Thermal comfort and energy related occupancy behavior in Dutch residential dwellings

Abstract

Residential buildings account for a significant amount of the national energy consumption of all OECD countries and consequently the EU and the Netherlands.Therefore, the national targets for CO2 reduction should include provisions for a more energy efficient building stock for all EU member states. National and European level policies the past decades have improved the quality of the building stock by setting stricter standards on the external envelope of newly made buildings, the efficiency of the mechanical and heating components,the renovation practices and by establishing an energy labelling system. Energy related occupancy behavior is a significant part, and relatively unchartered, of buildings’ energy consumption. This thesis tried to contribute to the understanding of the role of the occupant related to the energy consumption of residential buildings by means of simulationsandexperimentaldataobtainedbyanextensivemeasurementcampaign.ThefirstpartofthisthesiswasbasedondynamicbuildingsimulationsincombinationwithaMonteCarlostatisticalanalysis,whichtriedtoshedlighttothemostinfluentialparameters,includingoccupancyrelatedones,thataffecttheenergyconsumptionandcomfort(afactorthatisbelievedtobeintegraltotheenergyrelatedbehaviorofpeopleinbuildings).ThereferencebuildingthatwasusedforthesimulationswastheTUDelftConceptHousethatwasbuiltforthepurposesoftheEuropeanprojectSusLabNWE.TheconcepthousewassimulatedasanAenergylabel(veryefficient)andFlabel(veryinefficient)dwellingandwiththreedifferentheatingsystems.Theanalysisrevealedthatifbehavioralparametersarenottakenintoaccount,themostcriticalparametersaffectingheatingconsumptionarethewindowUvalue,window g value, and wall conductivity. When the uncertainty of these parameters increases, the impact of the wall conductivity on heating consumption increases considerably.Themostimportantfindingwasthatwhenbehavioralparameterslikethermostatuseandventilationflowrateareaddedtotheanalysis,theydwarftheimportance of the building parameters in relation to the energy consumption. For the thermalcomfort(thePMVindexwasusedastheestablishedmodelformeasuringindoorthermalcomfort)themostinfluentialparameterswerefoundtobemetabolicactivity and clothing, while the thermostat had a secondary impact.Thesimulationswerefollowedbyanextensivemeasurementcampaignwhereanin-situ, non-intrusive, wireless sensor system was installed in 32, social housing, residentialdwellingsintheareaofDenHaag.ThissensorsystemwastransmittingTOC 18Thermal comfort and energy related occupancy behavior in Dutch residential dwellingsquantitativedatasuchastemperature,humidity,CO2levels,andmotioneveryfiveminutesforaperiodofsixmonths(theheatingperiodbetweenNovembertoApril)andfrom every room of the 32 dwellings that participated in the campaign. Furthermore, subjective data were gathered during an initial inspection during the installation of the sensor system, concerning the building envelope, the heating and ventilation systems of the dwellings. More importantly though, subjective data were gathered related to the indoor comfort of the occupants with the use of an apparatus that was developed specificallyfortheSusLabproject.Thisgimmick,namedthecomfortdial,allowedustocapturedatasuchastheoccupants’comfortlevelinthePMV7pointscale.Inadditionfurthercomfortrelateddataliketheoccupants’clothingensemble,actionsrelatedtothermal comfort, and their metabolic activity were captured with the use of a diary. Thesubjectivedatameasurementsessionlastedforaweekforeachdwelling.Thesedataweretimecoupledrealtimewiththequantitativedatathatweregatheredbythesensor system. The data analysis focused on the two available indoor thermal comfort models, Fanger’sPMVindexandtheadaptivemodel.ConcerningthePMVmodeltheanalysisshowedthatwhiletheneutraltemperaturesarewellpredictedbythePMVmethod,thecoldandwarmsensationsarenot.Itappearsthattenantsreported(onastatisticallysignificantway)comfortablesensationswhilethePMVmethoddoesnotpredictsuch comfort. This indicates a certain level of psychological adaptation to occupant’s expectations.Additionallyitwasfoundthatalthoughclothingandmetabolicactivitiesweresimilaramongtenantsofhouseswithdifferentthermalquality,theneutraltemperaturewasdifferent.Specificallyinhouseswithagoodenergyrating,theneutraltemperature was higher than in houses with a poor rating.Concerning the adaptive model, which was developed as the answer to the