was chosen to emphasize that it is acoefficient with a relative association. It is related to the room’s heating capacity, budoes not have to represent its real value. It will later on be defined by differenmeasurements and different units, which is not an issue as it is used as coefficient. Iis named relative because it only has a valid meaning in relation to the other room’scoefficients.

ttt

on the control value urðkÞ.(2) Compute the building’s actual heating coefficient CFHðkÞwith

Eq. (3) from v rðkÞ.(3) Estimate the room’s heat flow _QrðkÞ for each room r 2 R via

Eq. (4) from v rðkÞ, and CFHðkÞ.

3.3. Restore the control value

The valve opening v r depends on the control values ur computedby the room temperature controller. If a detailed model is neces-sary then the specific characteristic curves of the valves can beused. In the case of the ERI, simple on/off-valves are used and itcan be simplified that v r ¼ ur 2 f0;1g.

The proposed algorithm uses the control value ur for eachroom’s individual heating control. As information about the controlvalue is often not available, like in the investigated ERI case, thecontrol value ur can also be computed by remodeling the controlalgorithm. In the case of the ERI, the valves are individually con-trolled for each room by a bounce control with a hysteresis hrðkÞof 0.3 K around a temperature set-point wrðkÞ. This means, that ifthe temperature TrðkÞ in a room drops below ðwrðkÞ � hrðkÞÞ thevalves are opened ðv r ¼ ur ¼ 1Þ and the room is heated until theroom temperature reaches ðwrðkÞ þ hrðkÞÞ at which point the valvesare closed ðv r ¼ ur ¼ 0Þ. Thus,

urðkÞ ¼

1; TrðkÞ 6 wrðkÞ � hrk;

0; TrðkÞP wrðkÞ þ hrk;

urðk� 1Þ; otherwise:

8>><>>:

ð5Þ

If more advanced PID controllers (Proportional, Integral, Derivative)are used, the control value can be recomputed in a comparable wayif the controller parameters are known. If the set-point wrðkÞ is notknown, it can be partly reconstructed from the step response of theroom temperature [43] if the quality of control is good enough.

3.4. Computing the relative heating coefficient

With a detailed model of a building’s heating system, the indi-vidual consumption of one room can be computed quite precisely.The main reason for defining a relative heating coefficient Cr andthe rather simple relative equation (4) is that such detailed mod-els require a lot of specific information about the heating systemand building beginning with the layout and types for pipes,pumps, and valves, to the materials used for walls, floor, and win-dows. Unfortunately, in practice such information gets often lostafter a few years of operation or can only be extracted laboriouslyfrom outdated documentation. BIMs are opening new perspec-tives in this regard, but are not yet available for all buildings asdiscussed above. The relative heating coefficient Cr can thereforebe individually adapted to the available information and allowsmany options for simplifications. The next subsections introduceseveral coefficients that will be later compared in Section 4 forthe ERI building.

3.4.1. Relative heating coefficient based on the room sizeA simple estimation is to use the room size Ar as an indicator of

the relative heating capacity. The estimation is based on Newton’slaw of cooling, which states that the convective heat transfer _Q �RS;r

is computed for any pair of heat exchanging systems from: theirtemperature difference, the size Ar of the shared surface, and theheat transfer coefficient hr of the surface. Assuming that eachroom’s floor is used completely for heating, the convective heattransfer computes to

_Q �RS;rðkÞ ¼ hrArðTW; rðkÞ � TrðkÞÞ ð6Þ

692 J. Ploennigs et al. / Advanced Engineering Informatics 25 (2011) 688–698

with a mean water temperature TW;rðkÞ ¼ 1=2ðT in; FHðkÞþTout; FHðkÞÞ.

This is an idealized model that neglects many effects of a floorheating system [44] and the computed heat transfer _Q �RS;rðkÞ maynot be equal to the actual heat transfer _Q rðkÞ. Nonetheless, itshould provide an estimation of the relative heating coefficientCrðkÞ. The relative heating coefficient is defined for the case thatthe valves in all rooms are open, such that vrðkÞ ¼ 1 for all rooms.Then, Eq. (4) would only hold if the relative heating coefficient isproportional to the computed heat transfer, i.e.

