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Nonlinear interval regression analysis with neural networks and grey prediction for energy demand forecasting

  • Data analytics and machine learning
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Abstract

Predicting energy demand plays an important role in devising energy development plans for cities and countries. Available data on energy demand usually consist of a nonlinear real-valued sequence, but the samples are often derived from uncertain assessments without satisfying any statistical assumptions. This study thus establishes interval grey prediction models without statistical assumptions by using data intervals to represent uncertainty in energy demand forecasting. The proposed prediction models first apply nonlinear regression analysis using neural networks to determine the interval data. The models then employ grey prediction to derive the tendency of the upper and lower limits of energy demand. Finally, the best non-fuzzy performance value can be further obtained for each time point using the estimated upper and lower limits. The advantage of the proposed models is that hyper-parameter settings involving residual modification and machine learning are not a serious problem, and the construction is simple enough to implement as a computer program without any statistical assumptions. The forecasting accuracy of the proposed models was verified using actual energy demand data. The results showed that the proposed grey-prediction-based models using functional-link nets to modify residuals performed well compared to other interval grey prediction models.

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Acknowledgements

The authors would like to thank the anonymous referees for their valuable comments.

Funding

This research is supported by the Ministry of Science and Technology, Taiwan, under grant MOST 108-2410-H-033-038-MY2 and MOST 110-2410-H-033-013-MY2.

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Authors and Affiliations

  1. Department of Business Administration, Chung Yuan Christian University, Taoyuan, Taiwan

    Yi-Chung Hu

  2. College of Civil Engineering, Yango University, Fuzhou, China

    Wen-Bao Wang

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  1. Yi-Chung Hu
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  2. Wen-Bao Wang
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Correspondence to Yi-Chung Hu.

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Hu, YC., Wang, WB. Nonlinear interval regression analysis with neural networks and grey prediction for energy demand forecasting. Soft Comput 26, 6529–6545 (2022). https://doi.org/10.1007/s00500-022-07168-8

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