Research on Wind Power Ultra-short-term Forecasting Method Based on PCA-LSTM

Abstract

Wind power ultra-short-term forecasting can provide the support for adjusting the intraday power generation plan, carrying out the incremental spot trading of wind power, and improving the utilization of wind power. In order to improve the forecast accuracy of wind power, a wind power ultra-short-term power forecast method based on long-term-term memory (LSTM) network is proposed. First, the principal component analysis method is used to reduce the multivariate meteorological time series dimension. Then by using the cyclic memory characteristics of LSTM network to model multi-dimensional time series, the nonlinear mapping relationship between meteorological data and power data is established, and the wind power forecast is finally realized. The actual data of the eastern China wind farm is used to verify the results. It shows the method established in this paper can effectively use the meteorological and power data to forecast the wind power, and compared with the traditional time series, BP neural network method, the method in this paper has higher forecast accuracy and has broad application potential.