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. Keywords: Occupant behavior; Building energy consumption; Data mining; Cluster analysis; Grey relational analysis The identification of major determinants of building energy consumption, together with a thorough understanding of the impacts of the identified determinants on energy consumption patterns, could assist in achieving the goal of improving building energy performance and reducing greenhouse gas emissions due to the building energy consumption. In general, the factor influencing the total building energy consumption can be divided into seven categories: (1) Climate (e.g., outdoor air temperature, solar radiation, wind velocity, etc.), (2) Building-related characteristics (e.g., type, area, orientation, etc.) (3) User-related characteristics, except for social and economic factors (e.g., user presence, etc.), (4) Building services systems and operation (e.g., space cooling/heating, hot water supplying, etc.), (5) Building occupants' behavior and activities, (6) Social and economic factors (e.g., degree of education, energy cost, etc.), and (7) Indoor environmental quality required.
Zhun et al. analysed the influence of occupant behaviour on building energy consumption and developed a technique for identifying energy-saving potential by changing user behaviour to reduce energy consumption; this is the opposite of the approach described in this paper, in which the building adapts to user behaviour REF .
A systematic procedure to study the influence of occupant behavior on building energy consumption
Energy and Buildings
Engineering Medicine