A novel ensemble learning-based grey model for electricity supply forecasting in China

Yubin Cai; Xin Ma; Yubin Cai; Xin Ma

doi:10.3934/math.2021714

AIMS Mathematics

2021, Volume 6, Issue 11: 12339-12358. doi: 10.3934/math.2021714

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Research article

A novel ensemble learning-based grey model for electricity supply forecasting in China

Yubin Cai ¹,
Xin Ma ^{1,2
,
,}

1.
School of Science, Southwest University of Science and Technology, Mianyang 621010, China
2.
School of Economics & Management, Southwest University of Science and Technology, Mianyang 621010, China

Received: 04 June 2021 Accepted: 16 August 2021 Published: 27 August 2021
MSC : 91D10

Electricity consumption is one of the most important indicators reflecting the industrialization of a country. Supply of electricity power plays an import role in guaranteeing the running of a country. However, with complex circumstances, it is often difficult to make accurate forecasting with limited reliable data sets. In order to take most advantages of the existing grey system model, the ensemble learning is adopted to provide a new stratagy of building forecasting models for electricity supply of China. The nonhomogeneous grey model with different types of accumulation is firstly fitted with multiple setting of acculumation degrees. Then the majority voting is used to select and combine the most accurate and stable models validated by the grid search cross validation. Two numerical validation cases are taken to validate the proposed method in comparison with other well-known models. Results of the real-world case study of forecasting the electricity supply of China indicate that the proposed model outperforms the other 15 exisiting grey models, which illustrates the proposed model can make much more accurate and stable forecasting in such real-world applications.
- grey system,
- ensemble learning,
- grid search,
- electricity supply
Citation: Yubin Cai, Xin Ma. A novel ensemble learning-based grey model for electricity supply forecasting in China[J]. AIMS Mathematics, 2021, 6(11): 12339-12358. doi: 10.3934/math.2021714

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Abstract

Electricity consumption is one of the most important indicators reflecting the industrialization of a country. Supply of electricity power plays an import role in guaranteeing the running of a country. However, with complex circumstances, it is often difficult to make accurate forecasting with limited reliable data sets. In order to take most advantages of the existing grey system model, the ensemble learning is adopted to provide a new stratagy of building forecasting models for electricity supply of China. The nonhomogeneous grey model with different types of accumulation is firstly fitted with multiple setting of acculumation degrees. Then the majority voting is used to select and combine the most accurate and stable models validated by the grid search cross validation. Two numerical validation cases are taken to validate the proposed method in comparison with other well-known models. Results of the real-world case study of forecasting the electricity supply of China indicate that the proposed model outperforms the other 15 exisiting grey models, which illustrates the proposed model can make much more accurate and stable forecasting in such real-world applications.

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