Building energy consumption using deep learning

Abstract

The consumption of energy in buildings has elicited the occurrence of many environmental problems such as air pollution. Building energy consumption prediction is fundamental for improved decision-making towards regulating or decreasing energy usage. There have been several applications of Machine Learning (ML) algorithms for predicting the energy consumption of operational buildings without much exploration into forecasting the potential building energy consumption at the early design stage. On the topic of reducing energy inefficient buildings, it is essential to address the root of the problem, the essentiality of predicting energy use before construction to alleviate futuristic problems of constructing new buildings that are harmful to the environment. At the early design stage, the customary model utilised for predicting energy consumption is the forward model, based on building energy modelling tools, which is stated to be mundane and time consuming. In contrast, the Machine Learning(ML) model is recognized as the most contemporary and best technique for prediction. To address this gap, this paper (1) presents the utilization of deep learning for predicting annual energy consumption of buildings, and (2) conduct a comparative analysis of the prediction performance of the models. The originality of this paper is to build a model trained by a dataset of multiple buildings that enables building designers to input key features of a building design and forecast the annual average energy consumption at the early stages of development. The ANN method outperforms SVM and DT for predicting annual energy consumption.