Analysis of occupants' behavior related to the use of windows in German households

Calı̀, Davide; Andersen, Rune Korsholm; Müller, Dirk; Olesen, Bjarne W.

doi:10.1016/j.buildenv.2016.03.024

Cited by 125 publications

(63 citation statements)

References 26 publications

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“…The results of this study support the recommendations of others (e.g. [19,21,22]) that when investigating occupant behaviour in buildings, other drivers than just indoor and outdoor temperature should be taken into account.…”

Section: Discussionsupporting

confidence: 87%

“…For the purpose of this paper, multivariate logistic regression was used to establish the probability of opening or closing the main bedroom window, based on multiple indoor and outdoor environment variables. This statistical method has recently been used by Cali et al [21] for modelling window opening and closing in German households, Shi and Zhao [22] for Chinese households, and…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have identified the key factors that influence occupants' window opening behaviour in buildings (e.g. [17][18][19][21][22][23][24][25][26][27][28][29][30][31][32][33][34]). A detailed international review and discussion of these factors is provided by Fabi et al [20].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings

Jones

Fuertes

Gregori

et al. 2017

Building and Environment

100

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This paper presents the development of stochastic models of occupants' main bedroom window operation based on measurements collected in ten UK dwellings over a period of a year. The study uses multivariate logistic regression to understand the probability of opening and closing windows based on indoor and outdoor environment factors (physical environmental drivers) and according to the time of the day and season (contextual drivers). To the authors' knowledge, these are the first models of window opening and closing behaviour developed for UK residential buildings. The work reported in this paper suggests that occupants' main bedroom window operation is influenced by a range of physical environmental (i.e. indoor and outdoor air temperature and relative humidity, wind speed, solar radiation and rainfall) and contextual variables (i.e. time of day and season). In addition, the effects of the physical environmental variables were observed to vary in relation to the contextual factors. The models provided in this work can be used to calculate the probability that the main bedroom window will be opened or closed in the next 10 minutes. These models could be used in building performance simulation applications to improve the inputs for occupants' window opening and closing behaviour and thus the predictions of energy use and indoor environmental conditions of residential buildings.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

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Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings

Jones

Fuertes

Gregori

et al. 2017

Building and Environment

100

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“…As stated by Carbonare [3], the correct characterization of the user-dependent variables in residential buildings plays a huge role to obtain improvements on the current state-of-the-art. We define a methodology for future research and will be tested on two variables (window opening and indoor temperature), which were already studied by many researchers on the field of building simulation [4][5][6]. For reasons of practicality, only the window opening variable will be described, and in the case of the indoor temperature only the results will be presented.…”

mentioning

confidence: 99%

Clustering the occupant behavior in residential buildings: a method comparison

2018

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This paper aims therefore at providing the first steps for a user-orientated control system for mechanical ventilation. As stated by Carbonare [3], the correct characterization of the user-dependent variables in residential buildings plays a huge role to obtain improvements on the current state-of-the-art. We define a methodology for future research and will be tested on two variables (window opening and indoor temperature), which were already studied by many researchers on the field of building simulation [4][5][6]. For reasons of practicality, only the window opening variable will be described, and in the case of the indoor temperature only the results will be presented. BackgroundClustering is the process of classifying data into different groups, aiming at finding similarities among them. A cluster is then defined as a subset of objects in the database that belong to the same group. Similarity is often calculated through distance measures. The main challenges of a clustering process are [7]:The aim of this paper is to investigate possible patterns of the occupant behavior in residential buildings. Measurements were taken in multi-family buildings where several occupant-related variables were recorded. We chose and compared two different clustering methods: whole time series and features clustering (k-means algorithm). The mentioned methods were performed selecting two variables (window opening and indoor temperature) and tested with supervised learning methods. Results suggest that features clustering can perform better than whole time series. The representation of the occupant behavior through features is meant to be applied in future work regarding the optimization of control strategies in ventilation systems. Eingruppierung des Nutzerverhaltens inWohngebäuden: ein Me thodenvergleich. Ziel dieser Arbeit ist es, mögliche Muster des Nutzerverhaltens in Wohngebäuden zu erkennen. Von einem Mehrfamiliengebäude wurden zunächst Daten, die auf das Nutzerverhalten schließen lassen, über einen Zeitraum von zwei Jahren erfasst. Zur Auswertung wurden zwei ClusteringMe thoden verglichen: das Clustering von Zeitreihen und die Features ClusteringMethode (mittels kMeansAlgorithmus). Die gen annten Methoden wurden hauptsächlich mit zwei Variablen (Fens teröffnung und Innentemperatur) durchgeführt, und mit Machine LearningMethoden getestet. Die Ergebnisse weisen auf eine bessere Performance der Features ClusteringMethode gegenüber dem Clustering von Zeitreihen hin. Die Darstellung des Nutzerverhaltens durch Feature Clustering wird in einer zukünftigen Arbeit als Teil einer Optimierung der Regelungsstra tegien von Lüftungsanlagen genutzt.

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“…Alguns dos motivos podem ser: a) a incerteza na definição do modelo físico do edifício no programa de simulação, b) a deficiência do arquivo climático, ou c) incertezas na hora de modelar o comportamento do usuário na edificação (CALÌ et al, 2016). Tem havido muitos esforços para aprofundar e melhorar os modelos físicos e, também estudos que procuram refinar os dados climáticos.…”

Section: Críticasunclassified

Simplificações na modelagem de habitações de interesse social no programa de simulação de desempenho térmico EnergyPlus

Gil¹

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S i m p l i f i c a ç õ e s n a m o d e l a g e m d e h a b i t a ç õ e s d e i n t e r e s s e s o c i a l n o p r o g r a m a d e s i m u l a ç ã o d e d e s e m p e n h o t é r m i c o E n e r g y P l u s S Ã O C A R L O SEsta HIS é modelada no EnergyPlus de duas formas: como um modelo multizona (MuZ) e como um modelo monozona (MoZ), em que toda a habitação é considerada apenas como uma única zona térmica. O impacto da simplificação das zonas térmicas é avaliado em dois estudos que consideram: 1) vários horários para abertura e fechamento de portas internas, e 2) diferentes geometrias e distribuições internas para os ambientes. Em ambos os estudos, os resultados mostram que a diferença absoluta horária da temperatura entre os modelos MoZs e MuZs é significativamente baixa para todos os casos considerados, estando abaixo de 0,4ºC mais de 50% do tempo. As maiores diferenças encontradas entre os MoZs e MuZs são obtidas nos modelos simulados nos climas mais frios, nos modelos nos quais as portas internas são consideradas fechadas e nos ambientes menores com uma exposição à radiação solar mais reduzida. As diferenças anuais mínimas e máximas da temperatura interna do ar entre os MoZ e MuZ são notadamente elevadas. No entanto, estes valores são observados num dia e uma hora específica, sendo a média anual significativamente baixa para todos os casos. Palavras-chave:

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Analysis of occupants' behavior related to the use of windows in German households

Cited by 125 publications

References 26 publications

Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings

Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings

Clustering the occupant behavior in residential buildings: a method comparison

Simplificações na modelagem de habitações de interesse social no programa de simulação de desempenho térmico EnergyPlus

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