A Data‐Driven Investigation of How Forecast Horizon and Training Data Size Influence Machine Learning Performance in Solar Irradiance Forecasting

Abstract

Accurate short‐ and medium‐term solar irradiance forecasting is vital for integrating solar power into the grid, but high variability and weather dependence make it challenging. Machine learning (ML) models offer promise, but their performance often depends on forecast horizon and training data size. In this study, four models, including random forest (RF), support vector regression (SVR), gradient boosting regressor (GBR), and deep neural networks (DNN), are compared for 15‐min, hourly, and day‐ahead solar irradiance prediction. The models are trained with meteorological and irradiance data for the city of Sioux Falls, South Dakota, using datasets ranging from 1 to 8 years (2012–2019). A total of 96 models are developed by combining four algorithms across three horizons and eight training data sizes. Using the maximum relevance minimum redundancy (MRMR) technique, the best features are selected, and the results are evaluated using standard error metrics. The results indicate that RF provides the most stable accuracy prediction, SVR provides the best accuracy on day‐ahead forecasts with limited data, GBR performs best at the hourly prediction level, and DNN accuracy is more sensitive to both data quality and overfitting. It emphasizes the importance of choosing models that align with the forecast horizon and the size of available data. In this paper, we provide an in‐depth comparison of four state‐of‐the‐art ML models across various horizons and training data sizes. Results show the importance of integrating MRMR, with guidelines for researchers and energy planners to achieve more accurate and efficient solar irradiance prediction.