Using pattern recognition to characterise heating behaviour in residential buildings

Abstract

ABSTRACT The understanding of energy-related occupant behaviour and its better reproduction in building energy analysis has recently become a primary field of interest. The combination of computing ease and the availability of big streams of high resolution building performance data enhance the ability to study this behaviour. In this context, data mining methods are increasingly employed. The aim of this study is to propose a data mining methodology for the characterization of heating behaviour in residential buildings. The methodology consists of two multivariate statistical analysis methods, namely Principal Component Analysis (PCA) followed by cluster analysis. The methods were applied on monitored gas consumption data of five dwellings in Italy. Findings support literature indicating that people heat their homes in different ways. It was found that households do not always follow a different heating schedule on weekends, have very different temperature preferences and operate the heating system at different hours during the day. In fact, some households may change heating practices over the heating season. The highlight of the proposed methodology is the insightful and simple way that PCA can extract succinct information about the heating behaviour of the user.