A systematic procedure to study the influence of occupant behavior on building energy consumption

Abstract

Efforts have been devoted to the identification of the impacts of occupant behavior on building energy consumption. Various factors influence building energy consumption at the same time, leading to the lack of precision when identifying the individual effects of occupant behavior. This paper reports the development of a new methodology for examining the influences of occupant behavior on building energy consumption; the method is based on a basic data mining technique (cluster analysis). To deal with data inconsistencies, min-max normalization is performed as a data preprocessing step before clustering. Grey relational grades, a measure of relevancy between two factors, are used as weighted coefficients of different attributes in cluster analysis. To demonstrate the applicability of the proposed method, the method was applied to a set of residential buildings' measurement data. The results show that the method facilitates the evaluation of building energy-saving potential by improving the behavior of building occupants, and provides multifaceted insights into building energy end-use patterns associated with the occupant behavior. The results obtained could help prioritize efforts at modification of occupant behavior in order to reduce building energy consumption, and help improve modeling of occupant behavior in numerical simulation.

Impact of occupant behaviour on the energy-saving potential of retrofit measures for a public building in the UK

“…Occupant behaviour in buildings, e.g. heating behaviour, window opening behaviour, has been suggested to be one of the main aspects causing this gap (de Wilde 2014), due to its great impact on the building performance, which has been demonstrated in a number of existing studies using data from either real buildings (Mulville, Jones, and Huebner 2013;Yu et al 2011;Gram-Hassen 2010;Gill et al 2010;Brown, Dowlatabadi, et al 2009;Bağcı 2009;Guerra Santin, Itard, and Visscher 2009;Steemers and Yun 2009;Hass, Auer, and Biermayr 1998) or building simulation (Bonte, Thellier, and Lartigue 2014;Lee and Malkawi 2014;Mavrogianni et al 2014;Roetzel 2014;Silva and Ghisi 2014;de Meester et al 2013;Fabi, Andersen, and Corgnati 2013). These studies, however, focused on the building design stage and operation stage, while the impact of occupant behaviour in the building refurbishment stage still requires further exploration, especially its impact on the predicted energy saving potential of refurbishment measures by dynamic building performance simulation.…”

An application of data mining in district heating substations for improving energy performance

“…In the past several decades, it has been successfully applied in economics, retails, telecommunication, and financial services [4]. Recently, efforts have also been made to investigate the application of data mining in HVAC field, including building energy consumption prediction [5,6], building energy management [7,8], fault detection and diagnosis [9,10], and occupant behaviour [11,12].…”

The indispensability of good operation & maintenance (O&M) manuals in the operation and maintenance of low carbon buildings

“…The need for end-users to be guided through the workings of the unconventional systems cannot be overemphasised. Literature sources have indicated that occupants behaviour or 'people factors' as described by James E. Piper, bear greatly on the building performance (Yu et al, 2011;Masoso and Grobler, 2010;Piper, 1999). Yu et al (2011) reported a study by Ouyang and Hakao (2009) which investigated energy-saving potential by improving user behaviour using 124 households in China.…”

“…Literature sources have indicated that occupants behaviour or 'people factors' as described by James E. Piper, bear greatly on the building performance (Yu et al, 2011;Masoso and Grobler, 2010;Piper, 1999). Yu et al (2011) reported a study by Ouyang and Hakao (2009) which investigated energy-saving potential by improving user behaviour using 124 households in China. The 124 houses were divided into two groups; one group was educated to promote energy-conscious behaviour and adopt corresponding energy saving measures, while the second group not educated, they were expected to keep the status-quo.…”