LSTM EFG for wind power forecasting based on sequential correlation features
Abstract Amid the gradual increase of wind power generation, how to relieve the pressure of peak load and frequency regulation to the power system by wind power forecasting to make it run steadily becomes a key issue. Due to the continuous development of the field of artificial intelligence, neural network, as a machine learning technology, has shown a good predictive effect in time series data forecasting. Long term short term Memory is a kind of time recursive neural network, which is suitable for processing and predicting events with relatively Long intervals and delays in time series. This paper proposes an improved Long Short Term Memory enhanced forget gate network model, abbreviated as LSTM EFG, used to forecasting wind power. Based on the correlation, the features data of turbine groups in certain distance are filtered to further optimize the forecasting effect on wind power by clustering. The results show that the method with Spectral Clustering has an higher accuracy with an increase of 18.3% than those of the other forecasting models, and at the same time the convergence process has been sped up.
Future Gener. Comput. Syst.