Enhanced Wind Power Forecasting Using Deep Learning and Nature-Inspired Optimization Algorithms

Abstract

Wind power forecasting is a critical component of renewable energy integration, ensuring grid stability, efficient power dispatch, and minimizing financial losses due to forecasting inaccuracies. However, the variability of wind speed poses significant challenges to accurate predictions. This study investigates the application of advanced nature-inspired optimization algorithms, including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Firefly Algorithm (FA), to enhance deep learning-based forecasting models. The research employs Convolutional Neural Networks (CNN) and Transformer architectures, which have demonstrated superior capability in capturing spatial-temporal dependencies in wind data. Metaheuristic techniques are applied to optimize model hyperparameters, improving prediction accuracy and computational efficiency. Performance evaluation is conducted using multiple metrics, including Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and R² (coefficient of determination), along with additional meteorological factors such as air pressure, humidity, and turbulence intensity. Furthermore, this study analyzes the financial impact of forecasting errors, quantifying revenue losses and penalty cost reductions associated with inaccurate predictions. Results demonstrate that GWO outperforms other optimization techniques, achieving the lowest forecasting error and maximizing financial gains. The proposed approach provides a systematic and data-driven strategy for enhancing wind power forecasting, contributing to more reliable renewable energy management. Future work will explore hybrid optimization techniques, ensemble models, and adaptive learning mechanisms to further improve predictive accuracy and economic benefits.