An Investigation on the Soft Computing Method Performance of the Optimizing Energy Consumption Cost

Majlesi Journal of Electrical Engineering

دوره 17، شماره 1، خرداد 2023، صفحه 109-115   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2023.1978858.1061

نویسندگان

Mohammed S. M. Nemer^{* 1}؛ Aqeel Hussain²؛ Ali Ihsan Alanssari³؛ Suhair Hussein Talib⁴؛ Kadhim Abbas Jabbar⁵؛ Siham Jasim Abdullah⁶

¹Department of Computer Engineering, Bahçeşehir University, Istanbul, Turkey

²Medical Technical College, Al-Farahidi University, Baghdad, Iraq

³Al-Nisour University College, Iraq

⁴Medical Instrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq

⁵National University of Science and Technology, Dhi Qar, Iraq

⁶Department of Dental Industry Techniques, Al-Noor University College, Nineveh, Iraq.

چکیده

During peak demand hours, hydroelectric energy is one of the most significant sources of energy. Power sector restructuring has increased competition among the country's electricity providers. Estimating the future price of energy is critical for producers in order to enhance investment profit and make better use of resources. One of the most significant technologies of artificial intelligence, Artificial Neural Networks (ANN), has various applications in estimating and forecasting phenomena. Combining artificial intelligence models with optimization models (e.g. Artificial Bee Colonoy [ABC]) has recently become quite popular for improving the performance of artificial intelligence models. The goal of this study is to look at the effectiveness of ANN and ABC-ANN models in forecasting the dispersed and sinusoidal data of Angola's daily peak power price. The findings reveal that in this case study, the employment of the ABC-ANN model is not superior to the ANN model and has not resulted in enhanced performance and forecasting of power market data. As a result, the R2 of the ANN and ABC-ANN models is 0.88 and 0.85, respectively.

کلیدواژه‌ها

Artificial neural network؛ artificial bee colony؛ energy cost؛ optimization

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