Occupants' behaviour: determinants and effects on residential heating consumption

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Occupants' behaviour: determinants and effects onresidential heating consumptionOlivia Guerra-Santin a & Laure Itard aa OTB Research Institute for Housing, Urban and Mobility Studies, Delft University ofTechnology, Jaffalaan 9, NL-2628, BX, Delft, the NetherlandsPublished online: 21 Apr 2010.

To cite this article: Olivia Guerra-Santin & Laure Itard (2010) Occupants' behaviour: determinants and effects on residentialheating consumption, Building Research & Information, 38:3, 318-338, DOI: 10.1080/09613211003661074

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RESEARCH PAPER

Occupants’behaviour: determinants ande¡ects on residential heating consumption

Olivia Guerra-Santin and Laure Itard

OTBResearch Institute for Housing,Urban andMobility Studies,Delft University of Technology, Ja¡alaan 9,NL-2628,BX Delft, the Netherlands

E-mails: [email protected] and [email protected]

What are the key determinants and effects of occupants’ behaviour on energy use for space heating? Statistical analyses

were carried out on energy use and self-reported behaviour data from a household survey in the Netherlands. Results

showed that the number of usage hours for the heating system have a stronger effect on energy consumption than

temperature setting. Small correlations were found between energy use and the ventilation system, since most

households barely use the ventilation system. The main building characteristic determining behaviour is the type of

temperature control. Households with a programmable thermostat were more likely to keep the radiators turned on

for more hours than households with a manual thermostat or manual valves on radiators. In relation to household

characteristics, the presence of elderly persons in the household proved to be a determining factor in the use of the

heating system and ventilation. As a result of wide variations in preferences and lifestyle, occupant behaviour has

emerged as an important contributor to energy use in dwellings. The results indicate that the type of heating and

ventilation system has an influence on occupant behaviour.

Keywords: behaviour, comfort, energy demand, household, housing, inhabitant behaviour, residential stock, space

heating

Quels sont les principaux determinants et effets du comportement des habitants en matiere d’utilisation d’energie pour le

chauffage des locaux ? Des analyses statistiques ont ete realisees sur l’utilisation de l’energie et sur les donnees

comportementales autodeclarees issues d’une enquete menee aux Pays-Bas aupres des menages. Les resultats ont

montre que le nombre d’heures d’utilisation du systeme de chauffage a un effet plus important sur la consommation

energetique que le reglage de la temperature. Il n’a ete trouve que peu de correlations entre l’utilisation d’energie et le

systeme d’aeration, dans la mesure ou la plupart des menages utilisent tres peu le systeme d’aeration. La principale

caracteristique des batiments qui determine le comportement est le type de reglage de la temperature. Les menages

equipes d’un thermostat programmable etaient plus susceptibles de maintenir les radiateurs allumes pendant

davantage d’heures que les menages equipes d’un thermostat manuel ou de robinets manuels sur les radiateurs.

S’agissant des caracteristiques des menages, il a ete demontre que la presence de personnes agees dans le menage est

un facteur determinant dans l’utilisation du systeme de chauffage et du systeme d’aeration. Du fait des grandes

variations existant dans les preferences et les modes de vie, le comportement des occupants est apparu comme etant

un facteur important dans l’utilisation de l’energie dans les habitations. Les resultats indiquent que le type de systeme

de chauffage et d’aeration a une influence sur le comportement des occupants.

Mots cles: comportement, confort, demande energetique, menage, logement, comportement des habitants, parc de

logements residentiels, chauffage des locaux

IntroductionAlthough building characteristics are known to have asignificant effect on energy consumption, their influ-ence in residential buildings has decreased in recent

years because governments worldwide have introducedregulations and policies in an attempt to raise energyperformance in the built environment. These regu-lations are designed first and foremost to increase

BUILDING RESEARCH & INFORMATION (2010) 38(3), 318–338

Building Research & Information ISSN 0961-3218 print ⁄ISSN 1466-4321 online # 2010 Taylor & Francishttp: ⁄ ⁄www.informaworld.com ⁄journals

DOI: 10.1080/09613211003661074

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system efficiency and to improve the thermal propertiesof new buildings. Meanwhile, renovation programmesare being launched to raise the energy performance ofexisting buildings.

In 1995, the energy-efficiency regulations in theNetherlands were expanded to include the EnergyPerformance Coefficient (EPC). It is calculated on thebasis of the energy used for space and water heating,ventilation, lighting, the efficiency of the installations,and the size of the dwelling. The calculation of energyused for space heating also takes account of solar andinternal heat gains and losses through transmission.

These measures, along with ongoing improvementsto system efficiency, materials, and constructionmethods, have significantly reduced the amount ofenergy used for space heating (Leth-Petersen andTogeby, 2001; Beerepoot and Beerepoot, 2007;Jeeninga et al., 2001). However, studies have indicatedthat as buildings become more energy efficient, thebehaviour of occupants plays an increasinglyimportant role in consumption (Jeeninga et al., 2001;de Groot et al., 2008; Haas et al., 1998; Papakostasand Satiropoulos, 1997; Andersen et al., 2009).Indeed, the wide variation in energy consumptionin buildings with the same physical characteristicsis attributed to differences in occupancy patterns(Jeeninga et al., 2001; Haas et al., 1998; Lindenet al., 2006; Branco et al., 2004) and differencesbetween the calculated and real performances.

Occupancy patterns may be determined by lifestyle,preferences, attitudes, perceptions of comfort, personalbackground, and household characteristics (Andersenet al., 2009; Schweiker and Shukuya, 2009). Theresults of a study in Japan (Schweiker and Shukuya,2009) indicate that the way people use heating systemsis determined more by personal factors such as experi-ence, attitude, and origin than by external conditions.In a Danish study on behaviour, the control ofheating, mechanical ventilation and air-conditioning(HVAC) systems, and the indoor environment,Andersen et al. (2009) found that ventilation andheating behaviour is influenced by, amongst otherthings, perception, gender, and ownership.

The use of heating and ventilation systems can also beinfluenced by the type of HVAC system present in thedwelling. Various authors (Nicol and Humphreys,2009; de Dear, 2004; Lenzuni et al., 2008; Karjalalai-nen, 2007; Lan et al., 2008; Moujalled et al., 2008;Ye et al., 2006; American Society of Heating, Refriger-ating and Air-Conditioning Engineers (ASHRAE),2005) contend that a dynamic interaction betweenoccupants and the building is crucial in raisingcomfort levels. Becker and Pacuik (2009) maintainthat thermal adaptation processes are linked tocontextual variables such as local climate, occupant

expectations, and the ability to control the environ-ment. Their results of a survey that collected data onhygrothermal conditions and occupant behaviourpointed to a relationship between thermal responsesand control and interaction processes in the indoorenvironment. According to Brown and Cole (2009),the interplay between knowledge, personal control ofHVACs, and comfort is complex, since there is arelationship between knowledge and control, but norelationship with comfort.

Several studies have focused on the effect of the type ofthermostat control on energy use, based on theassumption that households with programmablethermostats set the temperature at a lower levelwhen nobody is home or during the night (setbackbehaviour). Therefore, a reduction on energy use isassumed in households with a programmable thermo-stat in comparison with households with manualthermostats or no thermostat control.

Nevius and Pigg (2000) performed a study in 299houses to ascertain the thermostat setting duringdetermined times of the day and the average numberof hours per week that inhabitants were present athome. They found that the presence of a thermostathas a minimal effect on energy use, and temperaturesettings do not significantly differ between dwellingswith programmable thermostats and those with amanual thermostat. In a study of 427 households,Shipworth et al. (2010) found that in dwellings withthermostats, the mean temperature setting is slightlylower than in dwellings without a thermostat. Theyalso found that households with a programmablethermostat keep the heating system on for longerthan households with manual thermostats, though thedifference was not statistically significant at thep ¼ 0.05 level.

In a survey of 279 houses in California, Lutzenhiser(1992) found that households with manual thermo-stats used less energy in comparison with householdswith programmable thermostats. According to Lutzen-hiser, manual control involves the deliberate cooling ofpeople or the deliberate preparation of a cool space forpeople, while automatic cooling occurs regardless ofoccupancy or activity.

Conner and Lucas (1990) measured indoor tempera-ture and gathered data on self-reported heatingbehaviour on 400 households. They found that thetemperature setting in dwellings with programmablethermostats decreased only by 0.58F in comparisonwith dwellings with manual thermostats. Theyconcluded that programmable thermostats do notsignificantly decrease the incidence of setback behaviour.

