A multi-step probability density prediction model based on gaussian approximation of quantiles for offshore wind power

With the increasing utilization of offshore wind power, accurate prediction of offshore wind power is crucial for preventive control and scheduling. In this paper, a new hybrid probability density model is proposed for multi-step offshore wind power prediction, including time varying filter based empirical mode decomposition (TVFEMD), approximate entropy (AE), Yeo–Johnson transform quantile regression (YJQR) and gaussian approximation of quantile (GAQ). Firstly, TVFEMD decomposition and AE theory are used to preprocess the original data for reducing the complexity and modeling workload. Secondly, the 16-step ahead offshore wind power is predicted using YJQR, in which the model structures established for each component are selected by grid search for comprehensive optimization to ensure the best prediction performance. Finally, the GAQ method is adopted to construct probability density curves for the 16-step cumulative quantile prediction results. The variance of the probability density curves in each step is adjusted to optimize the interval prediction results, resulting in more robust and integrated prediction results. Taking the historical offshore wind power data collected by German transmission system operator 50 Hertz as an example, the model has higher prediction accuracy and stability on the basis of obtaining reasonable quantile estimation results in multi-step offshore wind power probabilistic prediction.