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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. [Copyright &y& Elsevier]

Abstract: The evaluation of building energy consumption usually requires building energy profiles on an hourly basis. Computer simulations can be used to obtain this information but generating simulations requires a significant amount of experience, time, and effort to enter detailed building parameters. This paper presents a simple methodology to estimate hourly electrical and fuel energy consumption of a building by applying a series of predetermined coefficients to the monthly energy consumption data from electrical and fuel utility bills. The advantage of having predetermined coefficients is that it relieves the user from the burden of performing a detailed dynamic simulation of the building. The coefficients provided to the user are obtained by running EnergyPlus Benchmark Models simulations; thus, the simulation process is transparent to the user. The methodology has been applied to a hypothetical building placed both in Atlanta, GA, and in Meridian, MS, and in both cases, errors obtained for the estimated hourly energy consumption are mainly within 10%. [ABSTRACT FROM AUTHOR]