A previous study (Guerra Santin et al., 2009) foundthat 42% of the variation in the energy consumed in

Occupants’ behaviour and heating consumption

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the Dutch housing stock for heating space and watercould be explained by the type of dwelling, type ofHVAC system, and insulation level. An additional4.2% could be explained by household characteristicsand occupant behaviour. The household character-istics that influenced energy consumption were size,age of the respondent, type of ownership, andincome. The behavioural influences were the numberof heated bedrooms and thermostat settings. Never-theless, only a few variables related to occupant be-haviour were investigated. The first part of thisstudy (Guerra Santin et al., 2010), on the effect ofbuilding regulations on energy consumption in dwell-ings built after the introduction of the energy perform-ance regulations, highlighted the importance ofinsulation levels in the variation of energy consump-tion. That said, there are still wide variations in theenergy consumption of dwellings with the sameEPC. These could be caused by the actual thermalquality of the dwellings and by differences in occupantbehaviour.

Energy consumption could be reduced still further if aclearer idea existed of occupant behavioural patterns(trends in the use of heating and ventilation systems)that underlie the wide variation in energy consump-tion. This paper seeks to determine the importance ofoccupant behaviour in levels of energy consumptionin dwellings built after 1995, the year when energyperformance requirements were introduced in theNetherlands. The ultimate aim is to ascertain howoccupant behaviour influences the effectiveness ofbuilding regulations and to gain insight into therelationships between behaviour, HVAC systems, andhousehold characteristics.

The following research questions are addressed:

. What behavioural patterns can be observed indwellings built after the introduction of newbuilding regulations in the Netherlands?

. How does occupant behaviour affect energyconsumption?

. Is there a difference in the use of heating andventilation systems in dwellings with differentventilation systems and temperature control?

. Do households with different characteristicsdisplay a difference in the use of heating andventilation systems?

Figure 1 shows the framework of this research. Firstly,the effect of occupants’ behaviour on energy use isanalysed (a); secondly, the relationship betweenoccupant’s behaviour and building characteristics isinvestigated (b); and lastly, the relationship betweenbehaviour and household characteristics is studied

(c). The second section describes the data and method-ology; the third section presents the results of the study;the fourth section contains an extension of the analysis;the fifth section presents the discussion; and the sixthsection contains conclusions and recommendationsfor further research.

Data andmethodologyThe study consisted of a statistical analysis of a house-hold survey carried out by the OTB Research Institutefor Housing, Mobility and Urban Studies in theNetherlands. The objective of the OTB survey was toobtain detailed data on occupant behaviour and topair it with building characteristics defined in theEPC calculation. The survey consisted of a paper ques-tionnaire sent to all households in two previouslyselected districts in the Netherlands. The survey wascarried out simultaneously in the districts in theautumn of 2008. The districts were built after theintroduction of the Dutch energy performance regu-lations. Districts were selected to ensure the possibilityof gathering data about the EPC level from eithermunicipalities or architect offices.

The questionnaire was sent to a total of 7000 house-holds in both districts, with a response rate of 5%(313 usable cases). The low response rate was causedby the length and detail of the questionnaire, and bythe fact that respondents felt uncomfortable withproviding personal information about their lifestyleand personal belongings.

As some sectors of society, such as students and low-income households, were implicitly excluded, thesurvey was compared with the WoononderzoekNederland (WoON) database from the Dutch Ministryof Housing. This database contains random casesacross the country, but with less detailed data onheating and ventilation behaviour, and no informationabout the EPC level. The type of data collected isexplained further in the following subsections.

Figure 1 Research framework

Guerra-Santin and Itard

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Building characteristicsThe studied districts are Leidsche Rijn in Utrecht(Figure 2) and Wateringse Veld in The Hague (Figure3). The construction of Leidsche Rijn began in 1997and will be complete by 2025. The construction ofWateringse Veld began in 1996 and will be completeby 2011. There are eight zones in these districts, butthey could not all be included in the sample as someare still unoccupied or under construction.

Both districts contain the types of housing that arerepresentative for the Netherlands: detached, semi-detached, terraced, corner houses, maisonettes (two-floor flats), and flats. The dwellings in WateringseVeld have individual central heating systems, whilealmost all the dwellings in Leidsche Rijn have districtheating (with four exceptions). The houses in LeidscheRijn have mechanical exhaust ventilation and thehouses in Wateringse Veld have both mechanicalexhaust ventilation and balanced ventilation (fordefinitions, see Table 1). The data on buildingcharacteristics were obtained from EPC files provided

by municipalities and architects. These data werematched with the survey responses.

There were three types of heating control in thesample: manual valves on radiators, a manual thermo-stat, and a programmable thermostat. Dwellings withmanual valves on radiators in addition to a manualor a programmable thermostat were categorizedaccording to the type of thermostat. Only a few dwell-ings had balanced ventilation, which is normal forhouses in the Netherlands. The variables for thebuilding characteristics are shown in Table 1.

Household characteristicsThe analysis took account only of the householdcharacteristics that had been proven important inother studies. The information gathered with thesurvey is related household demographics such ashousehold size, age of occupants, main occupation,hours of work or study outside the house, householdincome, years of residence in the house, and type ofprevious dwelling. As some authors had reported arelationship between age and energy consumption,the dichotomous variables ‘presence of elderlypersons in the household’ and ‘presence of children inthe household’ were analysed. ‘Elderly’ was definedas occupants over the age of 65 years and children asoccupants under the age of 12 years. ‘Educationallevel’ was defined as a categorical variable comprisingthree groups: lower education, middle education, ortechnical education and higher education. Incomelevel was defined as a categorical variable (accordingto the Dutch Central Bureau Statistics) and refersto the total household income (for definitions, seeTable 1).

The current use of the heating and ventilation systemcould also be influenced by the set-up in the previoushouse. Since the survey was carried out in relativelynew dwellings, the respondents were asked to statewhether they had previously lived in a single- or multi-family dwelling (for definitions, see Table 1).

Occupants’ behaviourThe questionnaire included questions on householdcharacteristics, use of heating and ventilation systems,showering and bathing frequency, and energy consump-tion. The occupants were asked to fill in tables showingthe use of the systems and the time spent at home onweekdays and weekends in winter and summer. Sincethe survey was carried out in the autumn, they wereasked to consider their behaviour for a summer and awinter week without extreme weather conditions (toomuch wind, rain or cold). Tables were used to collectdata on the duration and times that the heating andventilation systems were used; and on the use of thedifferent rooms in the house.

Figure 2 LeidscheRijn,Utrecht

Figure 3 WateringseVeld,TheHague


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The data collected in the survey took as far as possiblethe form of continuous variables. All variables werechecked for normality and outliers. A Kolmogorov–Smirnov test was performed to check for kurtosis, skew-ness, and normality of distribution. The non-normalvariables were checked for outliers. Since outliersseemed to be accurate data, non-normal variables wereeither transformed according to their shape with base10 logarithm or square-root (Field, 2005) or convertedinto categories. In cases where this was not possible,non-parametric tests were used. For the statistics andnumber of cases for behaviour variables, see Table 2.

Statistical analyses with SPSS1 were conducted todetermine the relationships and differences betweenthe variables and energy consumption. In the firstpart of the study, which investigated relationshipsbetween energy use and occupant behaviour, Pearsonproduct-moment correlation coefficients were appliedfor continuous variables; independent-samples t-testswere applied for dichotomous variables; and one-wayanalysis of variance (ANOVA) and Chi-square testsfor categorical variables. Regression analyses werecarried out to determine the variability in energy con-sumption explained by behaviour. The second part ofthe study concentrated on the relationship betweenoccupant behaviour (use of heating and ventilationsystems) and type of temperature control, ventilationsystems, and household characteristics. Since thesevariables were categories in most cases, ANOVA andChi-square tests were used to determine the relation-ship between them.

EnergyconsumptionRespondents were asked to report the energy consump-tion from their last available energy bill. The energydata were corrected using heating degree-days, basedon a 12-month period 2006–2007, with a baselinetemperature of 188C (2264.3 heating degree-days forUtrecht and 2186.9 heating degree-days for TheHague), since the years of the reported energy usedranged from 2005 to 2008.

