Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF) - Haytham Elmousalami; Felix Kin Peng Hui; Aljawharah A. Alnaser (Buildings)
The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R2 of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R2 of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO2 emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure.