Short-term solar PV forecasting in microgrids using cloud top temperature and vision transformer based models

Introduction Expanding clean-energy microgrids in remote areas is essential for achieving global decarbonisation and energy transition goals. Accurate short-term solar photovoltaic (PV) forecasting plays a key role in reducing diesel dependence, improving battery scheduling, and enabling reliable integration of renewable energy. However, forecasting remains challenging in many developing regions due to the lack of ground-based irradiance sensors, cloud cameras, and real-time monitoring infrastructure. Methods This paper proposes a novel forecasting framework, termed CTT–ViT–Transformer, which integrates Generative AI techniques to enhance short-term solar PV forecasting in sensor-constrained microgrids. The framework employs Cloud Top Temperature (CTT) satellite imagery, capturing cloud height and thermal characteristics, processed through a Vision Transformer (ViT) for spatial feature extraction and a Transformer model for time-series prediction. Results The proposed framework is evaluated using operational data from a real-world islanded microgrid. Results indicate that a standard Transformer model outperforms LSTM and CNN-LSTM baselines, achieving a mean absolute error (MAE) of 23.45 kW, root mean square error (RMSE) of 28.24 kW, and R² of 0.93. The CTT–ViT–Transformer further improves forecasting accuracy, reducing errors to an MAE of 15.99 kW and RMSE of 24.28 kW with an R² of 0.97, and consistently outperforms models relying on RGB satellite imagery. High predictive accuracy is maintained across four-step-ahead forecasts, with R² values exceeding 0.96. Discussion The proposed approach requires no ground-based irradiance sensors, lowering adoption barriers for resource-constrained microgrids while remaining compatible with sensor-based data when available. Its scalability supports proactive energy management in the carbon-neutral microgrid on Koh Paluay Island by enabling more efficient scheduling of renewable generation and energy storage, thereby reducing fossil fuel use and operational costs. By enabling affordable and accurate forecasting, this framework aligns with Sustainable Development Goal 7 (Affordable and Clean Energy) and Sustainable Development Goal 13 (Climate Action), contributing to a just and sustainable global energy transition.