A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans

How to consider both the influence of weather and wind power in modeling process probability distribution forecast error (WPFE), emphasize application value conditional modeling, is rarely studied at present. This paper proposes a novel method for WPFE. uses proposed MNSGA-II-Kmeans algorithm perform multi-objective clustering multi-dimensional influencing factors (MDIF), including power. It can maximize difference between distributions each MDIF mode’s WPFE while clustering, thus ensuring way. Based on results, by using versatile simulate support vector machine realize recognition modes, specific function be provided stochastic economic dispatch identifying data. A plant north China with historical data selected calculation. The results verify effectiveness method, comparison non-conditional that does not MDIF, it effectively increase consumption system.