Prediction of the total solar irradiance based on the CEEMDAN-BiGRU-Attention model

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.

KEYWORDS:

• Total solar irradiance
• time series prediction
• CEEMDAN
• attention mechanism
• BiGRU

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

Additional information

Funding

This research received no external funding.

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.