Model-based virtual sensors for room heat metering and energy ...

Model-based virtual sensors for room heat metering and energyconsumption analysis in buildings with renewable energy sourcesJoern PloennigsTechnical University Dresden, GermanyAmmar Ahmed, Paul Stack, Karsten MenzelUniversity College Cork, IrelandAbstractModern low-energy buildings use a mixture of conventional (gas, oil, electricity) and renewableenergy-sources (geothermal, sun collectors) to heat or cool the building and to provide domestic hotwater. The complexity of these systems increases the difficulties to analyse their efficiency and toevaluate building’s performance. This paper introduces a simple and scalable model-based virtualsensor that allows analysing a buildings’ energy consumption down to room level using only aminimal set of sensor information. The approach is demonstrated for a case study of theEnvironmental Research Institute (ERI) building that uses such a hybrid HVAC system.Keywords: Energy Efficiency, Building Performance Analysis, Virtual Sensors, Hybrid HVACSystems.1 IntroductionEnvironmentally-friendly buildings combine concepts for energy-efficient buildings with the usage ofrenewable energy sources to create the energy needed for heating, cooling or domestic hot water(Chwieduk, D., 2003). This enables the buildings to operate energy-efficiently and aids the efforts topreserve a clean environment. Therefore, the buildings first need to meet energy-efficiency standardslike the CEN standards implementing the European Union EPBD (Roulet, C. A. and Anderson, B.,2006). Additionally, environmentally-friendly buildings utilise renewable energy resources (Ngowi,A. B., 2001, Chwieduk, D., 2003) to minimize the usage of fossile-fuels and related air pollution.Renewable-energy can be regenerated by natural conservation processes (Chiras, D., 2006) fromwind, sunlight, and geothermal heat. But, these resources are often not always available, such as thatthere is insufficient sunlight for solar collectors on a cloudy winter day. Therefore, regenerativesystems usually are backed up by conventional units such as gas boilers, which results in hybridsystems. However, the conventional back-up systems also reduce the building’s environmentallyfriendlinessas they burn fossil fuels.To ensure a continous high energy-efficiency during operation, the buildings’ performance needsto be regularly analysed and compared to past measures and the intended performance during designphase (Wolfgang, F., 2005). This results in continuous commissioning buildings over their life cycle(Claridge, D. et al., 1994). Determining the amount of energy provided by each renewable orconventional unit is an integral part of building performance analysis of environmentally-friendlybuildings. Furthermore, it is important to analyse a building’s energy consumption down to consumerson room level to identify energy leaks and optimise the building’s performance. But, hybrid systems

have also an increased complexity that renders it harder to analyse the building’s performance.Analysing a building’ energy consumption down to room level requires a high amount ofmeasurement equipment, such as sensors and meters in each single room. Therefore, detailed buildingperformance analysis is often hampered by high monitoring cost, and practitioners often requestalternative cost-efficient methods (Jagemar, L. and Olsson, D., 2007).This paper develops an estimation algorithm in Section 3 to analyse building’s energyconsumption down to room level. The algorithm is adjustable in its information intake to differentsensing and metering equipment available in a building, providing a flexible usage with roughestimations using few sensors to detailed computations using a high density sensing deployment. Thealgorithm can be used for offline data analysis and for online monitoring as a so-called virtual sensor(a model-based sensor) to estimate the heat flows in rooms. Section 4 uses the algorithm’s output andcompares it to the reached user comfort level to evaluate actions improving the building’s energyconsumption. Both approaches provide a broad set of performance metrics to support buildingperformance analysis, monitoring, and optimisation (Augenbroe, G. and Park, C.-S., 2005).The approach is validated using the ERI building as an existing low-energy building with a hybridHVAC system that is introduced in the next section.2 The Environmental Research Institute buildingThe Environmental Research Institute (ERI) is a 4500 m² low-energy building, located in the maincampus of University College Cork. The building is used as “Living Laboratory” by the InformaticsResearch Unit in Sustainable Engineering (IRUSE) and the Irish strategic research cluster ITOBO(ITOBO, 2007) to serve as a full-scale test bed for Intelligent Buildings demonstrating buildingperformance concepts (Keane, M., 2005).CoolingCircuits P-12SolarThermalArrayHotWaterGas-firedBoilerHeat PumpUnderfloorHeatingPumpP-7MMeter HPMeter FH MUnderfloorHeatingManifoldsWater from culverttoriverFigure 1. High Level Schematic of the Mechanical System for the ERIThe building is equipped with a wireless sensor network of about 100 devices and a wiredBuilding Management System (BMS) system consisting of about 180 sensors and meters that monitorindoor and outdoor conditions. The ERI covers various HVAC requirements for laboratories, cleanrooms, cold stores, offices, open offices, and seminar rooms used by multiple research groups frombiologist, chemist, engineers, and computer scientists.The building is heated by an under-floor heating system that is primarily supplied by a geothermalheat pump that taps into a water supply fed from a culvert running adjacent to a nearby river. Thewater is preheated by heat recovered from the cold stores and heat generated by the solar thermalarray. The under-floor heating operates at a maximum temperature of 40ºC. A condensing gas boiler

