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Medium-Range Probabilistic Forecasts of Wind Power Generation and Ramps in Japan Based on a Hybrid Ensemble

Central Research Institute of Electric Power Industry, 1646 Abiko, Chiba 270-1194, Japan
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Atmosphere 2018, 9(11), 423; https://doi.org/10.3390/atmos9110423
Received: 20 September 2018 / Revised: 22 October 2018 / Accepted: 27 October 2018 / Published: 29 October 2018
(This article belongs to the Section Climatology and Meteorology)
This study shows the application of self-organizing maps (SOMs) to probabilistic forecasts of wind power generation and ramps in Japan. SOMs are applied to atmospheric variables obtained from the Japanese 55-year atmospheric Reanalysis JRA-55 over the region, thus deriving classified weather patterns (WPs). Probabilistic relationships are established between the synoptic-scale atmospheric variables over East Japan and the generation of regionally integrated wind power in East Japan. Medium-range probabilistic wind power predictions are derived by SOM as analog ensembles based on the WPs of the multi-center ensemble forecasts. As this analog approach handles stochastic uncertainties effectively, probabilistic wind power forecasts are rapidly generated from a very large number of forecast ensembles. The use of a multi-model ensemble provides better results than a one-forecast model. The hybrid ensemble forecasts further improve the probabilistic predictability skill of wind power generation compared with non-hybrid methods. It is expected that long-term wind forecasts will provide better guidance to transmission grid operators. The advantage of this method is that it can include an interpretative analysis of meteorological factors for variations in renewable energy. View Full-Text
Keywords: self-organizing maps; weather patterns; synoptic circulation; multi-model ensemble; wind power self-organizing maps; weather patterns; synoptic circulation; multi-model ensemble; wind power
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Ohba, M.; Kadokura, S.; Nohara, D. Medium-Range Probabilistic Forecasts of Wind Power Generation and Ramps in Japan Based on a Hybrid Ensemble. Atmosphere 2018, 9, 423.

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