Machine Learning Approaches for Short-Term Rooftop PV Forecasting in Tropical Climates: A Systematic Review

Abstract

In many tropical regions, rooftop photovoltaic (PV) systems are pivotal for sustainable energy transitions. Nevertheless, grid stability and operational planning are complicated due to their variable output. With a focus on tropical climates and developing-country contexts, particularly in Sri Lanka, this systematic review combines machine learning (ML), deep learning (DL), and hybrid forecasting approaches for short-term (1–24 hours) Rooftop PV prediction. Classifying based on algorithm, data requirements, system scale, and climatic context, 32 peer reviewed studies were found through a systematic search across major academic databases (2018–2025). Key findings demonstrate that while hybrid ML–DL models (e.g., CNNBiLSTM), optimization-enhanced networks, and ensemble frameworks outperform conventional statistical baselines, they encounter real-world limitations, including limited validation on distributed rooftop systems, high computational cost, and data scarcity. Promising directions include lightweight and interpretable hybrid models, tropical dataset development, and the integration of explainable AI with uncertainty quantification. Overall, this review provides a practical roadmap for researchers and practitioners aiming to design scalable, dependable PV forecasting systems in resource-constrained tropical environments.