Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting

Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation

Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation (LUBE) using neural networks with two outputs to make probabilistic modeling for predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), continuous ranked probability score (CRPS), and loss, which is the integration of PICP and MPIW, by removing unnecessary features through feature selection. When features with high gain were selected by random forest (RF), in the modeling of 14.7 kW PV systems, loss improved by 1.57 kW, CRPS by 0.03 kW, PICP by 0.057 kW, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise and reduce the modeling accuracy.