An AI-Based Solar Power Forecasting and Switching System

Solar power generation is highly sensitive to short-term weather variations, particularly under rapid cloud movement, leading to significant power fluctuations and challenges in stable energy dispatch. To address this issue, this study proposes an AI-based solar power prediction and power switching control system integrating a hybrid deep learning model with an embedded microcontroller. The model combines radar echo imagery and meteorological time-series data, where a convolutional neural network (CNN) extracts spatial cloud features and a gated recurrent unit (GRU) captures temporal dynamics for short-term irradiance forecasting. Based on the prediction results, the microcontroller performs real-time power source switching between solar and grid supply. Experimental results using real-world data from the Taiwan Central Weather Bureau demonstrate that the proposed system achieves reliable prediction performance and enables effective proactive energy management. These results suggest that the integration of AI-based forecasting and embedded control has potential for renewable energy utilization and power dispatch applications. By supporting intelligent energy management and more efficient use of photovoltaic energy resources, the proposed framework may contribute to sustainable energy utilization in photovoltaic systems.