Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating

Providing accurate forecasts for the future power generation of renewable energy power plants is critical for the power grid operation and related tasks. Probabilistic forecasts provide additional information in comparison to conventional point forecasts and can be used to retain optimal decision-making performance under uncertain future conditions. In this article, we present a hierarchical ensemble technique that combines multiple probabilistic forecasting techniques and multiple weather models (WM). In a first step, multiple probabilistic forecasting techniques create predictions on each weather model individually. Thereafter, this set of probabilistic forecasts from each WM is aggregated to an overall refined prediction using a novel dynamic weighting technique which we call gradual coopetitive soft gating. The proposed combination technique introduces a weather situation-adaptive and lead-time dependent weighting that better exploits the advantages of single predictive models in the overall ensemble. We compare the proposed method to other state of the art probabilistic forecasting methods and weather ensemble methods on 37 data sets of wind farms with multi-model weather forecasts. The proposed technique is able to achieve statistically significant performance improvements compared to other techniques.