A new wind power forecasting algorithm based on long short‐term memory neural network

Abstract

Wind power is one of the most large-scale new energy sources. But wind power instability will affect power grid safety, which is in a great need for wind power forecasting algorithms. To accurately predict wind power and reduce power grid fluctuations, it proposes a new wind power forecasting (WPF) algorithm based on long short-term memory (LSTM) neural network using wind farm real operation data. First, the wind farm power data are de-averaged and divided into two different sets in order to meet the requirements of the algorithm. Then, structure of the LSTM neural network is designed and hyper-parameters are adjusted to improve accuracy of forecasting. Finally, the definite LSTM neural network is used for forecasting the power data in time series to derive the power forecasting value, which is reduced and evaluated according to the original size. The results show that compared with other forecasting methods, in short-term WPF at different time scales, this algorithm has smaller errors in power forecasting results, which is also suitable for long-term WPF.