Enhanced Weather Prediction for Optimizing Renewable Energy Production using Artificial Intelligence

Abstract

Artificial Intelligence in weather prediction has increased the accuracy and reliability of renewable energy. The present study proves how the integration of AI models in weather forecasting can enhance the optimization of energy generation, reduce fluctuations in energy outputs, and increase the stability of the grid. This research utilized a quantitative and analytical research design, using meteorological data from places like NASA and ECMWF, in conjunction with renewable energy production data in solar power and wind power plants. Accordingly, ANNs and LSTM networks as well as Random Forest Regression and Hybrid AI models were developed and tested with statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² score. It was found that for weather prediction, LSTM model performed well above the other models by giving a minimum value of RMSE at 2.1°C/m/s and maximum R² score at 0.91. The Hybrid AI Model of ANN + LSTM also outperformed all the other models, achieving an R² score of 0.92 and strongly reducing forecasting errors for renewable energy output prediction. The results have shown that efficiency in energy management through AI-based forecasting has resulted in a 22.7% increase in the accuracy of predictions, with a 42.7% reduction in energy output fluctuations, and a 16.7% improvement in grid stability. Additionally, fossil fuel backup usage decreased by 40%, promoting sustainable energy utilization. These results underscore the transformative potential of AI in optimizing renewable energy production, ensuring a more stable, reliable, and environmentally friendly energy system.