Exploring Data Availability, Time Horizons and Ensemble Learning for Regional Photovoltaic Power Forecasting

Accurate forecasting of Photovoltaic (PV) power generation is essential for integrating renewable energy into power grids, ensuring operational stability, and advancing sustainable energy management. This study examines the influence of training data availability, forecast horizons, and ensemble learning techniques on the accuracy of regional PV power forecasting. A sliding window approach is employed to evaluate model performance over an entire year, capturing the effects of seasonal variations and diverse data conditions. The results show that the amount of available training data, the selected forecast horizon, and the specific combination of base models within ensemble methods substantially influence forecasting accuracy. Crucially, the findings reveal that the effectiveness of an ensemble model is shaped by the learning algorithm and the degree to which its base models are well-suited to the characteristics of the input data and the forecasting timeframe. These insights highlight the need for adaptive ensemble configurations tailored to both data context and prediction objectives, providing practical guidance for the development of more reliable and flexible PV forecasting solutions.