Investigating the effectiveness of LSTM and deep LSTM architectures in solar energy forecasting

This study investigates the effectiveness of Long Short-Term Memory (LSTM) and Deep LSTM architectures in solar energy forecasting using real-world data. We compare these models based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics to assess their predictive performance. While Deep LSTM models show higher accuracy by capturing complex temporal patterns, they also demand greater computational resources. Hybrid models integrating LSTM with techniques like CNNs and Transformers demonstrate further improvements, achieving lower error rates. The findings highlight the trade-offs between model complexity and computational efficiency, providing insights into selecting suitable architectures for solar power forecasting. This research contributes to advancing deep learning techniques for renewable energy systems, enhancing their role in modern energy management.