Research on short-term wind power forecasting method based on incomplete data

Abstract

In accordance with the topographic features and other relevant factors, data affecting wind power generation in engineering practice may be difficult to obtain, and three short-term wind power forecasting methods are proposed based on incomplete data. Most wind power forecasting studies are based on wind speed data, but wind power forecasting needs more influential factors in addition to wind speed data, and it is difficult to obtain these data in actual production and life. Therefore, this paper proposes the wind power forecasting under the condition of incomplete data. First, this paper makes theoretical analysis on multi-state space Markov chain wind power forecasting, backpropagation (BP) neural network wind power forecasting, and genetic algorithm (GA)-BP neural network wind power forecasting, and the corresponding wind power forecasting models are constructed. Second, with the actually measured data of a wind farm in the Heilongjiang province as the research object, the historical data are preprocessed first and then imported into three wind power forecasting models for simulation, and the curves of relative error and absolute error of total wind power forecasting in this area are obtained. Finally, the simulation results of three methods based on incomplete data are analyzed and evaluated. The results show that under the condition of incomplete data, the maximum error of the GA-BP neural network wind power forecasting model improved by the genetic algorithm is reduced from 6.8% to 1.6%, and the forecasting accuracy is greatly improved.