Enhancement of Wind Power Forecasting Through LSTM Networks, Correlation Analysis, and Predictive Modeling

The growing demand for renewable energy has driven the rapid expansion of wind power farms. However, the intermittent nature of wind energy presents significant challenges for power producers in maintaining a stable balance between supply and demand. This underscores the necessity for advanced forecasting systems to optimize power generation and ensure a reliable energy supply. Such systems are crucial for predicting fluctuations in wind speed and enabling backup mechanisms during periods of reduced wind power generation. This paper analyzes five months of comprehensive data from three wind turbines to forecast future power generation. By examining daily datasets, including wind speed and generated power, correlation analysis and data count assessments are performed to identify the most effective datasets for accurate predictions. To enhance the forecasting accuracy, a Long Short-Term Memory (LSTM) model, a type of recurrent neural network well-suited for time-series data, is used. The LSTM model is trained on the collected data to capture temporal dependencies and improve the reliability of wind power generation forecasts. The results demonstrate the effectiveness of LSTM in handling the complexities of wind energy data, providing a robust framework for future wind power prediction systems.