Hybrid PSO And RNN Model for Wind Energy AND Wind Power Optimization

Abstract

This research paper proposes a novel hybrid model for short-term and long-term wind power forecasting. The model integrates the strengths of Recurrent Neural Networks (RNNs), primarily focusing on Long Short-Term Memory (LSTM) networks due to their superior ability to handle long-range dependencies in time-series data [1], [2], [3], with Particle Swarm Optimization (PSO) [4], [5], [6] and Harmony Search (HS) [7], [5], [8] algorithms. PSO and HS, both meta-heuristic optimization techniques, are employed to optimize the hyperparameters of the LSTM network, enhancing its accuracy and generalization capabilities. The proposed hybrid model aims to overcome the limitations of individual techniques, such as premature convergence in PSO and local optima entrapment in HS [5], [9], [10], while leveraging the temporal dependency capturing abilities of LSTMs for improved wind power forecasting. The performance of the proposed model will be evaluated using real-world wind farm data, and compared with existing state-of-the-art methods, demonstrating its efficacy and potential for practical applications in renewable energy systems. The model's robustness and accuracy will be assessed through various metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) [11], [1], [2], considering various forecasting horizons.