Two types of energy for heating are used in the studieddistricts: heat (dwellings with district heating) andgas (dwellings with individual central heating). Theenergy reported from dwellings using gas includedenergy for space heating, water heating, andcooking. The energy data from dwellings with districtheating includes energy used for space and waterheating, but not for cooking, since these householdscooked with electricity. Only gas was reported as theprimary energy. The generation of district heatingis considered to have an efficiency of 0.95 in theNetherlands (NEN, 2004). Energy for cooking anddifferences in system efficiency might therefore havehad a slight effect on the reported energy use.However, gas for cooking is not expected to exceed5% (EuroACE, 2004).

Not all cases were used in the analysis because thereported energy use was incomplete or faulty.Table 3 shows the number of cases per districtcontaining data on energy that could be used for theanalysis.

Table 1 Household and building characteristics in theOTB survey

Variable Category

Elderly Presence of elderly persons in the household (yes/no)

Children Presence of children in the household (yes/no)

Education level Lower education:middle (middle general or lower technical education); and higher (higher technical orhigh education)

Income level according to theCentraal Bureau voor deStatistiek (CBS) (2008)

(1) Below themedian (, E28 500)(2)Median (E28 500^34 000)(3) One to two times themedian (E34 000^56 000)(4) Above twice themedian (. E56 000)

Type of ventilation system Mechanical exhaust ventilation: these systems extract indoor air from a house while air from outsidein¢ltrates through leaks in the building shell and through passive vents like grilles or windows(Department of Energy, 2002)

Balanced ventilation: these systems supply and exhaust approximately equal quantities of fresh outsideair and polluted inside air, respectively. A balanced ventilation system has two fans and two ductsystems (Department of Energy, 2002), one for the supply and one for the exhaust.Heat recovery in aheat exchanger is applied between the warm exhaust air and the cold supply air

Type of temperature control Manual valves on radiatorsManual thermostat (non-programmable thermostat)Programmable thermostat (or clock thermostat)

Type of previous dwelling Multifamily dwelling: a dwelling located in a building that contains other dwellingsSingle-family dwelling: a building containing one dwelling unit (a detached house, a terraced house)


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Table 2 Descriptive statistics of continuous and categorical variables in theOTB survey

Variable De¢nition Mean SD n

Energy Energy for space and water heating per year 34 520.17 24774.55 240(log) Energy for space and water heating per year 4.45 0.31 240

Thermostat Highest chosen setting 20 7 290Hours in the highest chosen setting 11 5 236Lowest chosen setting 15 14 299Hours in the lowest chosen setting 13 5 236Highest chosen temperature∗Hours 210 105 218(SQRT) Highest chosen temperature∗Hours 14 4 218

Radiator Hours per day radiator on in the living room 16 9 313Hours per day radiator on in bedrooms 24 3 313Hours per day radiator on in the bathrooms 12.71 11.5 230Hours per day radiator on in the attic 10.81 11.05 74Hours per day radiator on in the entrance 5.97 9.98 285Hours per day radiator on in the rest of the house 20 20 313

Grilles Hours per day grilles open in the living room 14 11 313Hours per day grilles open in the bedrooms 16 11 313Hours per day grilles open in the bathrooms and in the attic 5 10 313Hours grilles (total) 80 48 270(log) Hours grilles (total) 1.81 0.41 257

Windows Hours per day windows open in the living room 6 8 124Hours per day windows open in the bedrooms 18 18 268Hours per day windows open in the bathrooms and the attic 9 9 83Hours windows open (total) 21 22 289

Showers (log) Showers in minutes per week 1.67 0.30 303

Baths Number of baths per week 1.35 0.61 313

Use of space Number of bedrooms used as a living area 2.31 0.96 313

Variable Category n %

Ventilation system Ventilation category1:1^3 hours in the lowest setting andthe rest o¡/always o¡

51 16.3

Ventilation category 2:1^3 hours in the highest setting andthe rest o¡

110 35.1

Ventilation category 3:1^3 hours in the lowest setting andthe rest in the highest setting per 24 hours in the highestsetting per half in the lowest and half in the highestsettings

67 21.4

Ventilation category 4: 24 hours in the lowest setting 25 8.0Ventilation category 5:1^3 hours in the highest setting and

the rest in the lowest setting60 19.2

Type of temperaturecontrol

Manual valves on radiators 81 26.0Manual thermostat 79 25.4Programmable thermostat 151 48.6

Ventilation system Mechanical exhaust ventilation 217 92.3Balanced ventilation 18 7.7

Attic as a living area No 257 82.1Yes 56 17.9

Windows open in theliving room

Always closed 189 60.4Open for 2^3 hours 127 39.6

Radiators on in theliving room

Fewer than 5 hours 72 23.06^18 hours 72 23.0More than19 hours 169 54.0

Radiators on inbedrooms

Always closed 117 91.01^18 hours 65 22.8Always open 103 36.1

(Table continued)


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Results: analysis of OTB surveyReported behaviourFour domains of behaviour or preferences weredefined:

. use of the heating system

. use of the ventilation system, windows, and grilles2

. inhabitants’ use of rooms and their presence athome

. showering and bathing frequency

The responses are discussed below.

Use of heating systemThe occupants were asked to report the thermostatsetting per hour on weekdays and at weekends. Themajority (74.4%) reported a preferred highest settingof 19–208C within a sample range of 15–308C. Italso emerged from the reports that a large percentage(61.2%) of respondents kept a lowest setting of 158Cwithin a range of 10–208C (Figure 4). The actual vari-ation in the highest and the lowest setting was not verygreat in the sample, but there were wide variations

in the number of hours that the occupants kept thethermostat at the chosen highest setting for bothweekdays and weekends (Figure 5).

Occupants were also asked about the hours the radia-tors were kept open in each room (Table 3). Abouthalf the respondents (54%) reported that they alwayskept the radiator on in the living room; in the rest ofthe cases the distribution was between zero and 18hours. In all the other rooms most cases fell at eitherzero or 24 hours with few values in the middle.

The obtained data were used as a basis for a set ofvariables that took account of the highest chosenthermostat setting and the number of hours that thissetting was retained. The variable ‘hours radiatorsopen in the bedrooms’ was based on the bedroomwhere the radiator was on for more hours. In mostcases, one or two bedrooms were ventilated for farmore hours than the others, so a normalization per

Table 2 Continued


Radiators on in thebathrooms


Radiators on in theentrance


Windows open in theliving room

Always closed 179 59.11hour 35 11.62^6 hours 56 18.5

More than 7 hours 33 10.9Windows open in the

bedroomsAlways closed 34 11.31^3 hours 99 32.64^20 hours 94 31.0More than 21hours 76 24.8

Note: SQRT, square root; SD, standard deviation.

Table 3 Available energy data per district

District Response fromsurvey

Reported energydata

LeidscheRijn 125 94WateringseVeld 177 147Unknown 11 7Total 313 248

Figure 4 Highest chosen thermostat setting


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number of bedrooms was not the best option for largedwellings with few occupants (Table 2).

Ventilation: windows, grilles andmechanicalventilationMost occupants reported that they opened thewindows in the living room for a few hours a day inthe winter. Only 8.1% kept them open all the time.A larger percentage always kept the windows open inthe bedrooms, attic, and bathrooms. Table 2 showsthe statistics for open windows.

Grilles for air supply were mostly either open or closedall day in all rooms in all dwellings. More than half therespondents said they always kept the grilles open inthe living room and bedrooms. Most respondentssaid that the grilles in the remaining space were keptclosed (bathroom, attic, entrance). Table 3 shows thestatistics for open grilles per room.

The respondents were asked to report the setting of theventilation system (mechanical exhaust or balanced)

per hour. Most said that the ventilation was almostalways off or in the lowest setting and that they increasedthe setting when cooking or taking a shower. Table 3shows the statistics for hours per setting per week.Five main patterns of use were found (Table 2).

Showers and bathsThe users reported the number of showers taken byeach family member per week and the usual duration.The duration in minutes for shower use (for the com-plete household) per week was divided by householdsize. Users also reported the number of times a weekthe bath was used (Table 2). Table 3 presents thevariables used in the analysis.

E¡ect of occupant behaviour on energy useThis section explores the relationships betweenoccupant behaviour and energy use. A variety of stat-istical tests were used according to the characteristicsof the behaviour variables. For parametric variables(normal form), Pearson product-moment correlationcoefficients were used to determine the effect of behav-iour on energy use. Pearson’s r correlation coefficientswere used for continuous but non-normal variables.For dichotomous variables, independent-samplest-tests were used to determine differences in energyuse for different groups. For categorical variables,one-way ANOVA tests were used to determine thedifference on energy use among groups.