CRS;rðkÞ � _Q �RS;rðkÞ: ð7Þ

The heat transfer coefficient hr of an underfloor heating system de-pends on its configuration, the pipe radii, materials and floor mate-rials. It may be provided by the system integrator or can becomputed or measured for an underfloor heating system using stan-dards such as EN 1264 [41]. But this requires already detailed infor-mation that is not always available. However, if it is assumed thatall rooms in the building have the same heating system configura-tion with identical heat transfer coefficient hr , then hr can be fac-tored out in Eq. (3) and eliminated in Eq. (4), such that it also canbe removed from CRS;rðkÞ without loss of generality as CRS;r only de-fines a relative coefficient in comparison to other rooms

CRS;rðkÞ ¼def hrArðTW; rðkÞ � TrðkÞÞ;

or; if hr is identical for all rooms;

CRS;rðkÞ ¼def ArðTW; rðkÞ � TrðkÞÞ:

ð8Þ

Beside the room size Ar , other indicators also may be used to define(more precisely) the active shared surface size such as the valvenumber, the heating system size or the flow rate as discussed inthe next subsections.

3.4.2. Relative heating coefficient based on the valve numberUsing the room size to define the surface used for heat transfer

assumes that the underfloor heating system uses relatively thesame amount of surface in each room. But, an underfloor heatingsystem does not always use all the surface of a room’s floor andif radiators are used instead of an underfloor heating system thentheir size has not necessarily a direct relation to the room size. Thenumber of valves VNr per room might then provide a better esti-mation. Further, it is usually easily countable for existing buildings.Using this coefficient assumes that the heating system size andflow rate is about the same for each valve.

CVN;rðkÞ ¼def VNrðTW; rðkÞ � TrðkÞÞ: ð9Þ

3.4.3. Relative heating coefficient based on the heating system sizeThe actual size SAr of the heating system should provide a more

precise estimation than the valve number or room size. The size ofthe heating system now can be variously measured: (1) by the ac-tive surface size of the heating system like a radiator; (2) by thecontained water volume in liter (assuming identical pipe radii);or (3) the tube length in meter (same assumption).

CS;rðkÞ ¼def SArðTW; rðkÞ � TrðkÞÞ ð10Þ

Fig. 2. Schematic of the underfloor heating system.

3.4.4. Relative heating coefficient based on the design sizeThe EN 1264 [41] defines some models to estimate the heating

performance of an underfloor heating system which are used to de-sign the system’s capacity. The physical behavior of an underfloorheating system is not exactly as simple as assumed by Eq. (6) dueto logarithmic temperature gradients in pipes. The EN 1264 definesa more complex model commonly used for heat exchangers. It isbased upon the log mean temperature difference TLMTD between

the pipe system and the room. It is computed from the inlet andreturn temperature in the pipes as well as the air temperature ofa room. Based upon the EN 1264, the heat consumption of anunderfloor heating system can then be estimated to

_Q �r ðkÞ ¼ ArUrTLMTDðkÞ ¼ ArUrT in; rðkÞ � Tout; rðkÞ

ln Tin; rðkÞ�TrðkÞTout; rðkÞ�TrðkÞ

: ð11Þ

We assumed that the inlet and return temperature of the room’s areequal to the temperatures measured centrally ðT in; rðkÞ � T in; FHðkÞ;Tout; rðkÞ � Tout; FHðkÞÞ. The overall heat transfer coefficient Ur is de-fined by the system properties of the heating pipes and the floorstructure for various systems in the EN 1264 [41]. If the buildingcontains the same heating system configuration in all rooms, theoverall heat transfer coefficient Ur can be neglected in the compu-tation of the relative heating coefficient as in Eq. (8), such that

CD;rðkÞ ¼def ArUr

T in; FHðkÞ�Tout; FHðkÞ

lnTin; FH ðkÞ�Tr ðkÞ

Tout; FHðkÞ�Tr ðkÞ

;

or; if Ur is identical for all rooms;

CD;rðkÞ ¼def Ar

Tin; FHðkÞ�Tout; FHðkÞ

lnTin; FHðkÞ�Tr ðkÞ

Tout; FHðkÞ�Tr ðkÞ

:

ð12Þ

3.4.5. Relative heating coefficient based on the commissioned flow rateThe previous relative heating coefficients assumed that the

heating system was only based on the size of the heating systemand neglected the differences in the water flow of the pipes and,therefore, also the provided heat. It results from the fact that theflows in each room are usually not monitored and simulationsare usually too complex [44]. However, the water flow rates ofan underfloor heating system are usually measured once duringcommissioning (e.g. [45]). If this information is still available thenit can be used to estimate rooms’ real flow rates and heat con-sumption quite precisely.

