Probabilistic prediction of short term wind power considering temporal and spatial dependence of prediction error

Abstract

The short-term probabilistic prediction of wind power has the characteristics of spatial dependence and time-series dependence. Considering the two characteristics at the same time can improve the prediction level. In this paper, a probabilistic short-term wind power prediction model considering the temporal and spatial dependence of prediction error is proposed. Considering the coupling relationship between the two properties, the NWP(Numerical Weather Prediction) wind speed point prediction error in the historical period is hierarchical clustering, and the empirical distribution model is used to fit the probability distribution of the error under different wind conditions; the cumulative empirical distribution probability value corresponding to the NWP wind speed at the time to be predicted is bootstrap sampling; under the given confidence level, the possible wind speed at each time point to be predicted in the short term is calculated The power fluctuation range of the generator. The test results show that this method can ensure the statistical significance and fitting stability of the sub sample set at the same time, and improve the classification accuracy of the days to be predicted. Compared with considering a single property, the result of probability prediction performs better on multiple evaluation indexes.