Solar and photovoltaic forecasting through post-processing of the Global Environmental Multiscale numerical weather prediction model

Hourly solar and photovoltaic (PV) forecasts for horizons between 0 and 48h ahead were developed using Environment Canada's Global Environmental Multiscale model. The motivation for this research was to explore PV forecasting in Ontario, Canada, where feed-in tariffs are driving rapid growth in installed PV capacity. The solar and PV forecasts were compared with irradiance data from 10 North-American ground stations and with alternating current power data from three Canadian PV systems. A 1-year period was used to train the forecasts, and the following year was used for testing. Two post-processing methods were applied to the solar forecasts: spatial averaging and bias removal using a Kalman filter. On average, these two methods lead to a 43% reduction in root mean square error (RMSE) over a persistence forecast (skill score=0.67) and to a 15% reduction in RMSE over the Global Environmental Multiscale forecasts without post-processing (skill score=0.28). Bias removal was primarily useful when considering a regionalforecast for the average irradiance of the 10 ground stations because bias was a more significant fraction of RMSE in this case. PV forecast accuracy was influenced mainly by the underlying (horizontal) solar forecast accuracy, with RMSE ranging from 6.4% to 9.2% of rated power for the individual PV systems. About 76% of the PV forecast errors were within +/- 5% of the rated power for the individual systems, but the largest errors reached up to 44% to 57% of rated power. (c) Her Majesty the Queen in Right of Canada 2011. Reproduced with the permission of the Minister of Natural Resources Canada.