Area Day-ahead Photovoltaic Power Prediction by Just-In-Time Modeling with Meso-scale Ensemble Prediction System

Photovoltaics (PV) output prediction, which is indispensable for power system operation, can affects demand and supply adjustment adversely when large prediction error occurs. Thus, the reduction of large error as well as average error is required in PV power prediction. In 2019, the operation of the Meso-scale Ensemble Prediction System (MEPS) of numerical weather prediction started from the Japan Meteorological Agency, and the amount of forecasting information would be potentially useful for the improvement of PV power prediction. However, very few studies on inputting multiple meteorological elements of the MEPS have been reported. In this paper, we newly develop the prediction model for an area day-ahead PV power output composed of Just-In-Time Modeling (JIT Modeling) with multiple elements of the MEPS. The developed method achieves precise forecasts with low computational load by both selecting meteorological elements valid for improving prediction accuracy and adequately devising the structure of JIT Modeling. Some numerical examples demonstrating the effectiveness of the developed method are also presented. In particular, the proposed method reduces large error significantly.