Abstract
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.
Highlights
Wind energy is receiving increasing attention due to reasons ranging from climate change to its status as the fastest-growing energy source globally
We present the application of a self-organizing maps (SOMs)-based analog ensemble method for medium-range wind power generation/variation forecasts by using multi-center ensemble forecast data, in order to support system operation for transmission grid operators
As discussed in a previous study of wind power and climate in Japan [19], the wind ramp events in East Japan are largely affected by synoptic circulation
Summary
Wind energy is receiving increasing attention due to reasons ranging from climate change to its status as the fastest-growing energy source globally. Statistical/empirical post-processing techniques for numerical weather forecasts are frequently used, powerful approaches that improve the impacts of model error or initial boundary conditions These techniques are used in various end-user applications, including estimates of renewable energy production. The goal of this study is to evaluate the ability of multi-model ensemble forecasts, in combination with SOM-based analog methods, to forecast probabilities of area-integrated wind power and ramps for medium-range lead times. SOMs were used to identify weather patterns (WPs) over East Japan, while using the analog approach for wind power forecasts up to one week in advance This method could be categorized as a hybrid ensemble method, as suggested by Eckel and Delle Monache [16], that is skillful compared with that based on a single deterministic forecast.
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