Fill Missing Data for Wind Farms Using Long Short-Term Memory Based Recurrent Neural Network

摘要

Due to the uncertainty and volatility of wind energy resources, its large-scale consumption in power gird needs to be based on accurate prediction of output. This puts high demands on the integrity and accuracy of historical wind power data. However, in many wind farms, data loss due to equipment failure or human factors is common, which has a negative impact on wind power forecasting. In this paper, a Long Short-Term Memory (LSTM) strategy is incorporated in the recurrent neural network (RNN) to set up a prediction model and fill the wind power missing data, which behaves better than the traditional RNN methods. The case of this paper uses the historical wind power data of Liaoning Province, which obtains the ideal results, proving the validity of the proposed model and method.