A small positive correlation was found between energyuse and number of bedrooms used as a living area;hours in the highest chosen temperature setting;hours with the grilles open (transformed with a base10 logarithm); and hours with radiators on in theliving room, bedrooms, bathrooms, and entrance. Nocorrelations were found at a significance level of 0.05for the highest or lowest temperature setting (forstatistics, see Table 4).

Table 4 Pearson product-moment correlation coe⁄cients for behaviour variables and energy use in theOTB survey

Energy variable Behaviour-related variables Statistica n

(log) Energy for heating (MJ/year) Hours radiators on in the living room r ¼ 0.269∗∗∗ 221Hours radiators on in bedrooms r ¼ 0.205∗∗∗ 215Hours radiators on in the bathrooms r ¼ 0.193∗∗∗ 176Hours radiators on in the entrance r ¼ 0.174∗∗∗ 215Number of bedrooms used as a living area r ¼ 0.267∗∗∗ 233Hours in the highest chosen temperature setting r ¼ 0.180∗ 176Highest chosen temperature setting n.s.(log) Hours grilles open r ¼ 0.254∗∗∗ 194Hours windows open n.s.Showers in minutes per week per household size n.s.

Notes: ∗, 0.05, ∗∗, 0.01, and ∗∗∗, 0.001; and n.s., not statistically signi¢cant.+, Pearson’s r is used for parametric data; Pearson’s r is used for non-parametric data.

Figure 5 Histograms showing the hours on the highest chosenthermostat setting


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Since the data on gas use include the energy used forheating water, the correlation between the frequencyof showers and baths and energy use was analysedfor dwellings with individual central heating. A smallcorrelation was found between showers in minutesper week and energy use. No correlation was foundwith baths per week.

To analyse the effect of open windows, a dichotomousvariable was created since most households either havethe windows always closed during the winter, or openthem only for a few hours. An independent-samples t-test was conducted to compare energy use for dwell-ings where windows are open for 1–3 hours in theliving room and dwellings where windows are alwaysclosed. A small-effect (h2 ¼ 0.018), positive, statisti-cally significant difference was found with houseswhere windows are open.

The correlations between variables were studied andthe most important variables in each group (thosewhich explain most of the variation in energy use)were introduced into a regression model. These were‘windows open in the living room’ (open window ¼1, closed window ¼ 0), ‘highest chosen temperaturemultiplied per hours’ (transformed with square-rootfor normality), ‘hours radiators on in the rest of thehouse’, and ‘hours grilles open’ (transformed with abase 10 logarithm for normality). Further analysisshowed that ‘hours grilles open’ was not statisticallysignificant in the model. A second model containingonly statistically significant variables was then intro-duced; this explains 11.9% of the variation inenergy consumption in dwellings. Assumptions werechecked to test the accuracy of the model. Theassumption of independent errors (testing the lack ofcorrelation between residuals) was almost certainlymet, with a Durbin–Watson value of 1.653. A 95%confidence interval for B showed that the model wasgood. The model did not seem to have collinearityproblems as the tolerance values and variance inflationfactor (VIF) were within the limits. The analysis ofresiduals statistics had shown that there were nomajor problems with outliers in the model. Thevalues on Cook’s distances were all well below 1;only two cases (1.4%) exceeded three times the lever-age value; 1% of the cases had a large Mahalanobisdistance (a critical Chi-square value of approximately15 for a model of three variables) and less than 2.5%(four cases) were beyond three standard residuals.Covariance ratio and DFBeta statistics were alsorevised and no cases were found that could have astrong influence on the regression parameters. It canbe concluded that the model was fairly accurate. Formore information about the model, see Table 5. Theequation can be summarized as follows.

log Energy for heating ¼ 4.254 + (open windowsin the living room) (–0.130) + (SQRT highest

temperature multiplied per hours) (0.012) +(hours radiators on in the rest of the house) (0.003)

where SQRT is square root.

The model, therefore, indicates that longer periods ona high temperature are related to energy use. The hoursthat radiators are on in spaces other than the livingroom and bedrooms are also an important factor.Radiators in the living room are usually on in allhouses and correlate to the duration of the use of thethermostat; therefore, it does not appear to be signifi-cant in the model. Radiators in bedrooms are usuallyclosed, while the variation in the use of radiators inthe bathroom and entrance is larger across households.Dwellings where windows are closed are related tomore energy use, which contradicts the fact thatmore ventilation should lead to more energy use,although this variable has a small effect on energyuse. This might be caused by the fact that occupantsconsciously ventilating the living room for a fewhours might turn off the heating during these hours.The fact that the mechanical ventilation system isscarcely used in all households and the grilles are alsomostly open in all households may explain the factthat ventilation does not influence the variation inenergy use in the sample.

Determinants of the use of heating and ventilationsystemsInteraction with the environment can affect occupantbehaviour. The type of ventilation system and tempera-ture control can have an indirect influence on choicesand behaviour patterns. The sample had three typesof temperature control: a programmable thermostat,a manual thermostat and manual valves on the radia-tors. The effect of the type of ventilation system isnot reported since both types of system present inthe sample (mechanical exhaust and balanced) are

Table 5 B, standard error ofB, andb of the regressionmodel withbehaviour variables

B Standarderror ofB

b

(Constant) 4.254 0.087Windows are open in the

living room20.130 0.042 20.227∗∗

SQRTHighesttemperature∗Hours

0.012 0.006 0.148∗

Hours radiators on in therest of the house

0.003 0.001 0.193∗∗

Notes: Dependent variable: (log10)Energy for spaceandwater heating inMJ/year.

R2 ¼ 0.119 (percentage of the variation on energy use explainedwith themodel).

∗, 0.05; and ∗∗, 0.01.SQRT, square root.


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controlled in the same manner and therefore norelationship was found with behaviour. Householdcharacteristics that could affect energy use, such asthe presence of children and elderly persons, the typeof previous dwelling, income, household size, andeducation were also analysed.

Relationship between the type of temperature controland occupant behaviourIn this section, Pearson’s Chi-square tests are usedto determine whether different types of temperaturecontrol have an effect on occupant behaviour.Chi-square tests are used because behaviour variableswere non-normal and had to be converted intocategorical variables (for categories, see Table 3).

Pearson Chi-square tests were carried out to test theeffect of the type of temperature control on thehours that radiators were open in different rooms.Statistically significant differences were found for thehours with the radiator open in the living room,bedrooms, bathroom, and entrance (for the statistics,see Table 6). In all cases, a programmable thermostatwas associated with more hours of open radiators.This was followed by a manual thermostat. No statisti-cally significant differences were found for the highesttemperature or fort the hours in this setting among thethree types of temperature control.

Statistically significant differences were found in the useof mechanical ventilation systems for dwellings withdifferent types of temperature control. A connectionemerged between the type of temperature control andcategories of ventilation system. Independent of thetype of control, all households had a preference tokeep the ventilation system on the lowest setting for afew hours and off for the rest of the time. In addition,households with manual thermostats also kept thesystem in the highest setting for a few hours andturned it down the rest of the time. Households withprogrammable thermostats kept a combinationbetween the lowest and the highest setting. The relation-ship between temperature control and behaviour isshown in Figure 6; statistics are shown in Table 6.

Relationship betweenhousehold characteristics andoccupant behaviourThe relationship between the use of heating system andhousehold characteristics was analysed. The householdcharacteristics taken into account were income,education level, household size, the presence of theelderly and children, the presence at home, and thetype of previous house. The statistical tests useddepended on the type of variables. Household charac-teristics were in categories (Table 1), while behaviourvariables were in continuous form. Some behaviourvariables had to be re-coded as categorical variables

using 25% distributions because of their non-normalshape. Independent-samples t-tests were used to deter-mine the differences in behaviour in dichotomoushousehold variables. One-way ANOVA tests wereused for household characteristics with more than twolevels. Pearson’s Chi-square tests were used to determinewhether different behaviour can be observed in differentgroups of households. For statistics, see Table 6.

Household size and income turned out to be uncon-nected to any of the types of behaviour defined forthe use of heating and ventilation systems in thisstudy; although statistically significant differenceswere found on the energy used per type of incomegroup [F(3, 220) ¼ 3.111, p , 0.05] and householdsize [F(4, 66.414) ¼ 4.548, p , 0.05], both with asmall effect size.