Basically, a room’s heat consumption can be computed from itsinlet and outlet temperature T in; r , and Tout; r and its flow rate _mr asdone for the central system in Eq. (2)

_QrðkÞ ¼ _mrðkÞcwðT in; rðkÞ � Tout; rðkÞÞ: ð13Þ

An issue is that the flow rate and inlet and outlet temperatures foreach room are unknown. The inlet and outlet temperatures vary foreach room and decrease logarithmically in the direction of flowdepending on the flow rate and the heat losses to the room. Theflow rate further depends on many influences like pump pressure,pipe architecture and which valves are open. Previously, all theseeffects were neglected assuming equal flow rate and water temper-ature for all rooms. However, if the room’s flow rates can be esti-mated then a more precise model is possible.

J. Ploennigs et al. / Advanced Engineering Informatics 25 (2011) 688–698 693

Fig. 2 shows the schematic of the assumed parallel underfloorheating system. It contains a central system with a pump fromwhich the heat pipes of all rooms split in parallel. As the pipe sys-tem contains an incompressible medium and neither leaks norstores with differential behavior, the mass flow in the heating sys-tem needs to be consistent such that

_mFHðkÞ ¼Xr2R

_mrðkÞ: ð14Þ

Each room’s flow rate _mrðkÞ varies at each time step depending onthe open valves and the resulting pressure. Assuming that a refer-ence flow rate _mC;r is known for each room from design or commis-sioning (estimations, simulations or measurements) and that it ismeasured for ideal cases with the room’s valve fully open and allother valves are open or closed, it then allows the estimation of aroom’s flow rate during operation.

Therefore, the parallel system in Fig. 2 is analyzed using theelectric circuit analogy to Hagen–Poiseuille’s law of fluid flow ina cylindrical pipe. The flow rate is computed in the analogy fromthe pressure provided by the central pump PFHðkÞ and the hydraulicresistance RFHðkÞ of the whole system to _mFHðkÞ ¼ PFHðkÞ=RFHðkÞ.The individual hydraulic resistance RrðkÞ of each room dependson two aspects: (1) the hydraulic resistance RH;r of the room’s pipesystem which is assumed to be time-independent and constant;(2) this hydraulic resistance is increased with the valve openingv rðkÞ linear proportional to the flow rate such thatRrðkÞ ¼ RH;r=vrðkÞ. As the system is parallel, the same pressurePFHðkÞ applies to each room and its flow rate is computed as

_mrðkÞ ¼PFHðkÞRrðkÞ

¼ v rðkÞPFHðkÞ

RH;r¼ v rðkÞ _mFHðkÞ

RFHðkÞRH;r

: ð15Þ

The reference flow rate _mC;r is assumed to be measured for the casethat the pump provides a fixed pressure PC;FH, the valve of the roommeasured is fully open ðv r ¼ 1; RV;r ¼ 1Þ and all other valves areclosed. Then the hydraulic resistance RH;r of each room is computedas

RH;r ¼PC;FH

_mC;r: ð16Þ

As the room resistance is assumed to be time-invariant, the hydrau-lic resistance of the building in operation can then be computed forthe given parallel system to

1RFHðkÞ

¼Xr2R

1RrðkÞ ¼

Xr2R

v rðkÞRH;r

ð17Þ

such that each room’s flow rate can be estimated via Eq. (15).The next unknown variables in Eq. (13) are the inlet and outlet

temperatures of each room. From assumption A1, it follows thatthe central system and the pipes leading to or from the room haveno heat loss such that the inlet temperature T in; rðkÞ equals the cen-tral inlet temperature T in; FHðkÞ. In contrast, the central return tem-perature of the system Tout; FHðkÞ is the weighted mean of thereturn temperature per room Tout; rðkÞ weighted by its mass flowsuch that

T in; FHðkÞ ¼ T in; rðkÞ and Tout; FHðkÞ ¼1

_mFHðkÞXr2R

_mrðkÞTout; rðkÞ:

ð18Þ

The return temperature of a room is estimated using the model ofthe underfloor heating system standardized in the EN 1264 [41].Setting Eqs. (13) and (11) equal allows solving the equation to thereturn temperature, which results in

