Prediction of Heating and Cooling Load to improve Energy Efficiency of Buildings Using Machine Learning Techniques

Abstract

Global warming has been a severe threat to humanity and greenhouse gases emitted from power plants is one of the major causes of global warming. In this paper, we use machine learning to incorporate energy efficiency techniques to buildings by predicting the Heating and Cooling Load using eight input features. Heating load is the amount of heat per unit time that a building needs to maintain the temperature at an established level whereas Cooling load is the amount of heat per unit time that must be removed. Heating, cooling, and ventilation systems are used to handle heating and cooling load. We train four regression (linear regression, Lasso, Ridge, and Elastic-Net) and three gradient boosting models (GBM, XGBoost, and LightGBM) and test them to compare their performance using 768 rows of data of residential buildings. We observe that the gradient boosting models perform significantly better than the standard regression models for both Heating Load and Cooling Load. XGBoost achieves the highest R-squared score of 0.99 for Heating Load and 0.99 for Cooling Load. From the results of this study, we conclude that machine learning techniques can predict Heating Load and Cooling Load with high accuracy. The obtained Heating load and cooling load values can be used to install efficient heating, cooling and ventilation systems and thus reduce both energy consumption and money.