An independent-samples t-test uncovered a relationshipbetween the presence of elderly persons in the householdand the number of hours that the thermostat was at thehighest chosen setting. The presence of elderly persons isrelated to more hours using the heating system. Inaddition, Chi-square tests showed that the presence ofelderly persons was associated with fewer hours perday of open windows in the living room and in thebedrooms. The presence of elderly persons was furtherassociated with the use of the ventilation system; theypreferred to keep the system at the highest chosensetting for a few hours and the rest of the time at thelowest chosen setting or off.

A further Chi-square test showed that the presence ofchildren at home was associated with keeping thewindows closed in the living room. The presence ofchildren did not appear to be related to the use ofheating system.

Though no relationship was found between edu-cational level and energy use, statistically significantdifferences were found in hours at the highest chosensetting on weekdays for households from differenteducational levels, albeit with a very small effect size(v ¼ 0.024). A higher education level was related tofewer hours at the highest chosen setting. The presenceof people at home was associated with longer hourswith the heating system at the highest setting andlonger hours with radiators on.

Behavioural patterns in a previous house could influ-ence behavioural patterns in a new house. A t-testshowed that households that had previously lived ina single-family dwelling were more likely to have thethermostat at the highest chosen setting for a longertime than households that had previously lived ina multifamily dwelling. Pearson’s Chi-square testsrevealed that the type of previous house was associatedwith the hours the radiator was on in the living room,the bedrooms, the bathrooms, and the entrance. In all


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Table 6 Statistics fromChi-square tests, independent t-tests, and one-way analysis of variance (ANOVA) tests for variables related tobuilding and household characteristics and behaviour in theOTB survey

(Characteristic variable∗Behaviourvariable) Pearson’s Chi-square

Category Manual valves Manualthermostats

Programmablethermostats

Total

Type of temperature control∗Hoursradiators open in the living roomx2 (4) ¼ 80.227∗∗∗

Always closed 34 17 21 721^18 hours 38 14 20 72Always open 9 48 110 167Total 81 79 151 311

Type of temperature control∗Hoursradiators open in the bedroomsx2 (4) ¼ 20.950∗∗∗


Type of temperature control∗Hoursradiators open in the bathroomsx2 (4) ¼ 17.119∗∗


Type of temperature control∗Hoursradiators open in the entrancex2 (4) ¼ 35.839∗∗∗


Type of temperaturecontrol∗Categories of use of theventilation systemx2 (8) ¼ 23.914∗∗

Ventilation category1 13 22 15 50Ventilation category 2 28 24 59 110Ventilation category 3 10 14 43 67Ventilation category 4 4 10 10 24Ventilation category 5 12 23 25 60Total 67 93 151 331

No Yes Total

Working at home or housework∗Hoursradiator on in the living roomx2 (2) ¼ 13.158∗∗∗

Always closed 61 11 721^18 hours 42 30 72Always open 108 59 167Total 211 100 311

Working at home or housework∗Hoursradiator on in atticx2 (2) ¼ 9.368∗∗


Presence of elderlypersons∗Categories of use of theventilation systemx2 (4) ¼ 19.074∗∗∗

Ventilation category1 35 15 20Ventilation category 2 97 13 110Ventilation category 3 64 3 67Ventilation category 4 23 2 25Ventilation category 5 46 15 60Total 265 47 312

Presence of elderly persons∗Hourswindows open in the bedroomsx2 (4) ¼ 9.668∗

Closed 32 1 331hour 37 4 412^5 hours 60 16 766^20 hours 62 14 76More than 21hours 68 7 75Total 259 42 301

Presence of elderly persons∗Hourswindows open in the living roomx2 (3) ¼ 13.254∗∗

Always closed 70 2 721hour 58 14 72More than 2 hours 137 31 168Total 265 47 312

Multifamily Single family Total

Previous house∗Hours radiators openin the living roomx2 (2) ¼ 6.103∗

Always closed 41 31 72

1^18 hours 39 33 72Always open 70 98 168Total 150 162 312

(Table continued)


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cases, households that had previously lived in a single-family dwelling were more likely to keep the radiatorsopen for a longer time. Figure 6 shows the relationship

between occupant behaviour, the type of temperaturecontrol, and household characteristics. The presenceof a thermostat, single-family houses, the presence at

Figure6 Relationships between the type of temperature control, household characteristics, and use of heating system in theOTB survey

Table 6 Continued

Multifamily Single family Total

Previous house∗Hours radiators openin the bedrooms

Always closed 63 54 1171^18 hours 38 29 65

x2 (2) ¼ 9.037∗ Always open 37 66 103Total 136 149 285

Previous house∗Hours radiators openin the bathroomsx2 (2) ¼ 9.687∗∗


Previous house∗Hours radiators openin the entrancex2 (2) ¼ 10.675∗∗


(Characteristic variable∗Behaviourvariable)

Statistics (t-tests andANOVA)

Mean (SD)

Presence of elderly persons∗Hours atthe highest chosen temperature

t (63.265) ¼ ^2.379∗ Yes,12.5 (3.85)No,10.72 (5.44)

Working at home or housework∗Hoursin the highest chosen setting

t (178.85) ¼ ^4.637∗∗∗ Yes,13.10 (4.15)No,10.09 (5.47)

Previous house∗Hours in the highestchosen setting

t (234) ¼ ^2.075∗ Multifamily,10.23(5.33)

Single-family,11.65(5.13)

Education level∗Hours in the highestchosen setting

F(2, 27.018) ¼ 4.084∗a Lower,13.44 (2.46)Middle,11.02 (5.03)Higher,10.81 (5.46)

Note: ∗, 0.05, ∗∗, 0.01, and ∗∗∗, 0.001; and n.s., not statistically signi¢cant.aTheWelch statistic is reported because theassumption of homogeneity of varianceswasviolated.The statistic is analternative to theF-ratio derived tobe robust when homogeneity of variance has been violated (Field, 2005).ANOVA, analysis of variance.


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home, higher education, and the presence of the elderlyare all related to more hours in the use of radiators andthermostats.

Comparisonwith the WoONdatabaseSince the OTB survey was performed in selected dis-tricts in the Netherlands, the results were comparedwith the WoON database of the Dutch Ministry ofHousing,3 which is assumed to be representative ofthe Netherlands. The WoON database holds dataobtained from a household survey, dwelling inspec-tions, and data on energy use in the dwellings.Similar tests to those conducted with the OTB databasewere carried out as frequently as possible. Both surveyscontained questions about occupant self-reportedbehaviour, but with a few differences, particularlyregarding the use of the heating system. Thesedifferences and their implications for the results arediscussed below. The tests for the comparison werecarried out solely with dwellings built after 1995.The variables for the analysis of the WoON surveyand the differences between the WoON survey andthe OTB survey are shown in Table 7. For descriptivestatistics, see Table 7.

E¡ect of behaviour on energy use (WoONsurvey)This section analyses the relationships between energyconsumption and behaviour using the WoON survey.For these analyses, Pearson’s correlations were usedfor continuous variables and one-way ANOVA forcategorical variables (for statistics, see Table 8). Theresults of Pearson’s correlations showed that theenergy used in a dwelling is positively correlated withthe number of rooms where the radiator is alwayson. In the OTB database this would be comparablewith radiators that are on in different rooms.

A second Pearson’s correlation test showed that higherlevels of energy use are related to a higher thermostatsetting during the night. No correlation was foundbetween thermostat settings and energy use for theOTB database, although correlations were foundbetween energy use and the hours the thermostat ison at the highest chosen setting. This differencemight be due to different methods of data collectionin the surveys. Even though in both surveys respon-dents were asked to report the thermostat setting perhour, the WoON database only contains averageddata on thermostat settings during the day, evening,night and weekends.

A correlation was found between energy use and thenumber of hours that grilles and windows were openin the bathroom. In the OTB database correlationswere found only with the number of hours thatgrilles were open in the dwelling and with open

windows in the living room. No relationship withventilation in the bathrooms was found in the OTBdatabase because only a few dwellings had naturalventilation in the bathrooms. In the WoON surveythe number of hours using mechanical ventilation inthe bathrooms also seemed to be correlated withenergy use. No information on the use of mechanicalventilation in the bathrooms was gathered in theOTB survey because all dwellings had mechanicalventilation with only one control switch for thewhole house, mostly placed in the kitchen.

