Wind power forecasting using optimized LSTM by attraction–repulsion optimization algorithm

Wind power forecasting is crucial for energy conversion and management. This study employs the long short-term memory (LSTM) network, a specialized form of recurrent neural networks (RNNs) noted for its effectiveness in time-series prediction, to predict wind power from various turbines. Furthermore, we incorporate a cutting-edge metaheuristic optimization technique to optimize the training process of the LSTM, enhancing its parameter optimization. Specifically, we utilize the attraction–repulsion optimization algorithm (AROA), an innovative optimization algorithm inspired by the natural phenomena of attraction and repulsion for addressing complex optimization and engineering problems. This research applies the AROA to optimize the LSTM training process, which markedly improves the model's forecasting accuracy. Our analysis is conducted using four datasets from La Haute Borne wind turbines in France. The proposed AROA-LSTM model achieved R² testing results of 0.9416, 0.9663, 0.9613, and 0.9622 for Turbines 1, 2, 3, and 4, respectively.