Tout; rðkÞ ¼ TrðkÞ þ ðT in; FHðkÞ � TrðkÞÞe�Ar Ur

cw _mr ðkÞ: ð19Þ

Finally, the room’s heating coefficient is defined equal to the room’sheat consumption estimated in Eq. (13) and estimates to

CF;rðkÞ ¼def _mrðkÞcwðT in; FHðkÞ � TrðkÞÞ 1� e�Ar Ur

cw _mr ðkÞ� �

: ð20Þ

4. Case study for the Environmental Research Institute

4.1. Comparison of the relative heating coefficients

The introduced approach was applied to the ERI building andmeasurements from 3 years were evaluated, including the heatmeter HP at the heating pump, the heat meter FH of the underfloorheating and temperature sensors in 58 rooms. The control valueswere computed from these temperature readings with Eq. (5)using the fixed set-points defined in the BMS which is 20 �C formost rooms.

Based on the measurements, the underfloor heating has anabsolute heat flow of 95.3 MWh for the 3 years. The geothermalheat pump provides 66% of the heat (63.5 MWh) and the remaining33% are backed-up by the gas boiler. Fig. 3 illustrates the meanweekly and monthly heat flows of the heat pump meter HP andunderfloor heat meter FH. It is visible that both systems are activemainly in the early morning hours of the cold month.

Using the algorithm at the end of Section 3.2, the heat flow ofthe individual rooms was estimated. The different introduced rela-tive heating coefficients Cr were computed for comparison. For abetter comparison, the relative heat consumption QRr ¼ R _Qr=Ar

per square meter was computed that is defined as the sum of theroom’s heat consumption _Q r over the investigated three years di-vided by the room’s size Ar .

Table 1 compares the results of the different relative heatingcoefficients CRS;r , CVN;r , CS;r , and CD;r towards the commissioned flowrate coefficient CF;r assuming that this is probably the best estima-tion as it uses the most complex model. The comparison uses dif-ferent quality measures to evaluate the different aspects that arerelevant for analyzing building’s heat consumption:

(1) The mean over all samples of the Euclidian error of the heatconsumption per sample (15 min). It provides a measure-ment of the estimation quality of the heat consumption_Q rðkÞ and should be as low as possible.

(2) The mean error of the estimated relative heat consumptionQRr per room provides a value of how much the estimatedrelative heat consumption differs in mean per room.

(3) To identify rooms in a building with abnormal high heatconsumption, the correct ranking of the rooms sorted bytheir relative heat consumption is probably more importantthan its specific value. The mean error of the ranking of therooms provides a measurement how much both rankingsdiffer.

(4) The top 20% of rooms with the highest energy consumptionare particularly interesting for energy analysis. Therefore, itis also analyzed how many of these rooms, that were identi-fied using CF;r , are also listed in the top 20% returned fromthe other coefficients. The rank of room 1.09, which is iden-tified as biggest consumer using CF;r is also provided for theother coefficients for comparison.

The first conclusion drawn from the comparison is that all coef-ficients have comparable errors to the commissioned flow ratecoefficient CF;r . The mean error of the relative heat consumptionis for all other coefficients ðCRS;r ; CVN;r ; CS;r ; CD;rÞ about 5 kWh/m2.This is about 20% of the average room’s consumption. This ratherlarge mean error results from the large model differences fromthe commissioned flow rate coefficient. However, the ranking of

Fig. 3. Weekly and monthly mean values of the underfloor heating meter FH and heating pump meter HP.

Table 1Comparison of the relative heat coefficients to the flow rate.

Eucl. err. _QrðkÞ in kWh/smp Abs. err. QRr in kWh/year Abs. err. rankðQRrÞ in ranks Top 20% overlap (%) Rank of room 1.09

Valve number ðCVN;rÞ 9.12E�02 5.25 4.34 73 1Room size ðCRS;rÞ 4.15E�02 5.38 4.20 73 2System size ðCS;rÞ 3.76E�02 5.16 3.83 73 4Design size ðCD;rÞ 5.02E�02 5.01 3.61 73 2Flow rate ðCF;rÞ 0 0 0 100 1

