Affiliations: [a] College of Information Science and Engineering, Northeastern University, Shenyang, China
| [b] College of Resources and Civil Engineering, Northeastern University, Shenyang, China
| [c] State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China
Correspondence:
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Corresponding author. Yuqing Chang, College of Information Science and Engineering, Northeastern University, Shenyang, China. E-mail: [email protected].
Abstract: Accurate and stable wind speed forecasting is an essential means to ensure the safe and stable operation of wind power integration. Therefore, a new hybrid model was proposed to improve wind speed forecasting performance, consisting of data pre-processing, model forecasting, and error correction (EC). The specific modeling process is as follows: (a) A wind speed series was decomposed into a series of subseries with different frequencies utilizing the ensemble empirical mode decomposition (EEMD) method. Afterward, various subseries were divided into high-frequency components, intermediate-frequency components, and low-frequency components based on their sample entropies (SE). (b) Three frequency components were forecast by separately employing the hybrid model of convolutional neural network and long short-term memory network (CNN-LSTM), long short-term memory network (LSTM), and Elman neural network. (c) Subsequently, an error sequence was further forecast using CNN-LSTM. (d) Finally, three actual datasets were used to forecast the multi-step wind speed, and the forecasting performance of the proposed model was verified. The test results show that the forecasting performance of the proposed model is better than the other 13 models in three actual datasets.
