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Optimizing Neural Network Model Performance for Wind Energy Forecasting

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High variability and intermittency of wind create difficulties in managing and optimizing wind farms. Short-term forecasts are essential for a power plant’s safe operation. The aim of this work was to develop an efficient model for forecasting wind energy in the short term using machine learning and metaheuristics methods. The study improved a fruit Fly Optimization Algorithm (FOA) with decreasing step size to enhance the forecasting accuracy of the backpropagation neural network and radial basis function neural network. The efficiencies of the proposed methods were evaluated by comparing the values of the mean absolute percentage error, the root-mean-square error, and the standard deviation error. It was found that the optimized models demonstrate the high efficiency of forecasting in comparison with actual meteorological data. The error estimation analysis showed that the error values for the optimized models are 4-5 times lower than those for the same models without optimization. It has been shown that FOA with decreasing step size for neural network improves accuracy and computational speed for short-term wind energy forecasts. This approach can be applied in programs for real wind farms and studied for other network parameters, such as weights and offsets.
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Short-Term Energy Load Forecasting; Backpropagation Neural Network; Electric Networks; Power Distribution; Radial Basis Function; Fruit Fly Optimization Algorithm (FOA)

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