discrepanciesofFanger’smodelrelatedtonaturallyventilatedbuildings(themajorityoftheresidentialsector),dataanalysisshowedthatwhileindoortemperaturesarewithintheadaptivemodel’scomfortbandwidth,occupantsoftenreportedcomfortsensations other than neutral. In addition, when indoor temperatures were below thecomfortbandwidth,tenantsoftenreportedthattheyfelt‘neutral’.Theadaptivemodel could overestimate as well as underestimate the occupant’s adaptive capacity towardsthermalcomfort.Despitethesignificantoutdoorstemperaturevariation,the indoor temperature of the dwellings, as well as the clothing of the tenants, werelargelyconstant.Certainactionstowardsthermalcomfortsuchas‘turningthethermostatup’weretakingplacewhiletenantswerereportingthermalsensation‘neutral’or‘abitwarm’.Thisindicatesthateitherthereisanindiscriminationamongthe various thermal sensation levels or alliesthesia, a new concept introduced by the creators of the adaptive model, plays an increased role. Most importantly there was an uncertainty on whether the neutral sensation means at the same time comfortable TOC 19Summarysensation while many actions are happening out of habit and not in order to improve one’sthermalcomfort.Achi²analysisshowedthatonlysixactionswerecorrelatedtothermalsensationinthermallypoorlyefficientdwellings,andsixinthermallyefficient dwellings.Finally, the abundance of data collected during the measurement campaign led the lastpieceofresearchofthisthesistodataminingandpatternrecognitionanalysis.Since the introduction of computers, the way research is performed has changed significantly.Hugeamountsofdatacanbegatheredandhandledbyevermorefastercomputers;theanalysisofthesedataacoupleofdecadesagowouldtakeyears.Sequentialpatternminingrevealsfrequentlyoccurringpatternsfromtime-orderedinput streams of data. A great deal of nature behaves in a periodic manner and these strong periodic elements of our environment have led people to adopt periodic behaviorinmanyaspectsoftheirlivessuchasthetimetheywakeupinthemorning,thedailyworkinghours,theweekenddaysoff,theweeklysportspractice.Theseperiodicinteractionscouldextendinvariousaspectsofourlivesincludingtherelationship of people with their home thermal environment. Repetitive behavioural actions in sensor rich environments, such as the dwellings of the measurement campaign,canbeobservedandcategorizedintopatterns.Thesediscoveriescouldformthe basis of a model of tenant behaviour that could lead to a self-learning automation strategyorbetteroccupancydatatobeusedforbetterpredictionsofbuildingsimulatingsoftwaresuchasEnergy+orESP-randothers.Theanalysisrevealedvariouspatternsofbehaviour;indicatively59%ofthedwellingsduringthemorninghours(7-9a.m.)wereincreasingtheirindoortemperaturefrom20 oC< T< 22 oC to T> 22oCorthatthetenantsof56%ofthedwellingswerefindingthe temperature 20 oC< T< 22 oC to be a bit cool and even for temperatures above 22 oC they were having a warm shower leading to the suspicion that a warm shower is a routine action not related to thermal comfort.Suchpatternrecognitionalgorithmscanbemoreeffectiveintheeraofmobileinternet,which allows the capturing of huge amounts of data. Increased computational powercananalysethesedataanddefineusefulpatternsofbehaviourthatcouldbetailor made for each dwelling, for each room of a dwelling, even for each individual of a dwelling. The occupants could then have an overview of their most common behaviouralpatterns,seewhichonesareenergyconsuming,whichonesarerelatedtocomfort and which are redundant, and therefore, could be discarded leading to energy savings. In any case the balance between indoor comfort and energy consumption will bethefinalfactorthatwouldleadtheoccupanttodecideonacustomisedmodelofhisindoor environment. TOC 20Thermal comfort and energy related occupancy behavior in Dutch residential dwellingsThegeneralconclusionofthisthesisisthattheeffectofenergyrelatedoccupancybehaviourontheenergyconsumptionofdwellingsshouldnotbestatisticallydefinedforlargegroupsofpopulation.Therearesomanydifferenttypesofpeopleinhabitingsomanydifferenttypesofdwellingsthatembarkinginsuchataskwouldbeaconsiderable waste of time and resources.The future in understanding the energy related occupancy behaviour, and therefore using it towards a more sustainable built environment, lies in the advances of sensor technology, big data gathering, and machine learning. Technology will enable us to move from big population models to tailor made solutions designed for each individual occupant.