Document Type : Research paper


1 Master of Science, Kazakh National Agrarian Research University, Abai Almaty, Kazakhstan

2 Department of Computer Technical Engineering, Technical Engineering College, Al-Ayen University, Thi-Qar, Iraq

3 Department of Medical Laboratory Technics, Al-Manara College for Medical Sciences, Maysan, Iraq

4 Department of Medical Laboratory Technics, Al-Nisour University College, Iraq

5 Technical engineering college/ National University of Science and Technology, Dhi Qar, Iraq

6 Department of Medical Engineering, Al-Hadi University College, Baghdad, Iraq

7 Department of Optics, College of Health \& Medical Technology, Sawa University, Almuthana, Iraq


Given the significant uncertainty surrounding future electricity prices, which is widely regarded as the most critical factor in this context, market participants must engage in forecasting to facilitate their exploitation and planning activities. The success of electricity market actors is dependent on the availability of more appropriate tools to address this issue. In contrast, there is a prediction of prices in the electricity market for varying periods due to the increasing use of renewable energy in global energy generation and the unsteady and disjointed configuration of renewable energy production. The fluctuating characteristics of wind energy production have increased the complexity of real-time demand management in power systems. This paper investigates the impact of renewable energy production on price forecasting using data from the Nord pool market's electricity market. The primary goal is to present a framework for forecasting market settlement prices using a hybrid wavelet-particle swarm optimization-artificial neural network (W-PSO-ANN). In two scenarios, the results showed that the proposed model accurately represents data and is more precise than the ANN and WANN models. Machine learning has demonstrated promise in predicting electricity prices, but it is not without limitations. The ANN, WANN, and W-PSO-ANN models have training phase RMSE indices of 0.09, 0.07, and 0.04 respectively. During testing, the values were 0.15, 0.11, and 0.08. This demonstrates that the proposed model outperforms previous models.


Main Subjects

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