694 J. Ploennigs et al. / Advanced Engineering Informatics 25 (2011) 688–698

the rooms differ in mean by about 4 ranks in comparison to the CF;r

ranking, which is acceptable. 73% (8 of 11) of the rooms ranked inthe top 20% do overlap with CF;r . Only the rough coefficient CVN;r

using the valve number estimated room 1.09 correctly as the big-gest consumer. But, the coefficient also has the highest Euclidianerror in estimating the room’s heat consumption per time step.Fig. 4 shows in detail the estimated relative heat consumptionsQRr for the rooms in the top 20%. It is visible that the valve numbercoefficient has also the strongest variance in comparison to thecommissioned flow rate coefficient (4.3E�02 vs. ca. 2.3E�02 forthe other coefficients). The design size coefficient CD;r has the low-est mean error in the relative heat consumption and rank, whichmakes it the second best coefficient after the commissioned flowrate coefficient. Nonetheless, the differences between the four sim-ple coefficients CRS;r , CVN;r , CS;r , and CD;r are small and will dependmostly on the quality of the available information.

It is also possible to combine several relative heating coeffi-cients using a weighted sum of the normalized coefficients. Thecoefficients best suited for a combination are the valve numberwith one of the room size, system size, or design size coefficients.Reason is that the commissioned flow rate coefficient computesfrom the flow rate and the floor heating area and the combinationwill join both aspects. For example, the weighted sum of the roomsize and the number of valves computes to

Fig. 4. Comparison of the relative room heat consumptio

Ccomb;rðkÞ ¼ kCRS;rðKÞCRS;FHðkÞ

þ ð1� kÞ CVN;rðkÞCVN;FHðkÞ

: ð21Þ

The parameter k is the weighting factor, expressing the trustworthi-ness of the room size influence in comparison to the valve numberinfluence. The evaluation of this combination for the ERI case study(k = 0.5) resulted in a slightly improvement of the room size andvalve number coefficient with comparable quality results to the sys-tem size coefficient. The system size coefficient and the designs sizecoefficient could not be improved by a combination with the valvenumber. However, in practice there remains the problem how tochoose the weighting factor k, resulting in confusion of the energyconsultant who wants to apply automatically the methods of thispaper without understanding all details.

4.2. Analysis of the rooms’ heat consumption

Fig. 5 shows for all rooms the estimated heat consumptionbased on the commissioned flow rate coefficient. The rooms weresorted by their heat consumption to illustrate the resulting Paretodistribution often measured in real buildings due to a few roomsusing up the majority of the heat [4]. In the case of the ERI, the11 rooms shown in dark red/gray are the top 20% consumers anduse about 50% of the heat. Fig. 8 illustrates on the left side the

n of the top 20% rooms for relative heat coefficients.

Fig. 5. Comparison of the room heat consumption for all rooms: dark red/gray – top 20% (20% of rooms with the highest heat consumption); orange/gray – top 50% (roomswith more heat consumption than average); light green/gray – lower 50% (less heat consumption than average). (For interpretation of the references to color in this figurelegend, the reader is referred to the web version of this article.)

00.10.20.30.40.50.60.70.80.9

G28

LG

07

G23

G

09

LG21

12

3 G

24

121

G22

10

6 G

04

133

105

124

G07

G

05

122

126

131

G08

G

25

LG35

LG

03

103

108

107

G26

13

0 10

2 10

4 10

1 12

5 G

03

LG05

LG

33

G11

G

10

G27

LG

04

LG26

G

29

LG24

LG

36

128

LG25

12

7 G

01

LG37

LG

29

LG27

LG

23

G02

G

30

G06

LG

30

LG01

LG

28

109

Valv

eO

pen

in %

oft

hesa

mpl

es

room number

< 20%

> 20 %

> 40%

Fig. 6. Valve opening for all rooms in percent of the samples, i.e. 10% for a room mean that the valves were open in 1 out of 10 samples or in 10% of the time: dark red/gray –valves are open in more than 40% samples; orange/gray – open in more than 20% samples; light green/gray – open in less than 20% samples. (For interpretation of thereferences to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 7. Comparison of the mean PMV thermal comfort value for all rooms and actions drawn in comparison to Fig. 5: dark blue/gray – rooms that are at least cool in more than66% of the time; blue/gray – rooms that are at least cool in 33% of time; light green/gray – rooms with a neutral thermal comfort in mean. (For interpretation of the referencesto color in this figure legend, the reader is referred to the web version of this article.)

