Weather forecasting based on data-driven and physics-informed reservoir computing models

Area of Science:

• Renewable Energy Systems
• Atmospheric Science
• Computational Intelligence

Background:

• Growing global energy demand necessitates advancements in renewable energy, particularly wind power.
• Stochastic weather volatility presents a significant challenge for accurate wind power forecasting.
• Existing forecasting methods struggle to capture the complex dynamics of weather systems.

Purpose of the Study:

• To develop and validate a novel two-model weather prediction system for enhanced wind power forecasting.
• To improve the accuracy and reliability of wind speed and atmospheric system predictions.
• To assess the applicability of a physics-informed approach for wind energy analysis.

Main Methods:

• A data-based model combining wavelet transform and recurrent neural networks (RNNs) for wind speed prediction.
• A physics-informed echo state network (PI-ESN) to model the chaotic behavior of atmospheric systems.
• Validation using a case study with wind speed data from Turkmenistan.

Main Results:

• The proposed models demonstrated robust performance in weather prediction.
• The physics-informed echo state network significantly outperformed traditional methods in forecasting accuracy.
• The study confirmed the reliability of the integrated approach for wind energy analysis.

Conclusions:

• The developed physics-informed model offers a promising solution for accurate wind power forecasting.
• This approach has the potential to enhance the stability and efficiency of the wind energy supply chain.
• The findings support the broader implementation of advanced computational methods in renewable energy research.