ABSTRACT
Total solar irradiance (TSI) data play an important role in guiding production, but they are difficult to predict accurately because of nonlinearity and noise. In this paper, a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a bidirectional gated recurrent units network (BiGRU) that introduces an attention mechanism into adaptive noise is proposed. First, the CEEMDAN method is used to decompose the TSI. Then, the fuzzy entropy (FE) of each component is calculated, and the input sequence is synthesized by K-means clustering of the near waves. Finally, the combined sequences are trained in a BiGRU model with multiple hidden layers, and the attention mechanism acts on the time step. In this paper, the TSI at 5485 time points was predicted with MAE, MSE and MAPE values of 0.1122, 0.0259, and 0.00008, respectively. Compared with BiGRU, the hybrid model decreased by 11.74% on MAE, 5.29% on MSE and 0.009% on MAPRE. The CEEMDAN-BiGRU-Attention model has good application prospects in the field of TSI prediction.
Disclosure statement
No potential conflict of interest was reported by the authors.
Author contributions
Conceptualization, X. J.; methodology, N.C.; formal analysis, J.H.; data curation, Y.L.; supervision, Y.L. and X.L.; writing – original draft preparation, N.C.; writing – review and editing, X.J. All authors have read and agreed to the published version of the manuscript.
Data availability statement
The public dataset used in this article is from the Kaggle website: https://www.kaggle.com/datasets/brsdincer/total-solar-irradiance-sidc
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Notes on contributors
Xuchu Jiang
Mr. Xuchu Jiang is a lecturer in the School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China.
Nisang Chen
Ms. Nisang Chen is an undergraduate student in the School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China.
Jinghong Huang
Mr. Jinghong Huang is an undergraduate student in the School of Public Finance and Taxation, Zhongnan University of Economics and Law, Wuhan, China.
Ying Li
Ms. Ying Li is a lecturer in the Scientific Research Department, Zhongnan University of Economics and Law, Wuhan, China.
Xiaobing Luo
Mr. Xiaobing Luo is a researcher in the Hubei Emergency Rescue Center, Wuhan, China.