is sized to act as a complete back-up system. The hot water is provided by an 84 m² solar thermalcollector array, where the rest of the requirement is provided by the boiler. See Figure 1 for aschematic of the mechanical system. The heat pump and boiler are separately controlled. Both havetimetable dependent set-points. They ensure that heating and domestic hot water are provided overworking hours.3 Estimating the room heat consumptionTo identify rooms with unusual high heat usage the room’s heat consumption needs to be analysed.But, instead of installing heat meters in each room to measure the individual heat consumption, theroom’s heat intake is estimated from the overall heat consumption of the underfloor heating system bycreating a virtual sensor model using knowledge about the room heating controls.In case of the ERI, each room has an individual temperature closed-control loop consisting of atemperature sensor, a decentralised controller and one to four on/off-valves operating separateunderfloor heating circuits. The temperature values are logged by the BMS. However, exactinformation about the control values at the valves is not available in the BMS system. This is notuncommon as often only measurements are seen as important are logged, but it is the control valuesthat define the room’s and building’s heat consumption. The temperature closed-control loops areactivated all of the time in the ERI; but, the central heating system provides heat only at specific times.The basic assumption for the estimation is: that only these rooms consume heat, which valves areopen and allow the hot water to circulate through the underfloor circuits. Let be defined as theabsolute heat flow of the underfloor heating system measured by the heat meter FH in Figure 1 in caseof the studied ERI. The heat flow is computed in the heat meter from the mass flow rate in thepipes and the temperature difference of the water going in and coming out of the underfloor circuits as · ; ; . The mass flow rate is controlled in case of the ERI by the centralunderfloor heating pump in Figure 1. The temperature difference depends on the heat consumption(flow) of the rooms. Let be the heat flow of any room in the set of rooms connected to theheating system. Assuming further that the underfloor heating system has no further losses beside inthe rooms, then the overall heat flow in time step is the sum of all room’s flows and each room takes a percentaged share =/ in the overall consumption, thus · ; ; . (1)Due to the basic assumption, a room’s heat flow depends on the valve opening 0 100%.Which is insofar correct, as the valve opening limits the individual mass flows in the underflowcircuits of each room. Hence, if the valves in a room are closed ( = 0%) then the mass and heat flowas well as will be zero. But, if the valves are open ( > 0%) then the percentaged share can be inthe range of 0 1 as long ∑ 1 holds as given by eq. (1). Thus, considering the valveopening only allows determining the rooms heated, but the share these rooms take of the building’sheat consumption is still unknown.Therefore, it is assumed that for each room a relative heating coefficient can be defined that is ameasurement of the room’s heating capacity in relation to other rooms if all valves are fully open.This can be for example the sizes of the room’s underfloor heating circuits. Let the actual heatingcoefficient of a building be further the sum of all room’s coefficients linearly depending ontheir valve opening such that · . (2)

Then, is a measurement of the building’s actual active heating capacity, i.e. if representsthe room’s underfloor heating circuits size then is the actual size of active circuits in time stepk. As is defined as the room’s heating capacity in relation to other rooms, eq. (2) also allows toestimate the percentual share of a room in the overall heat flow from eq. (1) such that finally results · . (3) This equation (3) allows estimating the room heat flow from the relative heating capacity of aroom in comparison to the others. In the named example of using the underfloor heating circuit size as it will mean that a room utilises the same proportion of the actual heat flow as his heating circuitsize is in relation to the building’s active circuits . The benefit of equation (3) is that it easilyscales to different available knowledge levels by modelling and adequately.The valve opening depends on the control values computed by the room temperaturecontroller. If a detailed model is necessary then the specific characteristic curves of the valves can beused. In case of the ERI, simple on/off-valves are used and it can be simplified that 0, 1.The relative heating coefficient of each room can be individually adapted to the availableinformation about the building. In simple cases it can be set to the number of valves or radiators,which permits rough estimations. Better results provide the size of the underfloor heating system orradiators assuming that their size is proportional their heating capacity. For very precise results,detailed models of the heating system systems can be used that model also dynamic aspects such asthe dependence of the heat transfer on the temperature difference between the heating system androom air temperature.The estimation in its basic form in eq. (3) neglects any time delay behaviour in the heating systemsuch as the dead times of the water floating through the pipes as well as the delay behaviour of theheat transfer between the underfloor and the room air temperature. It assumes that the delay effectsare less relevant for the estimation considering the fact that large sampling intervals of 15 min arecommon in building performance analysis like in the ERI case. If the delay times are very large andnot neglectable the equation (3) can be extended to an difference equation considering also pastcoefficients with and .Summarising, a buildings room’s heating consumption can be estimated in each time step by the following algorithm:(1) Compute the room’s control value and valve opening for each room .(2) Compute the building’s actual heating coefficient with eq. (2) from .(3) Estimate the room’s heat flow for each room via eq. (3) from , .The proposed algorithm uses the control value for each room’s individual heating control. Asinformation about the control value is often not available like in the investigated ERI case, the controlvalue can also be computed by remodelling the control algorithm. In case of the ERI, the valvesare individually controlled for each room by a bounce control with a hysteresis of 0.3Cº arounda temperature set-point . This means, that if the temperature in a room drops below ( - ) the valves are opened ( 1) and the room is heated until the room temperaturereaches ( + ) at which point the valves are closed ( 0). Thus,1, ; 0, ; (4) 1, otherwise.If more advanced PID controllers are used the control value can be recomputed in a comparableway if the controller parameters are known. If the set-point is not known it partly can bereconstructed from the step response of the room temperature (Vasyutynskyy, V. et al., 2005) if thequality of control is good enough.

