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Solar and photovoltaic forecasting through post‐processing of the Global Environmental Multiscale numerical weather prediction model by Sophie Pelland; George Galanis; George Kallos is a Engineering article available to read on EtoBox.

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## 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

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Author

Sophie Pelland; George Galanis; George Kallos

Publisher

Wiley

Published

2011

Language

EN

Field

Engineering (Physical Sciences)