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Analysis of regional climate variables by using neural Granger causality

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Abstract

In recent years, how to discover causality rather than correlation among climate variables and how to use causality to help in time-series tasks have received great concern. However, the high dimensionality and nonlinearity of climate variables are the main issues for causal inference based on historical large-scale climate time series. Therefore, a method based on neural Granger causality inference is proposed to study the interactions of climate variables, with focus on the variables commonly used in the energy field, especially in photovoltaics. Firstly, for each climate variable, the time-varying causality and global causality are, respectively, obtained on each time window and on the whole series by neural Granger causality inference. Secondly, the global causality is used as a feature selection map of the input variables in the prediction task. Finally, compared with some existing feature selection methods, the experiments determine that the proposed method not only reveals the appropriate causality rather than the correlation among climate variables, but also efficiently reduces the input dimensionality and improves the performance and interpretability of the predicting model.

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All data generated or analyzed during this study are included in this published article (and its supplementary information files).

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFB1500800), the National Natural Science Foundation of China(Grant No. 61773118, Grant No. 61973083), Science and Technology Project of State Grid Corporation of China (Intelligent operation and maintenance technology of distributed photovoltaic system SGTJDK00DYJS2000148). Social Development Project of Science and Technology Innovation Action Plan of Shanghai (No. 20dz1207102).

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Authors and Affiliations

  1. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, 210096, People’s Republic of China

    Shuo Shan, Yiye Wang, Kanjian Zhang & Haikun Wei

  2. School of Automation, Southeast University, Nanjing, 210096, People’s Republic of China

    Shuo Shan, Yiye Wang, Kanjian Zhang & Haikun Wei

  3. Beihang University, Beijing, 100191, People’s Republic of China

    Xiangying Xie

  4. State Grid Electronic Commerce Co., Ltd., Beijing, 100053, People’s Republic of China

    Xiangying Xie & Tao Fan

  5. Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, People’s Republic of China

    Yushun Xiao

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  1. Shuo Shan
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Correspondence to Haikun Wei.

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Shan, S., Wang, Y., Xie, X. et al. Analysis of regional climate variables by using neural Granger causality. Neural Comput & Applic 35, 16381–16402 (2023). https://doi.org/10.1007/s00521-023-08506-z

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