Determinants on the use of heating and ventilationbehaviour (WoONsurvey)Pearson’s Chi-square and one-way ANOVA tests wereused to determine the relationship between occupants’behaviour, on the one hand, and temperature controland type of ventilation, on the other hand (Table 9).One-way ANOVA tests showed that the type of temp-erature control had an effect on the number of roomswhere radiators were on. The number of rooms withthe radiator on was greater in dwellings with a program-mable thermostat than in dwellings with a manual ther-mostat or manual valves on the radiators. ANOVA testsshowed that the temperature during the night in dwell-ings with a programmable thermostat was statisticallysignificantly higher than in dwellings with a manualthermostat or manual valves on radiators.

Chi-square tests showed that the type of temperaturecontrol was also related to the hours that the venti-lation system was on. Households with a programma-ble thermostat were more likely to use the ventilationsystem for more hours than households with manualthermostats.

A relationship was found for the hours that windowswere open in the bathroom. Households with a pro-grammable thermostat open windows for more hours.

Further analysis of the WoON database showed thathouseholds with a manual thermostat were 5.8 timesmore likely to turn off the heating when grilles wereopen in the living room and 2.3 times more likely toturn off the heating when windows were open in theliving room.

Dwellings with balanced ventilation were associatedwith more hours of ventilation than households withmechanical exhaust ventilation. A Chi-square testshowed that people with mechanical ventilation were2.2 times more likely to turn off the heating if thewindows in the living room were open. Figure 7shows the relationships in the WoON database.

The results from both surveys point at the fact that aprogrammable thermostat is related to longer hourson the use of heating system and more rooms being


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heated. The greater variety of dwelling layouts in theWoON survey (i.e. more cases with windows in bath-rooms) resulted on more insight about the use ofopen windows.

Relationship between household characteristics andoccupant behaviour (WoONsurvey)An ANOVA test showed that households with elderlypersons were more strongly associated with higher

temperature settings than households without elderlypersons. The presence of elderly persons was alsofound to be associated with fewer hours of openwindows in bathrooms. Conversely, it emerged thathouseholds with elderly persons ventilated for morehours via bedroom windows (Table 9).

The presence of children was associated with opengrilles in the bedrooms; households without childrenwere more likely to open the grilles either all day or

Table 7 Descriptive statistics of continuous and categorical variables in theWoononderzoekNederland (WoON) survey

Variable Mean SD n

Household sizea 2.34 1.21 586Number of roomswith a radiator onb 2.68 2.37 564Temperature of the thermostat in the living room, weekdaysb 15.27 3.33 564Showers in minutes per weeka 125.33 112.99 582Showers in minutes per week per household sizea 54.30 41.58 582Temperature setting day (1^5)c 1.87 0.26 344Temperature setting evening (1^5)c 2.04 0.16 339Temperature setting night (1^5)c 1.57 0.25 350Temperature setting weekend (1^5)c 1.99 0.18 332Temperature day in degrees centigradec 18.89 2.62 230Temperature evening in degrees centigradec 20.08 2.15 242Temperature night in degrees centigradec 15.81 2.58 208Temperature weekend in degrees centigradec 19.62 2.11 237Hours per weekmechanical ventilation in the living roomd 79.97 79.21 68Hours per weekmechanical ventilation in the kitchend 55.51 73.80 148Hours per weekmechanical ventilation in bedroomsd 98.58 79.04 43Hours per weekmechanical ventilation in the bathroomd 55.11 74.57 152Hours per week grilles open in the living rooma 52.95 66.14 238Hours per week grilles open in the kitchena 60.77 70.02 157Hours per week grilles open in the bedrooma 80.85 72.18 193Hours per week grilles open in the bathrooma 63.89 76.14 56Hours per week windows open in the living rooma 7.45 22.64 573Hours per week windows open in the kitchena 6.62 23.54 570Hours per week windows open in the bedroomsa 50.24 51.66 576Hours per week windows open in the bathrooma 13.28 34.34 576

Note: SD, standard deviation.


Temperature controla Programmable thermostat 229 39.5Manual thermostat 339 58.4Manual valves on radiators 12 2.1Total 580 100

Ventilation systema Mechanical exhaust 476 82.2Balanced 103 17.8Total 579 100

Heating o¡when ventilation is via grillese Living room 48 43.2Kitchen 30 27.0Bedrooms 93 83.8Bathroom 36 32.4

Heating o¡when ventilation is via windowse Living room 118 43.4Kitchen 66 24.3Bedrooms 228 83.8Bathroom 74 27.2

Type of thermostat usee Almost always on the same programme 116 50.7Programme is sometimes changed 72 31.4Temperature is alwaysmanually set 41 7.0

Notes: aSame in the OTB database.bData on the number of hours radiators are on per room that are available in the OTB database.cSetting per hour is available in the OTB database.dIn the OTB database, all new houses have mechanical ventilation in the kitchen and bathroom.eNot in the OTB database.


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not at all. In addition, households without childrenwere more likely to keep the windows closed in thekitchen, and more likely to open them for a longertime in the bedrooms and bathrooms.

The results for household characteristics and behav-iour are similar for the OTB and WoON surveys. Inboth databases, the presence of elderly persons wasrelated to more hours of use of the heating system orhigher temperature settings. In both surveys, thepresence of the elderly was associated with fewerhours of mechanical ventilation, more hours of openwindows in the bathroom, and more hours of openwindows in the living room. The main differencebetween the surveys is that in the WoON survey house-holds with children were associated with hours ofwindow ventilation. These associations were not stat-istically significant for the OTB survey. Householdswith children were, however, associated with closed

windows in the living room in both surveys. Figure 7shows the determinants of occupants’ behaviour inthe WoON survey.

DiscussionE¡ect of occupants’ behaviour on energy useSmall correlations were found between energy con-sumption and variables relating to hours in the highestand the lowest chosen settings, hours that radiatorsare turned on, and the number of bedrooms used as aliving area. The WoON database showed similar usesof heating systems as the OTB database, the numberof heated rooms, and the hours that the heating wasturned on had an effect on energy use. However, themost important factor in energy use in the OTB data-base, i.e. the hours that the thermostat was at thehighest chosen setting, could not be verified in the

Figure7 Relationshipsbetween the type of temperature control, the type of ventilation, anduseof heating system in theWoononderzoekNederland (WoON) survey

Table 8 Pearson product-moment correlation coe⁄cients for behaviour variables and energy use in theWoononderzoekNederland(WoON) survey

Energy variable Behaviour variables Statistic n

(SQRT) Energy for heating(m3/year)

Number of roomswith a radiator on r ¼ 0.102∗ 545Temperature setting by day n.s.Temperature setting by evening n.s.Temperature setting by night r ¼ 0.163∗ 540Temperature setting by weekend n.s.Hours grilles open in the bathroom r ¼ 0.226∗ 56Hours windows open in the bathroom r ¼ 0.148∗∗∗ 576Hours per weekmechanical ventilation in the bathroom r ¼ 0.227∗∗ 152Type of thermostat use F(2, 221) ¼ 5.014∗∗ 222

Notes: ∗, 0.05, ∗∗, 0.01, and ∗∗∗, 0.001; n.s., not statistically signi¢cant.SQRT, square root.


