Artificial Intelligence Techniques for Optimizing Solar and Wind Energy Production Based on Weather Patterns

The variability of solar and wind energy due to weather fluctuations poses significant problems to the stability of the energy system and the utilization of renewable energy sources. Artificial Intelligence (AI) methodologies provide promising algorithms for the prediction and optimization of solar and wind energy. This article examines the use of advanced AI methodologies- specifically Random Forests, Long Short-Term Memory (LSTM) networks, and Transformer models- to forecast the performance of solar and wind farms and enhance their operational efficiency. Utilizing historical weather and energy production data from diverse geographical locations, we developed and evaluated models based on MAPE and RMSE metrics. The LSTM and Transformer-based models consistently outperformed conventional approaches, achieving a MAPE as low as 5.2% for solar predictions and 5.5% for wind forecasts. The strategy involves data pre-processing, feature creation (solar energy density, temperature, wind speed, and wind direction), and hyper parameter optimization using the Bayesian method. Research indicates that AI-driven forecasting facilitates improved integration of renewable energy into the grid, resulting in a proportional decrease in carbon-based resource use and enhanced system resilience. Major Findings: The study found that Transformer and LSTM models significantly outperformed traditional forecasting and optimization methods, achieving MAPE as low as 5.2% for solar and 5.5% for wind predictions. These results demonstrate that AI-driven forecasting enhances grid integration of renewable, reduces reliance on carbon-based energy, and improves overall system stability.