Energy baseline prediction for buildings: A review

Abstract

Building energy consumption prediction has gotten a lot of attention in the literature since it is a complicated process influenced by a lot of different factors. Hence, precisely calculating the energy consumption of buildings and enhancing its efficiency is a difficult task. As a result, good energy management and prediction are becoming increasingly vital in the support of energy saving. For this purpose, an energy baseline model is a useful tool for modeling energy consumption for any building over a period of time. It can define as a comparison tool that allows to evaluate energy performance before and after some modification in the system (building). The primary goal of the energy baseline model is to estimate energy demand if energy efficiency measures are applied to a building for the sake of energy savings estimations. The purpose of this study is to examine current approaches for estimating building energy baselines, including physics-based, data-driven, and hybrid approaches, as well as model development process and essential elements. The choice of input independent variables is critical when developing energy baseline models since it affects the estimation of energy savings. Therefore, this review summarizes the input variables that influence energy consumption. Performance metrics are also provided to evaluate the energy baseline models and the effects of independent variables.

Keywords

• Energy baseline model
• Physical modeling
• Data-driven
• Independent variable