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Table 9 Statistics fromChi-square tests, independent t-tests, and one-way analysis of variance (ANOVA) tests for variables related tobuilding and household characteristics and behaviour variables in theWoONsurvey

(Characteristic variable∗Behaviour variable)Pearson’s Chi-square

Category Programme Manual Total

Type of temperature control∗Hours per weekmechanical ventilation in the living roomx2 (1) ¼ 14.460∗∗∗

Fewer than15 hours 5 22 27More than16 hours 22 12 39Total 27 34 66

Type of temperature control∗Hours per weekmechanical ventilation in the kitchenx2 (3) ¼ 27.176∗∗∗

Fewer than 3 hours 3 35 384^7 hours 8 29 378^167 hours 12 17 29Always 23 15 38Total 46 96 142

Type of temperature control∗Hours per weekmechanical ventilation in bedroomsx2 (1) ¼ 8.337∗∗

Fewer than167 8 12 20hours 15 6 21Always 23 18 41Total

Type of temperature control∗Hours per weekmechanical ventilation in the bathroomx2 (3) ¼ 31.415∗∗∗

1hour 4 32 362^5 hours 7 21 286^124 hours 9 26 35More than125 hours 31 16 47Total 51 95 146

Type of temperature control∗Hours windowsopen in bathroomx2 (1) ¼ 3.894∗

Fewer than 6 hours 166 265 431More than 7 hours 62 65 127Total 228 330 558

Mechanical Balanced Total

Ventilation system type∗Hours per weekmechanical ventilation in the living roomx2 (1) ¼ 9.010∗∗

Fewer than15 hours 26 7 33More than16 hours 14 21 35Total 40 28 68

Ventilation system type∗Hours per weekmechanical ventilation in the kitchenx2 (3) ¼ 11.389∗∗

1hour 117 8 1252^5 hours 98 4 1026^124 hours 129 6 135More than125 hours 114 23 137Total 458 41 499

Ventilation system type∗Hours per weekmechanical ventilation in the bathroomx2 (3) ¼ 8.910∗


Ventilation system type∗Heating o¡whenventilation is via grillesx2 (1) ¼ 4.048∗

No 120 33 153Yes 101 13 114Total 221 46 267

No Yes Total

Presence of the elderly∗Hoursper weekwindowsopen in the bedroomsx2 (1) ¼ 25.660∗∗∗

Fewer than10 hours 174 58 23211^96 hours 109 49 158More than 97 hours 115 71 186Total 398 178 576

Presence of the elderly∗Hoursper weekwindowsopen in the bathroomx2 (2) ¼ 8.390∗


Presence of children∗Hoursper weekgrilles openin the bedroomx2 (3) ¼ 7.845∗


Presence of children∗Hours per week windowsopen in the kitchenx2 (2) ¼ 8.302∗

Closed 273 82 3551^6 hours 83 37 120. 7 hours 60 35 95Total 416 154 570

(Table continued)


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WoON database because of the nature of the data. Anassociation between the energy used and the tempera-ture setting during the night in the WoON databasedid, however, give an indication of the same parameter.

The results seem to be in accordance with the results ofother studies that found correlations between energyconsumption and choice on temperature setting(Jeeninga et al., 2001; Haas et al., 1998; Lindenet al., 2006; Hirst and Goeltz, 1985). Linden et al.(2006) found that a large proportion (38%) of house-holds lowered the temperature at night. Haas et al.(1998) found a relationship between indoor tempera-ture and energy use. The results from Jeeninga et al.(2001) indicated that most households (73%) keepthe indoor temperature at 18–208C, which weresimilar findings to those of the OTB survey.

Thermostat use emerged as an important factor onoccupant’s behaviour: households with a program-mable thermostat were associated with highertemperature settings during the night in the WoONsurvey, and with more hours with radiators on in theOTB survey. Nevius and Pigg (2000) found thathouseholds with programmable thermostats reportedslightly higher settings than households with manualthermostats, although the differences were not

statistically significant. Similar results were obtainedin the WoON survey, where the differences werefound to be statistically significant, though with asmall effect. Shipworth et al. (2010) found that the useof temperature controls did not reduce averagemaximum temperatures or hours of use, althoughhouseholds with programmable thermostats werefound to use the heating for 0.4 hours longer thanhouseholds with manual thermostats, the differenceswere not statistically significant. Shipworth et al. alsofound that households without a thermostatic controlset the temperature 0.68C lower than households withcontrol, though the difference was not statisticallysignificant. On the contrary, Conner and Lucas (1990)found that households with programmable thermostatsset the temperature an average of 0.58F lower thanhouseholds with manual thermostats, though also notstatistically significant. For cooling, Lutzenhiser (1992)found comparable results: houses with a manual ther-mostat use 21% less energy for cooling than houseswith programmable thermostats. In the OTB survey,statistically significant differences were found for thehours than radiators are on, but not for the hours thatthe heating system is used. Therefore, the results fromthe OTB survey are in agreement with past studiesand offer additional information on the effect of differ-ent types of temperature control on the use of radiators.

Table 9 Continued

No Yes Total

Presence of children∗Hours per week windowsopen in the bathroomx2 (2) ¼ 11.757∗∗


Presence of children∗Hours per week windowsopen in the bedroomsx2 (1) ¼ 32.179∗∗∗

Fewer than10 hours 172 60 23211^96 hours 100 58 158More than 97 hours 148 38 186Total 420 156 576

(Characteristic variable∗Behaviour variable) Statistics (t-tests andANOVA)

Mean (SD)

Type of temperature control∗Number of roomswith a radiator on

F(3,16.825) ¼ 16.841∗∗∗a None,4.17 (7.16)Programmable, 3.13 (2.46)Manual, 2.29 (2.05)Manual valves,0.80 (1.03)

Type of temperature control∗Setting by night F(3, 346) ¼ 4.464∗∗ Manual valves on radiators,1.53(0.31)

Manual thermostat,1.53 (0.25)Programmable thermostat,1.61

(0.22)Presence of the elderly∗Temperature setting day t (163.149) ¼ ^5.703∗∗∗ Yes, 20.18 (2.17)

No,18.30 (2.61)Presence of the elderly∗Temperature setting

eveningt (129.652) ¼ ^2.741∗∗ Yes, 20.66 (2.17)

No,19.84 (2.10)Presence of the elderly∗Temperature setting

weekendt (235) ¼ ^3.674∗∗∗ Yes, 20.36 (1.58)

No,19.29 (2.23)

Notes: ∗, 0.05, ∗∗, 0.01, and ∗∗∗, 0.001; and n.s., not statistically signi¢cant.aTheWelch statistic is reported because the assumption of homogeneity of variances was violated.TheWelch statistic isan alternative to the F-ratio derived to be robust when homogeneity of variance has been violated (Field, 2005).ANOVA, analysis of variance; SD, standard deviation.


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In this study significant statistical correlations werefound between energy use and the frequency oftaking showers, but no correlation was foundbetween energy use and bath frequency. This contra-dicts the results of Jeeninga et al. (2001) in whichbath frequency was an important contributor toenergy use. In the present sample, households did notuse the bath often, which may be related to the typeof households living in the districts under study.

Jeeninga et al. (2001) found that 20% of householdsventilated all rooms every day by opening windowsand that bedrooms were more ventilated than otherrooms. Similar results were found in the OTB survey.

Statistically significant differences were found betweenthe energy used in dwellings where windows in theliving room were open and dwellings where windowsin the living room were always closed. This suggestsa relationship between ventilation in the living roomand lower energy use. Open grilles and windowsseemed to be important in both databases. Householdstended to ventilate more often with grilles andwindows than with a mechanical ventilation system.This corresponds with Iwashita and Akasaka (1997),who found that 87% of the total active (windowsand grilles) air change was related to behaviour.

The behaviour observed in the OTB survey also seemsto be in accordance with Ernhorn (1988), who found ahigher frequency for natural ventilation in bedrooms,followed by children’s bedrooms and living rooms. Alarge variation was also found in bedrooms, but notin the living rooms.

Maier et al. (2009) found that mechanical ventilationwith heat recovery reduced energy use by 10–30% inlow-energy houses in Germany. Maier et al. also ascer-tained a poor relationship between heat consumptionand hours of open windows in dwellings withnatural, mechanical, and heat-recovery ventilation.This finding is contrary to the findings for the OTBand WoON databases where no relationship wasfound between mechanical systems and energy use,but a relationship was found between energy use andnatural ventilation. The difference might be explainedby occupants in the OTB survey using natural venti-lation more often in combination with an inappropri-ate use of the mechanical ventilation systems (theywere only scarcely used).

RegressionmodelA statistical regression model using occupant behaviourcan predict 11.9% of the variation in energy use. Theincrease to 11.9% in the current study shows anincrease in predictability with behaviour variablesalone. In a previous study (Guerra Santin et al. 2009)only 4.2% of the variation in energy use was explained

by household characteristics and occupant behaviour(only a part of this was explained by behaviour).

The relatively small size of the OTB sample might haveprevented the emergence of other small correlationswith energy use. Even so, it would be difficult to findone predictor that was more important that theothers, as energy use could be affected to a smallextent by many different factors.

