Reliable Hybridization Approach for Estimation of The Heating Load of Residential Buildings

Abstract

In recent times, the world's growing population, coupled with its ever-increasing energy demands, has led to a significant rise in the consumption of fossil fuels. Consequently, this surge in fossil fuel usage has exacerbated the threat of global warming. Building energy consumption represents a significant portion of global energy usage. Accurately determining the energy consumption of buildings is crucial for effective energy management and preventing excessive usage. In pursuit of this goal, this study introduces a novel and robust machine learning (ML) method based on the K-nearest Neighbor (KNN) algorithm for predicting the heating load of residential buildings. While the KNN model demonstrates satisfactory performance in predicting heating loads, for the attainment of optimal results and accuracy, two novel optimizers, the Snake Optimizer (SO) and the Black Widow Optimizer (BWO), have been incorporated into the hybridization of the KNN model. The results highlight the effectiveness of KNSO in predicting heating load, as evidenced by its impressive R2 value of 0.986 and the low RMSE value of 1.231. This breakthrough contributes significantly to the ever-pressing pursuit of energy efficiency in the built environment and its pivotal role in addressing global environmental challenges.