Short-term forecasting of rooftop photovoltaic energy generation using machine learning techniques

Rooftop photovoltaic (PV) systems exhibit significant variability in short-term electricity generation due to differences in panel tilt and azimuth angles. Neglecting this geometric diversity may reduce forecasting accuracy, particularly in distribution networks with heterogeneous rooftop PV installations. This study investigates short-term rooftop PV forecasting using an orientation-aware modeling framework based on a real-scale experimental dataset. Five 300 W monocrystalline PV panels were monitored hourly for approximately 1 year under four different tilt angles and seven azimuth angles, representing 28 distinct orientation configurations. Temporal variables (the cosine of day and hour), meteorological variables, and the cosine of the solar incidence angle were used as input features. Three forecasting model structures (Models I–III), each defined by a different set of input features, were developed. Each model was tested using the Persistence Method (PM), Long Short-Term Memory (LSTM), and Gradient Boosted Regression Trees (GBRT). Among the evaluated models, Model II, which included the cosine of the solar incidence angle, achieved the best overall performance. In this model, GBRT outperformed both LSTM and PM by yielding the lowest mean test root mean square error (RMSE) (13.56 ± 2.18 Wh), the highest mean test R ² (0.973 ± 0.009), and the lowest mean test mean absolute percentage error (MAPE) (18.04 ± 3.65). The results demonstrate that incorporating physically meaningful orientation-aware features substantially improves forecasting accuracy for heterogeneous rooftop PV systems. These findings highlight the importance of panel orientation in hour-ahead rooftop PV forecasting. Consequently, the proposed approach can improve the accuracy of distributed PV generation forecasts and net demand forecasts at the distribution level compared to traditional aggregation-based approaches relying on general solar radiation data.