Hybridizing Metaheuristic Methods with AI for Accurate Prediction of Residential Energy Consumption

Accurately predicting Total Energy Consumption in the Residential Sector is essential for sustainable energy planning. This study introduces four hybrid metaheuristic-artificial intelligence models—Earthworm Optimization Algorithm (EWAMLP), Stochastic Fractal Search (SFSMLP), Vortex Search (VSMLP), and Shuffled Complex Evolution (SCEMLP)—to enhance prediction accuracy. The models were evaluated using Root Mean Squared Error (RMSE) and R-squared (R۲) on training and testing datasets, with swarm sizes optimized for each method. SFSMLP achieved the best overall performance, ranking first with the highest R۲ values of ۰.۹۹۳۶ (training) and ۰.۹۸۵۹ (testing) and the lowest total score. VSMLP, with R۲ values of ۰.۹۹۱۵ (training) and ۰.۹۸۶۶ (testing), tied for first in accuracy while demonstrating efficiency with an optimal swarm size of ۳۰۰. SCEMLP, despite using the smallest swarm size (۵۰), maintained competitive accuracy with R۲ values of ۰.۹۹۱۱ (training) and ۰.۹۸۴۲ (testing), ranking third overall. EWAMLP, with R۲ values of ۰.۹۶۷۳ (training) and ۰.۹۵۷۰ (testing), showed reliable but slightly lower performance, ranking fourth. These findings highlight the potential of hybrid metaheuristic-AI models for precise energy consumption predictions. The superior performance of SFSMLP and VSMLP suggests their suitability for applications requiring high accuracy, while SCEMLP offers a balance of efficiency and reliability. This study provides a robust framework for energy modeling, contributing to advancements in residential energy management and sustainability.

کلیدواژه ها:

energy consumption ، Residential Sector ، artificial intelligence ، Metaheuristic algorithm