J. Ploennigs et al. / Advanced Engineering Informatics 25 (2011) 688–698 695

location of the rooms in the building using the same color scheme.The 58 rooms with a heating system have in mean a relative heatflow of 24.6 kWh/m2 per year. Generally speaking, the rooms fac-ing south have a lower heat flow, while the rooms on the lowerground floor demand a higher heat flow. This behavior is under-standable, as the sun supports heating on the south facing sideand on the ground floor heat is emitted to the ground while onthe 1st and 2nd floor heating is supported by the rooms below.

However, surprisingly the room with highest relative heat con-sumption of 115 kWh/m2 is room 1.09 on the first floor, which isnearly five times of all room’s average heat consumption. It is the

director’s office and has a higher set point of 21 �C. But, the meantemperature in the room is 19.5 �C and the valves are open in 66%of the samples (see Fig. 6). The reason for this very high heat con-sumption is probably that the room is free floating over the build-ing entrance area and that it has two exterior walls. Thus, it has avery high surface to emit heat to the outside and it needs to beinvestigated if insulation can be added.

The rooms LG28, LG01, and LG30 on lower ground floor are theother rooms with a high relative heat flow of 102, 90, and 72 kWh/m2, respectively. The rooms LG28, LG29, and LG30 have exteriordoors that are probably the reason for the heat loss and should

696 J. Ploennigs et al. / Advanced Engineering Informatics 25 (2011) 688–698

be better isolated. The only reason that LG29 has a lower relativeheat flow of 48 kWh/m2 is that it has a very low set point of16 �C. Also LG30 has a reduced set point of 18 �C, which gives thereason that it consumes 29% less heat than LG28. Its set point of20 �C should be reduced to save energy as its valves are open in77% of the samples, the highest value in the building. But, thiswould reduce the thermal comfort of the people working in thelaboratory.

The thermal comfort is an important aspect in considering ac-tions to reduce heat losses, as it represents the experience of thehuman users of the rooms. Reducing the set-points is usually aquick, simple, and cheap approach to reduce a room’s heat usage.But, it also may drastically reduce user’s thermal comfort for a rea-sonable impact on the heat consumption. Improving a room’s insu-lation on the other hand is a sustainable solution that is costly, but,influences the user comfort often positively in the long term. Thisrenders the thermal comfort important for decision making aboutimprovements of a building’s heat consumption.

Fig. 7 shows the mean thermal comfort for the rooms, and Fig. 8illustrates the estimated comfort levels on the right side. The ther-mal comfort Predictive Mean Vote (PMV) as defined in the ISO7730 [46] was predicted from the room temperature and the out-side conditions based on a data mining classification model that

Low

er G

roun

dFl

oor

Grou

ndFl

oor

Firs

tFlo

or

Lower 50% Top 50 % Top 20 %No heating

N

LG35

LG36LG33

G36

G34G33

G05

G32G29

1.34

1.321.311.30

LG01 LG02

LG13 LG16 LG17

LG23 LG24 LG25 LG26 LG27

LG03 LG04 LG05 LG09LG07 LG08LG06

LG19 LG21

LG30LG29LG28

G01 G02

G14 G15 G18G17

G22 G25 G26 G27

G03 G04 G09G07 G08G06

G19 G20

G30G24 G28

G10

G23

1.01 1.02

1.331.15 1.171.16

1.21 1.25 1.26 1.27

1.031.04

1.091.07

1.13

1.06

1.18 1.20

1.28

1.12

1.23

1.05

1.22 1.24

LG37

Fig. 8. (Left) Results for the heat consumption at the ERI (see Fig. 5 for color scheme). (Rireferences to color in this figure legend, the reader is referred to the web version of thi

Table 2Summary of the relative heat coefficients.

