Leverage of behavioural patterns of window opening and heating set point adjustments on energy consumption and thermal comfort in residential buildings Stefano Paolo Corgnati 1, Simona D’Oca, Valentina Fabi, Rune Korsholm Andersen, Abstract. The current trend in reduction of energy use in buildings is oriented towards sustainable measures and techniques aimed to energy need restraint. Even so, studies have underlined large differences in energy consumption in similar buildings, suggesting strong influence of occupant behaviour. Variability due to occupants’ interactions within buildings is therefore organic. Nevertheless, it is worth noting a lack of knowledge and study of the parameters influencing users’ behaviour and their way of life. Existing dynamic energy simulation tools exceed the static size of the simplified methods through a better and more accurate prediction of energy use; however they are still unable to replicate the actual dynamics that govern energy uses within buildings. Furthermore, occupant behaviour is currently described by static profiles, based on assumptions and average values of typical behaviour, which do not necessarily reflect reality accurately. The pursuit of a comfort condition in indoor environment is a result of complex correlation between different parameters and users’ personal sensitivity. As a consequence, a need for always more accurate statistical occupant behaviour models, considering different behavioural patterns and preferences among indoor environmental quality is arising. Final goal of this research is to simulate, in a more accurate way, the variation in actual energy consumption due to human interaction within buildings. In this effort, the study has highlighted which combination of users’ behavioural pattern consists the most energy-saver or energy-waster behaviour in residential buildings.
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Stefano Corgnati () Energy Department, Polytechnic of Turin, Turin, Italy e-mail:
[email protected]
Simona D’Oca, Valentina Fabi Energy Department, Polytechnic of Turin, Turin, Italy ICIEE, Department of Civil Engineering, Technical University of Denmark Rune Korsholm Andersen ICIEE, Department of Civil Engineering, Technical University of Denmark
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Keywords: energy consumption, behavioural pattern, occupant behaviour, statistical modelling
1 Introduction Several studies [5], [9] have been conducted to investigate the influence of different behavioural pattern and user’s level of interaction within building controls on energy consumption in dwellings. In 1983 Van Raaij and Verhallen [8] carried out a study in 145 Dutch dwellings and defined five patterns of energy behaviour (conservers, spenders, cool, warm and average) in relation to the use of heating systems and ventilation habits. The research highlighted that “warm” behavioural pattern users are generally older than the other four, that the educational level of “conservers” was higher than that of “spenders” and that the household size of the “conservers” cluster was smaller than the rest. They found no difference in users’ level of interaction with control systems due to income and employment. In 2001, Jeeninga et al. [6] showed that the household energy consumptions in completely identical apartments can vary as much as 100% only considering user actions forced in order to maintain a state of comfort in their homes. In 2005 a survey conducted by Portinga and others [7] among households in the Netherlands investigated the acceptability level of different energy-saving measures. They found that different socio-demographic groups and people with different environmental concerns preferred different type of energy-saving measures. Surprisingly, results of the research showed that senior, singles and low-income families were less willing to apply energy-saving measures at home. Andersen et al. 2011 [1] conducted a field survey of occupant behaviour and control of indoor environment in 15 Danish dwellings. Heating set point preferences were monitored and further analyzed by means of multiple logistic regressions to infer models of occupants’ interactions with building controls. The presented literature confirms that occupant behaviour can invalidate accurate prediction of building energy consumption. In order to get results close to reality, a further step in the research toward the focus on users’ level of interaction within building control systems is here presented. Occupant behaviour is known as the results of an interaction between physical parameters such as indoor and outdoor conditions and psychological, physiological and contextual variables connected to users’ preferences and sensitivity [4]). In most building energy performance simulations, this complex frame is inadequately described by fixed and deterministic values, resulting in discrepancies between real and simulated performance indicators. Occupant behaviour could be simulated by probabilistic models based on indoor and outdoor conditions inferred from actual occupant behaviour.
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2 Method The purpose of this research was to investigate the influence of different behavioural patterns of window opening and heating set point adjustments on energy consumption and thermal comfort in residential buildings. Models were developed following an incremental philosophy, switching from standardized and deterministic methodologies, toward a probabilistic approach in energy modelling, as synthetically described in the following flow chart (figure 1) and explained hereafter. Specifically, models considering both occupants interaction as probabilistic input have been developed and implemented in the energy simulation software IDA Ice [10]. Behaviour patterns for active, medium and passive occupant’s typologies were combined and subsequently merged in order to simulate the variation in actual energy consumption, due to human interaction within building controls. The aim of the study was to investigate which combination of users’ level of interaction represents the most energy saving or energy-wasting behaviour in residential buildings.
Fig. 1 Flow chart explaining the methodology of the study
2.1 Window opening and closing In the attempt to group behavioural patterns in dwellings, we at first focused on the analysis of the similarity in influential variables only for window opening and
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closing. A first probabilistic model was build in the simulation software IDA Ice, starting from a previous developed model [3]. In this study, Fabi et al. analyzed the Danish dwelling data set by means the statistical analysis software R and grouped it on the basis of commons patterns of behaviour emerged. Interestingly, the total number of window opening enormously varies from dwelling to dwelling, in which window has been opened from one time up to 718 times during the whole period of monitoring survey.
2.2 Thermostat adjustments Following the same methodological approach, the study has further focused on the definition of behavioural patterns for heating set point manipulation. Data proceeded from the same monitoring campaign [2] showed large disparity between individual heating set point settings. Findings can be seen as confirmation of human subjective perception of indoor thermal climate and comfort preferences affecting prevision in controlling indoor temperature. Differences in thermostat set point adjustments from dwelling to dwelling could be a result of variation in occupants’ sensitivity to the variables governing their behaviour. In order to overcome the high complexity of the issue, dwellings were grouped after their inhabitants’ frequency of thermostats manipulation and then named as Active, Passive and Medium user type. Moreover, the probability of turning up/down the thermostat was inferred for three separated behavioural models.
2.3 Hybrid modelling The leverage of occupants’ level of interaction with personal control on energy consumption in dwellings has been tested by means Hybrid models, accounting as statistical input both the user interaction with window opening and heating set point adjustments. At a first step, starting from the statistical analysis three models have been developed, by combing the same level of interaction for both window operations and thermostats adjustments for Active, Medium and Passive users. Some contradictions and simplifications still exist in this modelling approach, thus users’ willingness in reaching a certain level of comfort could be different regarding window operation and heating set point adjustments. Starting from this assumption a new conceptual methodology was developed. As a matter of fact, occupant behavioural patterns related to active, medium and passive users on windows opening and closing not necessarily comply in the reality with the same level of inhabitant interaction with thermostats. For this reason each of three behavioural models of window control previously developed has been matched in to a macro with the three concerning thermostat adjustments. Accordingly, a total of nine models were implemented, covering all possible combinations between them. As a consequence, by selecting randomly (30 times each) different behavioural pattern combinations, it will be feasible to replicate as much as possible the influ-
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ence of different behavioural pattern of window opening and heating set point adjustments on energy consumption and thermal comfort in residential buildings.
3 Results The most influential variable highlighted for turning up the thermostat is generally the outdoor temperature. As a matter of fact, the probability of turning up the thermostat is a function of outdoor temperature: the probability of turning up the thermostat rises when the outdoor temperature decreases. This finding is specifically related to active users (p
