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Title:Wind Forecasting Using HARMONIE with Bayes Model Averaging for Fine-tuning
Authors:Martin B. Peters, Enda O'Brien, Alastair McKinstry, Adam Ralph, 2013
Abstract: Wind-speed forecasts for a wind-farm in southwest Ireland were made for over one year using the operational HARMONIE mesoscale weather forecast model, and Bayes Model Averaging (BMA) for statistical post-processing to remove systematic local bias. The deterministic forecasts alone generated mean absolute errors of 1.7−2.0 ms−1 out to 24 hrs, when interpolated to the location of the met-mast. Application of BMA reduced these errors by about 15%, to 1.5−1.6 ms-1, on average. Forecast errors do not degrade significantly as forecast lead-time increases, at least out to 24 hours.
ICHEC Project:
Publication:European Geosciences Union General Assembly 2013, EGUDivision Energy, Resources & the Environment (ERE). Energy Procedia, vol 40 (2013), pp 95-101
URL: http://dx.doi.org/10.1016/j.egypro.2013.08.012
Keywords: wind-farm; forecasting; HARMONIE; Bayes Model Averaging
Status: Published

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