5 ConclusionThe introduced approach provides a simple and scalable way to estimate the heating energy of roomsfrom simple temperature readings and a central heat meter. The approach can basically be applied toany buildings heating system that rooms are individually controlled, as long as a representative staticor dynamic relative heating coefficient can be defined. Using this coefficient and remodelling thecontrol behaviour minimises the needed sensor equipment. As wireless temperature sensors providenowadays a cheap and easy integration in any building (Menzel, K. et al., 2008), the approach haspractical value for performance analysis of existing buildings. The approach can be used both foroffline data analysis and online as a virtual sensor to meter rooms’ heat flow.The investigated case study of the ERI building showed that the approach permits to identifyenergy problems in a building. The analysis covered also aspects of the renewable energy sourcesutilised by the ERI building. The combination with an approach to estimate the thermal comfort in therooms (Ahmed, A. et al., 2009) further complements the basis for decision making to improve thebuildings energy consumption. Both approaches mainly utilise simple temperature sensors and,therefore, provide simple ways to evaluate buildings system efficiency and rooms’ consumption whilereducing the building monitoring cost.AcknowledgementsWork in the Strategic Research Cluster ‘ITOBO’ is funded by grant 06-SRC-I1091 from ScienceFoundation Ireland (SFI) with additional contributions from five industry partners. Joern Ploennigsthanks the Humboldt-Foundation and the German BMBF for supporting his research in Ireland.ReferencesAHMED, A., J. PLOENNIGS, Y. GAO, and K. MENZEL. 2009. Analyse building performance data for energy-efficientbuilding operation. In: CIB W78 - 26th Int. Conf. on Managing IT in Construction. Istanbul, Turkey.AUGENBROE, G. and C.-S. PARK. 2005. Quantification methods of technical building performance. Building Researchand Information. 33(2), pp.159-172.CHIRAS, D. 2006. The homeowner's guide to renewable energy: achieving energy independence through solar, wind,biomass and hydropower. New Society Publishers.CHWIEDUK, D. 2003. Towards sustainable-energy buildings. Applied Energy. 76(1-3), pp.211-217.CLARIDGE, D., J. HABERL, M. LIU et al. 1994. Can You Achieve 150 Percent Predicted Retrofit Savings: Is It Time forRecommissioning? In: ACEEE 1994 Summer Study on Energy Efficiency in Buildings. Washington D.C., US, pp.73-88.ISO 7730 - Ergonomics of the thermal environment - Analytical determination and interpretation of thermal comfort usingcalculation of the PMV and PPD indices and local thermal comfort criteria. 2005. ISO.ITOBO. 2007. Information & Communication Technology for Sustainable and Optimised Building Operation. Cork:http://zuse.ucc.ie/itobo/.JAGEMAR, L. and D. OLSSON. 2007. The EPBD and Continuous Commissioning.KEANE, M. 2005. Hidden Depths. Building Service Journal., pp.23-28.MENZEL, K., D. PESCH, B. O’FLYNN et al. 2008. Towards a Wireless Sensor Platform for Energy Efficient BuildingOperation. In: 12th Int. Conf. on Computing in Civil and Building Engineering. Beijing, China, pp.381-386.NGOWI, A. B. 2001. Creating competitive advantage by using environment-friendly building processes. Building andEnvironment. 36(3), pp.291-298.ROULET, C.A. and B. ANDERSON. 2006. CEN Standards for Implementing the European Directive on EnergyPerformance of Buildings. In: PLEA - 23rd Int. Conf. on Passive and Low Energy Architecture. Geneva, Switzerland.VASYUTYNSKYY, V., J. PLOENNIGS, and K. KABITZSCH. 2005. Passive Monitoring of Control Loops in BuildingAutomation. In: FET 2005 - 6th IFAC Int. Conf. on Fieldbus Systems and their Applications. Mexico, pp.263-269.WOLFGANG, F. 2005. Assessing building performance. Butterworth-Heinemann.