Coefficient Required knowledgea

Valve number ðCVN;rÞ – Number of valves VNr

Room size ðCRS;rÞ – Room area or floor heating area Ar– (Heat transfercoefficient hr if it varies)

System size ðCS;rÞ – Water volume, tube length or radiator surface SAr

Design size ðCD;rÞ – Room area or floor heating area Ar– (Overall heat tracoefficient Ur if it varies)

Flow rate ðCF;rÞ – Commissioned flow rate _mC;r– Room area or floor hearea Ar– Overall heat transfer coefficient Ur– Specific hcapacity of the heating medium cw

a Required knowledge additional to: central heat flow _mFH, room temperature Tr , andb Additional to A1–A3.

was created from four rooms that possess also radiant temperatureand humidity sensors [37,39]. The estimated comfort level permitsto identify potential rooms where the temperature set point can bereduced. About 43 of the 71 rooms have a reduced comfort level ofeither ‘slightly cool’ (�1.5 < PMV < �0.5) or ‘cool’ (�2.5 < PMV< �1.5) in more than 33% of the samples. The rooms LG03, LG28,LG29, and LG30 have the lowest comfort level with cool comfortin more than 25% of the cases. Reducing their set-points would de-grade the comfort further and is not recommendable. Instead, it isthe office 1.27 with a mean temperature of 21.8 �C that has thehighest potential to optimize its heat consumption of 39 kWh/m2

by reducing its set point from 21 to 20 �C as it provides one ofthe best comfort levels in the building, while also having a highheat loss.

5. Discussion

Table 2 summarizes the required information, the presump-tions, and the concluding notes for the introduced and comparedrelative heating coefficients. In general, the precision of the ap-proaches increases with the information available and used asdemonstrated for the example. Thus, it is usually better to usethe commissioned flow rate coefficient than the valve number

L ow

er G

r oun

dFl

oor

Grou

ndFl

oor

F irs

tFlo

or

Neutral >33%Cool > 66%Cool Used for modelling

N

LG01 LG02

LG13 LG16 LG17

LG23 LG24 LG25 LG26 LG27

LG03 LG04 LG05 LG09LG07 LG08LG06

LG19 LG21

LG30LG29LG28

G01 G02

G14 G15 G18G17

G22G25 G26 G27

G03 G04 G09G07 G08G06

G19 G20

G30G24 G28

G10

G23

1.01 1.02

1.331.15 1.171.16

1.21 1.25 1.26 1.27

1.031.04

1.091.07

1.13

1.06

1.18 1.20

1.28

1.12

1.23

1.05

1.22 1.24

ght) User comfort for the ERI (see Fig. 7 for color scheme). (For interpretation of thes article.)

Presumptionb Notes

Heating system size and flow rate is aboutthe same for each valve

Good for simpleassumption if not much isknown about the system

Floor is completely used by a floor heatingsystem or heating system is dimensionedproportional to room size

Use design size insteadcoefficient ðCD;rÞ

Flow rate is about the same for each system Good if system size is notproportional to the roomsize

nsfer Floor is completely used by a floor heatingsystem or heating system is dimensionedproportional to room size

Good if system size isproportional to the roomsize

atingeat

Floor is completely used by a floor heatingsystem

Best if the commissionedflow rate is known

central inlet, outlet temperature T in; FH; Tout; FH.

J. Ploennigs et al. / Advanced Engineering Informatics 25 (2011) 688–698 697

coefficient. But, if only the valve number is known for a system,then it will still provide a basic estimation of the heat distributionin the building. The design size coefficient should be preferred overto the room size coefficient as it uses the same information, butconsiders also the logarithmic temperature gradient and is, there-fore, more precise. If the heating system size is not directly relatedto the room size, then it is usually better to use the system sizecoefficient.

Apart from the specific presumptions defined for each coeffi-cient in Table 2, all coefficients presume that the assumptionsA1–A3 are met. This will not be valid for each real world example.For example, A1 assumes that heat is only absorbed by rooms andno heat is lost in the pipes leading to or from them. This allows set-ting the inlet temperature T in; r equal for all rooms, which is a sim-plification of the real world. For example, the heating pump islocated in LG05 in the ERI such that the room furthest away is1.21 on the first floor which lies ca. 6 m above and ca. 45 m awayfrom the heating pump. Of course there is a temperature differencebetween the inlet temperature of room 1.21 and room LG07 justnext to the heating pump. It is possible to model such differencesby: (1) considering them as part of the relative heating coefficient,i.e. increasing the system size coefficient CS;r by the additional pipelength assuming that the temperature in the rooms passed is aboutthe same. (2) If this is not the case, the model also can be easily ex-tended by individual losses to the rooms passed. Both extensionssimply modify the model of the relative heating coefficient witha more precise one, which illustrates the flexibility of the approach.

