RESEARCH ARTICLE Solar and photovoltaic forecasting through post-processing of the Global Environmental Multiscale numerical weather prediction model Sophie Pelland 1 *, George Galanis 2,3 and George Kallos 2 1 CanmetENERGY, Natural Resources Canada, Varennes, Québec, Canada 2 Atmospheric Modeling and Weather Forecasting Group, Department of Physics, University of Athens, Athens, Greece 3 Section of Mathematics, Naval Academy of Greece, Piraeus, Greece 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. KEYWORDS solar forecasting; photovoltaic forecasting; numerical weather prediction; post-processing; Kalman filter; spatial averaging *Correspondence Sophie Pelland, CanmetENERGY, Natural Resources Canada, Varennes, Québec, Canada E-mail: spelland@nrcan.gc.ca Received 20 April 2011; Revised 12 July 2011; Accepted 11 August 2011 1. INTRODUCTION In order to integrate large amounts of intermittent renew- able generation reliably and cost-effectively into electric- ity grids, system operators need both to understand the variability of these generators and to be able to forecast this variability at different spatial and temporal scales. Al- though the timescales relevant for forecasting vary, most system operators use a day-ahead commitment process to commit generators to meet the next day’s forecasted load. Moving closer to real time, updated conditions and forecasts are used to dispatch generators, secure reserves, and lock in imports and exports. Meanwhile, the geo- graphic area of interest for forecasting can vary from a large area over which electricity supply and demand must be balanced to a much smaller region where grid congestion must be managed. The motivation for the research presented here was the in- troduction of feed-in tariffs in the province of Ontario, Canada [1], which led to a rapid increase in the contracted and installed capacity of photovoltaics (PV) and other renew- ables in Ontario. The Ontario Independent Electricity System Operator (IESO) recently put forward a call for proposals to develop centralized wind forecasting for the province, which has an installed wind capacity of over 1.1 GW and a peak load of about 27 GW. Meanwhile, contracts for over 1 GW of PV systems have been offered under the Feed-In Tariff Program [1], and PV forecasting implementation should begin once installed PV capacity becomes comparable with the (current) installed wind capacity. PROGRESS IN PHOTOVOLTAICS: RESEARCH AND APPLICATIONS Prog. Photovolt: Res. Appl. (2011) Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/pip.1180 © Her Majesty the Queen in Right of Canada 2011. Reproduced with the permission of the Minister of Natural Resources Canada.