Prediction of the Electricity Demand in the Market: An Application of Optimization and Machine Learning

Majlesi Journal of Electrical Engineering

مقاله 12، دوره 17، شماره 2، شهریور 2023، صفحه 109-115   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2023.1986619.1140

نویسندگان

Ahmed Majed Althahabi^{* 1}؛ Hassan Mohammed Abed²؛ Raed Khalid³؛ Abrar Ryadh⁴؛ Ali Al Mansor⁵؛ Kadhum Al-Majdi⁶؛ Adil Abbas Alwan²

¹Al-Manara College for Medical Sciences, Maysan, Iraq.

²Mazaya University College, Iraq

³Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, Iraq

⁴Medical Laboratory Techniques Department, Al-Mustaqbal University College, 51001 Hillah, Babylon, Iraq

⁵Department of Optical Techniques, Al-Zahrawi University College, Karbala, Iraq

⁶Department of Biomedical Engineering, Ashur University College, Baghdad, Iraq.

چکیده

In this study, the combination of Gray Wolf Optimization and Artificial neural networks (GWO-ANN) algorithm was applied to predict the long-term electricity demand in Iraq, considering the nonlinear trend and uncertainties in the variables affecting it. The results indicate that the population and gross domestic product are significant explanatory variables for long-term energy demand, consistent with previous studies. Compared to other intelligent methods, the GWO-ANN algorithm requires less data for modeling and optimally designs the ANN structure. The modeling and forecasting model outperform the ANN in simulating and predicting the long-term energy demand. Based on the most likely scenario, the predicted electricity demand in Iraq will reach approximately 415 GWh. Electricity is a critical factor in the development of societies and is utilized in various economic sectors.

کلیدواژه‌ها

Electricity Demand؛ Gray Wolf Optimization؛ Artificial Neural Networks؛ Predictive Modeling

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