Improvement of the Japan Meteorological Agency Meso-Scale Model for the Forecasting the Photovoltaic Power Production: Modification of the Cloud Scheme

摘要

Power production of a photovoltaic (PV) power plant varies according to weather conditions. Therefore, it is important for the prediction of the PV power production to use the weather data (satellite data or numerical weather prediction, etc.). In our research group, for the day ahead forecasting, the output of the numerical weather model, Japan Meteorological Agency Meso-Scale Model (hereafter MSM) is used for the input of the PV power production model. From our previous research, the MSM forecast of the global horizontal irradiancc (GHI) tends to be underestimated (overestimated) during summer (winter). Further investigation revealed that the error of the MSM GHI forecast is in a relation of the inverse correlation with the error of the MSM cloudiness forecast. So, in this study, to improve the MSM GHI forecast, the cloud scheme is modified to remove the error. The MSM is an operational, non-hydrostatic and regional model used for a short-range forecast (33 hours). The model horizontal resolution is 5 km mesh and the model vertical resolution is 50 levels. The current cloud scheme of the MSM has the seasonal error so that the parameter which dependent on the surface air temperature is introduced to represent the seasonality: if the surface air temperature is low (high), then the cloud production is accelerated (decelerated). Twelve cases (6 for winter and 6 for summer) are chosen for the analysis. The modified cloud scheme makes a success of the reduction of the MSM GHI forecast error. The daytime averaged root mean square error (RMSE) of the MSM GHI forecast for all cases is improved about 5% (from 120 W m~2 to 114 W m~2). The daytime averaged mean bias error (MBE) of the MSM GHI forecast for all cases is significantly reduced from -14.3 W m~2 to -5.13 W m~2. For each cases, although three of them are increased the RMSE (about 3 W m~2), the total trend are decreased the RMSE.