Hybrid Approaches to Renewable Energy Forecasting for Grid Stability Enhancement

Renewable energy forecasting is crucial for maintaining grid stability, reducing curtailment, and optimizing energy utilization. However, forecasting wind and solar energy is challenging due to their intermittent and variable nature, influenced by complex weather patterns, non- linear dependencies, and seasonal variations. Additionally, issues like data quality, geographical differences, and computational constraints further complicate accurate predictions and integration with grid systems. This paper discusses the importance of renewable energy forecasting and evaluates the effectiveness of three predictive models: Long Short-Term Memory (LSTM), Facebook Prophet (F-Prophet), and AutoRegressive Integrated Moving Average (ARIMA) using real-world solar and wind energy data. The experimental results demonstrate that LSTM achieved a Mean Absolute Error (MAE) of 4.19, representing a 98.74% and 98.89% improvement over F-Prophet and ARIMA, respectively. For wind forecasting, LSTM recorded a remarkably low MAE of 0.056, outperforming F-Prophet and ARIMA by 99.99%. In terms of Root Mean Square Error (RMSE), LSTM also showed superior performance improving over F-Prophet and ARIMA by 71.05% and 76.88% in solar forecasting, and by 91.01% and 91.24% in wind forecasting. These results emphasize LSTM's effectiveness in capturing complex, non-linear relationships and temporal dependencies in energy generation data.