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Iranian Journal of Chemistry and Chemical Engineering، جلد ۴۴، شماره ۴، صفحات ۱۱۹۷-۱۲۲۱
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عنوان انگلیسی Hybridizing Metaheuristic Methods with AI for Accurate Prediction of Residential Energy Consumption
چکیده انگلیسی مقاله 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 (R2) on training and testing datasets, with swarm sizes optimized for each method. SFSMLP achieved the best overall performance, ranking first with the highest R2 values of 0.9936 (training) and 0.9859 (testing) and the lowest total score. VSMLP, with R2 values of 0.9915 (training) and 0.9866 (testing), tied for first in accuracy while demonstrating efficiency with an optimal swarm size of 300. SCEMLP, despite using the smallest swarm size (50), maintained competitive accuracy with R2 values of 0.9911 (training) and 0.9842 (testing), ranking third overall. EWAMLP, with R2 values of 0.9673 (training) and 0.9570 (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.
کلیدواژه‌های انگلیسی مقاله energy consumption,Residential Sector,artificial intelligence,Metaheuristic algorithm
نویسندگان مقاله Jianjun Pang |
Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd. Guangdong Guangzhou,510665, P.R. CHINA

Yi Zhou |
Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd. Guangdong Guangzhou,510665, P.R. CHINA

Hua Huang |
Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd. Guangdong Guangzhou,510665, P.R. CHINA

Jinqing Lin |
Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd. Guangdong Guangzhou,510665, P.R. CHINA

Yuxin Yan |
Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd. Guangdong Guangzhou,510665, P.R. CHINA

نشانی اینترنتی https://ijcce.ac.ir/article_721356_ea77e97951a44eba011714830bb0cf18.pdf
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