Modeling energy consumption in a educational building: Comparative study between linear regression, fuzzy set theory and neural networks

Abstract

Quantifying the impact of energy saving measures on a given space requires representative models that can describe how energy is consumed in that space with dependence on known input variables. For this purpose, it is commonly accepted that linear regressions can be used to define those models, named energy consumption baselines. In this paper, we want to assess the performance of linear regressions to model electricity consumption compared to other modeling techniques that can capture nonlinear dynamics like fuzzy and neural networks models in three experimental places in a Portuguese University campus: a set of offices in a department, a classroom amphitheater and the library. Five input variables were defined for the study: day type, occupation, day length, solar radiation and heating and cooling degree days. The novelty of this paper is the comparative assessment between these different modeling techniques, which are usually addressed individually in the literature. From the results obtained in this research, we can outline the importance of selecting representative input variables, study their inter relation, fine tuning the models, and analyze the different models when being trained and tested. We generally conclude that neural networks have the best performance values, fuzzy models increase their performances when trained with varying epochs (with the exception of the amphitheater, where the model over fits and so as the testing performance) and linear regressions present the lowest performance. Hereupon, we discuss the encouragement of applying non-linear models such as the presented ones rather than traditionally used linear regression models, when evaluating consumption baseline to determine energy savings.