(Department of Electrical and Electronic Engineering, College of Engineering Science, Nyala University, Nyala 63311, Sudan, Sudan)(2) * Ahmed Mohamed Ishaga
(School of Engineering and Applied Science, Kampala International University, Kampala, P.O. Box 20000, Uganda, Uganda)(3) Hassan Ahmed Osman
(Department of Chemistry, College of Education, Nyala University, Nyala 63311, Sudan, Sudan)(4) Mohamed Elhindi
(College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China, China)(5) Nasreldin Ibrahim
(School of Electrical and Information Engineering, Tianjin University, Tianjin, Tianjin 300072, China, China)(6) Aissa Snani
(LabGED, Department of Computer Science, University of Badji Mokhtar, Annaba 23000, Algeria, Algeria)(7) Gomaa Haroun Ali Hamid
(Department of Electrical and Electronic Engineering, College of Engineering Science, Nyala University, Nyala 63311, Sudan, Sudan)(8) Abdallah Hammad
(Department of Electrical Engineering, College of Engineering, University of Bisha, P.O. Box 551, 61922, Bisha, Saudi Arabia, Saudi Arabia)*corresponding author
AbstractAccurate forecasting of electrical power load is essential for properly planning, operating, and integrating energy systems to accommodate renewables and achieve environmental sustainability. Therefore, this study introduces different machine learning (ML) methods, including support vector machines (SVM), random forests (RF), extreme learning machines (ELM), and extreme gradient boosting (XGBoost) to predict hourly electricity demand using electricity consumption and temperature data for train and test ML models. The data is processed by autocorrelation function (ACF) and cross-correlation function (CCF) to determine the appropriate lag time for the inputs. Furthermore, ML model accuracy is assessed using coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). Results show that the ELM model achieved the highest R² in both summer (0.971) and winter (0.868), outperforming the other models in accuracy R² and error reduction (MAE and RMSE). ELM also more effectively captured load fluctuations. The result of this research has applications for load demand forecasting in the proper planning and operation of the residential grid. The results help estimate load demand and provide useful guidance for residential grid planning and management by determining the best techniques for precisely estimating load demand and identifying domestic energy consumption patterns
KeywordsResidential Energy Consumption; Sustainable Energy System; Load Forecasting; Extreme Learning Machines; Machine Learning Algorithms
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DOIhttps://doi.org/10.31763/sitech.v6i1.1866 |
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Copyright (c) 2025 Modawy Adam Ali Abdalla, Ahmed Mohamed Ishaga, Hassan Ahmed Osman, Mohamed Elhindi, Nasreldin Ibrahim, Aissa Snani, Gomaa Haroun Ali Hamid, Abdallah Hammad
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