Transformer-Based Solar Irradiance Forecasting Model for Coastal and Microclimate-Sensitive Areas of First District of Batangas

Accurate solar irradiance forecasting is a key prerequisite for reliable solar photovoltaic (PV) operation, effective energy management, and system optimization. Conventional forecasting methods, such as historical averaging, persistence models, and coarse-resolution numerical weather prediction outputs, often exhibit limited performance in coastal and microclimate-sensitive environments where irradiance is characterized by rapid and nonlinear atmospheric variability. Batangas District 1, a coastal and peninsular region in the Philippines, is particularly influenced by sea–land breeze circulation, convective cloud development, humidity fluctuations, and aerosol transport, resulting in highly non-stationary irradiance patterns and persistent forecasting errors. Although recent advances in deep learning, particularly Transformer-based architectures, have demonstrated strong potential for modeling complex atmospheric time-series, locally developed and validated models for Philippine coastal environments remain scarce. Moreover, this study addresses this gap by developing FAT-Former, a Transformer-based solar irradiance forecasting algorithm designed to capture the microclimatic characteristics of Batangas District 1. The proposed model substantially outperformed the persistence benchmark and demonstrated strong temporal representation capability, achieving the lowest mean peak-time error and preserving 101.30% of the observed variance. However, predictive performance deteriorated under precipitation events and highly variable daylight conditions, while the resulting prediction intervals exhibited under-calibration. These findings suggest that forecasting performance in coastal environments depends not only on model complexity but also on the model’s ability to adapt to rapidly changing atmospheric regimes. All in all, FAT-Former demonstrates the potential of Transformer-based architectures for localized and uncertainty-aware solar irradiance forecasting. Further evaluation across diverse coastal and microclimate-sensitive locations is recommended to assess the model’s robustness, transferability, and broader generalizability.