Application of algorithms for forecasting and optimizing the production capacity of wind energy

With advances in technology, particularly where environmental implications are a concern, equipment has become increasingly complex and metered. Human activities like emissions and urbanization significantly impact the atmosphere and wind energy generation. This study predicts wind energy generation capacity for 2025–2029 based on data from 2000–2024. Predictive models (e.g., Long Short-Term Memory (LSTM), Facebook Prophet, and SARIMA) are applied to analyze energy trends. Performance results show LSTM provides the best accuracy. The findings enhance reliable wind energy forecasting and improved energy management methods in response to shifting climate dynamics.