Random Forest model for precise cooling load estimation in optimized and non-optimized form

Abstract Energy is vital for life and human development, with global warming due to activities such as the combustion of fossil fuels and deforestation emitting dangerous greenhouse gases, changing the climate of the Earth. Global energy demand is increasing, with developed nations viewing buildings as major energy consumers. Due to the long lifespan of buildings, it is important to evaluate their suitability to future climate change and possible changes in energy consumption. Appraisal of the cooling loads in each building is now required due to rising energy costs and the need to reduce the impacts of climate change caused by energy consumption from fossil fuels in buildings. This paper aims to apply Random Forest Regression (RF) and Support Vector Regression (SVR), well-known machine learning algorithms to predict building cooling loads. It utilizes the Jellyfish Search Optimizer (JSO) and Transit Search Optimization Algorithm (TSOA) to enhance accuracy and minimize overall error in Cooling Load (CL) estimation. The investigation suggests two high-performance schemes, applies two optimizers for hybrid schemes, and utilizes an ensemble approach for accurate appraisal . Moreover, the SHAP method is utilized to compare the effectiveness of the parameters. The research proves to be insightful in constructing CL projection and suggests that a RFJS-based model is the most effective way to optimize energy consumption. The hybrid model attained an R 2 of 0.994 at its best and RMSE of 0.744. Other than this, the following effective ensemble approach was RSJS, whose R 2 and RMSE were 0.989 and 0.985, accordingly. The third best-performing model was SVJS with R 2 and RMSE values of 0.972 and 1.583, accordingly.