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

Abstract

Hourly solar and photovoltaic (PV) forecasts for horizons between 0 and 48 h 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 “regional” forecast 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. © Her Majesty the Queen in Right of Canada 2011. Reproduced with the permission of the Minister of Natural Resources Canada.

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