Performance enhancement of short-term wind speed forecasting model using Realtime data.

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence in Energy
• Environmental Science

Background:

• Traditional energy sources face depletion and environmental concerns, driving a global shift to renewables.
• Wind power is a key carbon-free energy source, but its efficiency depends on accurate wind speed predictions.
• Unpredictable wind patterns pose challenges for stable wind power generation and turbine safety.

Purpose of the Study:

• To develop a novel hybrid model, L-LG-S, for precise short-term wind speed forecasting.
• To evaluate the performance of the L-LG-S model against existing state-of-the-art methods.
• To enhance the reliability and efficiency of wind power generation through improved forecasting.

Main Methods:

• Development of the hybrid L-LG-S model integrating advanced machine learning and deep learning techniques.
• Comparative analysis of the proposed model against contemporary algorithms for wind speed forecasting.
• Validation using real-world wind speed data from a wind turbine in Karachi, Pakistan.

Main Results:

• The L-LG-S model demonstrated superior accuracy in short-term wind speed forecasting.
• The model achieved a 98% improvement in accuracy across training, validation, and test predictions compared to legacy models.
• Evaluation metrics including Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) confirmed the model's effectiveness.

Conclusions:

• The L-LG-S hybrid model offers a significant advancement in precise short-term wind speed forecasting.
• This improved forecasting capability is vital for optimizing wind power generation and ensuring operational safety.
• The study highlights the potential of advanced hybrid models to address the challenges of renewable energy integration.