Predicting Daily Heating Energy Consumption in Residential Buildings through Integration of Random Forest Model and Meta-Heuristic Algorithms

This research utilizes a sophisticated hybrid model integrating the Random Forest algorithm with meta-heuristic optimization techniques to estimate heating energy consumption in residential buildings. The study addresses key variables including architectural characteristics, occupancy, and ambient temperature. The primary objective is to enhance the prediction accuracy of heating energy consumption using a novel approach combining Random Forest with various meta-heuristic algorithms. The study employs six combinations of the Random Forest algorithm and meta-heuristic optimizers. To mitigate overfitting, K-Fold cross-validation is implemented during model training. The model's performance is evaluated using five statistical indices: coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Relative Absolute Error (RAE), and Theil Inequality Coefficient (TIC). Results demonstrate the hybrid model's high predictive accuracy, with the Arithmetic Optimization Algorithm enhancing Random Forest's performance significantly. Notable statistical achievements include R2 = 0.977201, RMSE = 0.1179, MAE = 0.0573, RAE = 0.0930, and TIC = 0.0187. Additionally, the Ant Lion Optimizer shows excellent convergence, achieving a TIC value of 0.014986 after 101 iterations. The proposed hybrid model significantly outperforms traditional methods in predicting residential heating energy consumption. The integration of Random Forest with advanced meta-heuristic algorithms offers a robust framework for enhancing prediction accuracy in energy consumption modeling.