The assumptions A2 and A3 remove the dynamic behavior ofheat exchange, which also is strong simplification. But, the aimof the approach is to create a virtual sensor model, that assignsheat flow in a building to the causes, which are open control valvesof rooms, and not to model how exactly the heat is lost. Thus, theapproach provides not a physical model of the heat distribution ina building, but simple statistical estimations of this cause-and-ef-fect principle.

These assumptions are only a limitation of the currently definedrelative heating coefficients, and not a limitation of the general ap-proach. More precise coefficients can be defined, based on simula-tion results or measurements of the system. Particularly, if BIM willbe broadly available for buildings in the future, then this processcan be automated and the virtual sensor model can be automati-cally generated. The benefit is in the end, that still only simple tem-perature sensors are needed in the rooms to understand and assignheat flow in a building.

6. Conclusion

The introduced virtual sensors approach for estimation of aroom’s heat consumption provides a simple and scalable way toestimate the heating energy of rooms from simple temperaturereadings and a central heat meter. The approach can be appliedto any building’s heating system whose rooms are individuallycontrolled, as long as a representative relative heating coefficientCr can be defined. The various relative heating coefficients alsoexemplify the flexibility of this approach. Due to the simple rela-tional equation (4) even simple available information such as roomsize or valve number can be utilized to estimate a room’s heat con-sumption. The comparison showed that simple coefficients providereasonable estimations to identify rooms with high heat consump-tion, but cannot replace more complex models such as the com-missioned flow rate coefficient.

Using this coefficient and remodeling the control behavior min-imizes the required sensor equipment for building performanceoptimization. As wireless temperature sensors can, nowadays, becheaply and easily integrated in any building [10], the approach

has practical value for performance analysis of existing buildings.The approach can be used both for offline data analysis and onlineas a virtual sensor to measure rooms’ heat flow. In that case noadditional heat flow meters need to be installed in the rooms. In-stead, the virtual sensor is realized as software in the centralizedBMS or on embedded devices that are responsible for severalrooms. Combined with virtual sensors for thermal comfort [39],the temperature sensors serve multiple purposes from controllingthe room temperature, to monitoring of the temperature, comfort,and heat flow.

The investigated case study of the ERI building showed that theapproach permits to identify energy consumption problems in abuilding. This, in combination with an approach to estimate thethermal comfort in the rooms [37,39], further complements the ba-sis for decision making to improve the building’s energy consump-tion. Both approaches mainly utilize simple temperature sensorsand, therefore, provide simple ways to evaluate buildings systemefficiency and rooms’ consumption while reducing the buildingmonitoring cost.

7. Future directions and challenges

Tools to evaluate and control energy consumption of buildingswill become increasingly important because of limited energy re-sources and climate change. For their broad application it is neededthat they can be used with reliable benefit and low or even no hu-man effort. Their tasks range from detecting construction failuresand energy leakages, suggesting and implementing energy optimi-zations and helping to improve occupants’ comfort.

These tools should become standard in every building. Up tonow, analyzing buildings’ energy consumption is often a manualexpert task based on graphical plots instead of automated compu-tational algorithms. It usually neglects the benefits of energy sim-ulations due to their complexity for non-experts. Easy creation ofbuildings’ behavioral and simulation models is a precondition forimproved acceptance. The growing spread of building informationmodels (BIMs) reduces the effort for simulations significantly. Also,monitoring is usually too expensive. Smarter approaches like vir-tual sensors will help to make monitoring affordable for all build-ing owners.

The approach shown in this paper should be enhanced to be-come a holistic instrument that includes other heating types, cool-ing, electricity consumption, lighting, and hot water preparationand integrates into simple, autonomous building’s energy manage-ment systems. Such systems are a challenge for the future, partic-ularly as the usage of renewable-energy sources, combined withenergy storages (PCM, cars, etc.) as well as a localized dynamicsmart energy market will be the driver to much more dynamic inbuildings’ energy optimization and control.

Acknowledgements

Work in the Strategic Research Cluster ‘ITOBO’ is funded byGrant 06-SRC-I1091 from Science Foundation Ireland (SFI) withadditional contributions from five industry partners. Joern Ploen-nigs thanks the Humboldt-Foundation and the German BMBF forsupporting his research in Ireland.

The authors thank Luke Allan and Ena Tobin, Civil Engineering,UCC for their contribution to this research.

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