Determinants of behaviour: type of heating controlventilation and household characteristicsTemperature control turned out to be a very importantfactor in the use of heating and ventilation systems.Dwellings with a programmable thermostat wereassociated with more hours of heating system use,followed by dwellings with a manual thermostat. Avalidation conducted with the WoON databaseconfirmed and provided more insight into therelationship between the type of temperature controland behaviour. Temperature control was found to beassociated with the number of rooms where theradiator was turned on. The relationship indicatedthat more radiators were turned on in dwellings witha programmable thermostat. A programmablethermostat for temperature control was also associatedwith a higher temperature during the night. The analy-sis of the WoON database further confirmed thattemperature control was related to the ventilation ofthe dwelling. Households with a programmablethermostat were more likely to open windows for alonger time. It further emerged that households witha manual thermostat were more likely to turn off theheating system when windows or grilles were open.

Jeeninga et al. (2001) found that the type of thermostathad no influence on the preferred setting. However, theOTB survey did find a relationship between the type ofthermostat and the hours that radiators were on, whilethe WoON survey found a relationship between thetype of temperature control and the temperature atnight. Again, differences might rise from differences inthe variables used (hours of thermostat use instead oftemperature settings). Jeeninga et al. also found thathouseholds with a manual thermostat tended to favoura lower setting if no one was at home. The WoONsurvey indicated that households with a manualthermostat turned off the heating when windows wereopen. Both results point to a relationship between amanual thermostat and energy-saving behaviour.

Ventilation type turned out to be related to behaviouronly in the WoON database because the low number ofcases with balanced ventilation in the OTB databasecaused problems with assumptions in the tests. Accord-ing to the results of the WoON survey, balanced venti-lation was associated with more hours of ventilation


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and a lower probability that the heating would beturned off when the living-room windows were open.

In the OTB database, a relationship was foundbetween the presence of elderly persons and behaviour.Households with elderly persons kept the heating atthe highest chosen setting for longer and kept theradiators turned on for more hours. This behaviourmight be caused by elderly people spending more timeat home. They also opened the living-room andbedroom windows for fewer hours, while householdswith children were more likely to keep the living-roomwindows closed. Similar results were found in theanalysis of the WoON database; the only differencewas that small-effect, statistically significant differenceswere found for the hours that bedroom and bathroomwindows were open in households with children, whichwere not statistically significant in the OTB survey.

ConclusionsThe aim of this study was to determine the influence ofoccupant behaviour when determining energy use indwellings built after the introduction of the Dutchenergy performance requirements, and to clarify theunderlying relationships between behaviour, HVACsystems, and household characteristics.

Occupant behaviour and energy consumptionObserved behaviourSmall variations in the highest chosen thermostatsetting were found in the sample, with most occupantsreporting a top setting of 19–208C. Wide variationswere found for the hours the occupants kept the ther-mostat at the highest chosen setting. There seemed tobe greater differences in choice for the use of radiators.Half the respondents always kept the radiator on in theliving room. Most respondents reported either zero or24 hours for all other rooms.

The majority of respondents said that the living roomwindows were open during the winter or closed foronly a few hours. A larger percentage always keptthe windows open in the bedrooms, attic, and bath-rooms. Grilles were usually either open or closed allday in all rooms. More than half the respondents saidthey always kept the grilles open in the living roomand bedrooms. In the rest of the space the grilleswere closed in most cases. Most respondents reportedthat the ventilation system was either almost alwaysoff or at the lowest setting and that they turned upthe setting when cooking or taking a shower.

Influence of occupants’ behaviour on energy useThe number of hours that the heating system was on atthe highest chosen setting appeared to have a stronger

effect on energy consumption than the highest chosensetting as such, a factor that has been usually con-sidered in other studies. Low correlations were foundbetween energy use and the ventilation system, sincemost households kept the ventilation system off orat the lowest level. Window and grille ventilationseemed to have a stronger effect on energy use.

Influence of building characteristics on the use ofheating and ventilation systemsThe type of temperature control seemed to have an effecton the use of heating and ventilation systems.Householdswith a programmable thermostat were more likely tokeep the radiators turned on for more hours than house-holds with a manual thermostat or manual valves onradiators. Households with mechanical exhaustventilation also tended to turn off the heating when ven-tilating via windows, while households with balancedventilation tended to use the ventilation system formore hours. Therefore, households with manualthermostats and mechanical ventilation tend to a moreenergy-conserving behaviour than households withprogrammable thermostats and balanced ventilation.

Influence of household characteristics on the use ofheating and ventilation systemsThe presence of elderly persons in the householdproved to be a determining factor in the use of theheating system and ventilation; the heating was onfor more hours and there were fewer hours of venti-lation in households with elderly persons. The effectof the presence of the elderly on behaviour might beclosely related to the hours present at home. The pres-ence of children seemed to be related to the use of ven-tilation; households with children tended to ventilateless than households without children.

MethodologyThe data from two surveys were analysed in thisresearch: the survey of the OTB Research Instituteand the WoON database of the Dutch Ministry ofHousing. In both questionnaires the respondents hadto fill tables with information on how they usedheating systems. This generated very detailed data.Despite the similarities in the method of data collec-tion, the databases from the two surveys containeddifferent types of variables for synthesizing the dataobtained from the questionnaire. In the WoON data-base, the variables consisted of the average tempera-ture setting during four specific periods: day, evening,night, and weekends. Previous analysis of the KWRdatabase – the predecessor of the WoON survey(Guerra Santin et al., 2009) – had highlighted theimportance of temperature settings in the housingstock. For the OTB database, lower variations ontemperature settings were assumed because the


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thermal properties of the building stock had improvedafter 1995, and therefore a decision was taken to usethe highest and lowest chosen temperature instead ofusing average setting values as is common in otherstudies. The number of hours that the temperaturewas kept at the highest and lowest chosen setting wasalso introduced in the database.

The different decisions taken when data from the ques-tionnaires are being processed into the database caninfluence the results of the study. The variables in theOTB database showed a stronger correlation withenergy consumption than the variables in the WoONdatabase. This was probably due to data loss duringaveraging of the values. The size of the WoON samplemight have led to the use of variables that containedfar less data than the data collected in the survey.

Accurate energy-user profiles cannot be built withoutdetailed data on the use of heating and ventilationsystems. However, too much or too detailed data aresometimes difficult to process and analyse and might,therefore, compromise the quality of the data in thedatabases, since important information could be lost.Data collection should focus on aspects of behaviourthat tend to vary more widely across the populationand that seem to have a stronger influence on energy use.

Recommendations for policy and practiceAlthough no statistically significant differences werefound on the use of a thermostat in terms of hours ofuse and the highest setting between houses withmanual and programmable thermostats, a statisticallysignificant difference was found in the use of radiators,indicating that in dwellings with programmable ther-mostats occupants take fewer deliberate actions andleave control to the thermostat itself. This gives anopportunity for improvement, e.g. by implementingmanual thermostats per room. This would give onethe opportunity to maintain a desired temperature perroom, but with enough interaction with the thermostatto achieve deliberate heating only when needed.Another possibility would be to develop another typeof automatic thermostat: reacting to the presencesensors, for instance, instead of pre-programmed time-tables. In addition, the regression model showed thatthe radiators in other rooms than the living room havea significant effect. Therefore, giving feedback to theuser on the impact of their decisions (e.g. having theradiators on at the entrance and corridors) might helpoccupants reduce their energy use.

In the studied sample, almost all households used themechanical exhaust ventilation system and thebalanced ventilation system in a very poor way (i.e.they hardly used it). The use of windows in the livingroom was found to be an important factor for energyuse. This could indicate a need for better and more

occupant-friendly mechanical ventilation systems anda need for better integration within the whole systemof building characteristics, household characteristics,and occupants’ behaviour.

Recommendations for further researchAs a result of wide variations in preferences and life-style, occupant behaviour has emerged as an importantcontributor to energy use in dwellings built after theintroduction of energy performance regulations. Sincedifferences in occupancy patterns seem to have aninfluence on energy use, further research shouldconcentrate on defining user profiles that can help topredict energy use.

The results of this study indicate that the type ofheating and ventilation system has an influence onoccupant behaviour. Further research should seek todetermine whether behaviour is influenced by otherbuilding characteristics. This research focused ondwellings built within a relatively short period and,thus, had similar thermal characteristics and heatingand ventilation systems. Differences in behaviouracross the entire housing stock should be further inves-tigated to determine the influence of a good thermalenvironment on occupant behaviour.

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Endnotes1See http://www.SPSS.com/

2Grilles are small, manually operated openings in walls orwindows for direct outside (natural) ventilation.

3See http://www.vrom.nl/


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Occupants' behaviour: determinants and effects on residential heating consumption