Abstract
Accurate wind speed forecasting is of great significance for a reliable and secure power generation system. In order to improve forecasting accuracy, this paper introduces the LSTM neural network and proposes a wind speed statistical forecasting method based on the EEMD-FuzzyEn-LSTMNN model. Moreover, the MIC is used to analyze the autocorrelation of wind speed series, and the predictable time of wind speed statistical forecasting method for direct multistep forecasting is taken as four hours. In the EEMD-FuzzyEn-LSTMNN model, the original wind speed series is firstly decomposed into a series of components by using EEMD. Then, the FuzzyEn is used to calculate the complexity of each component, and the components with similar FuzzyEn values are classified into one group. Finally, the LSTMNN model is used to forecast each subsequence after classification. The forecasting result of the original wind speed series is obtained by aggregating the forecasting result of each subsequence. Three forecasting cases under different terrain conditions were selected to validate the proposed model, and the BPNN model, the SVM model and the LSTMNN model were used for comparison. The experimental results show that the forecasting accuracy of the EEMD-FuzzyEn-LSTMNN model is much higher than that of the other three models.
Highlights
With the large consumption of fossil energy and the impact of global climate change, increasing attention has turned to renewable energy
In order to improve the forecasting accuracy of the long short-term memory neural network (LSTMNN) model, this paper proposes a novel wind speed statistical forecasting method based on the ensemble empirical mode decomposition (EEMD)-Fuzzy Entropy (FuzzyEn)-LSTMNN model
It is obvious that each wind speed series is decomposed into 13 whichcomponents are respectively denoted by intrinsic mode functions (IMFs)
Summary
With the large consumption of fossil energy and the impact of global climate change, increasing attention has turned to renewable energy. As a kind of clean and renewable energy, wind power has been widely valued and promoted in the world. According to the World Wind Energy Association statistics, the total cumulative installed capacity from wind power around the world was approximately. With the expansion of wind power generation, its problems have gradually become more prominent. Fluctuation and intermittence nature of wind speed, the wind power output has great uncertainty [1,2]. Large-scale wind power integration will bring great hidden dangers to the safe and stable operation of the power system
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