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Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission

Author

Listed:
  • Yi Liang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Dongxiao Niu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Ye Cao

    (College of Management and Economy, Beijing Institute of Technology, Beijing 100081, China)

  • Wei-Chiang Hong

    (Department of Information Management, Oriental Institute of Technology, New Taipei 220, Taiwan)

Abstract

The power industry is the main battlefield of CO 2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD) with induced ordered weighted harmonic averaging operator (IOWHA) to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM) forecasting model and multiple regression (MR) model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure.

Suggested Citation

  • Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, vol. 9(11), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:941-:d:82661
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    References listed on IDEAS

    as
    1. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio Sánchez-Esguevillas, 2012. "Classification and Clustering of Electricity Demand Patterns in Industrial Parks," Energies, MDPI, vol. 5(12), pages 1-14, December.
    2. Pérez-García, Julián & Moral-Carcedo, Julián, 2016. "Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain," Energy, Elsevier, vol. 97(C), pages 127-143.
    3. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    4. Yang, Lisha & Lin, Boqiang, 2016. "Carbon dioxide-emission in China׳s power industry: Evidence and policy implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 258-267.
    5. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    6. Yusuke Kishita & Yohei Yamaguchi & Yasushi Umeda & Yoshiyuki Shimoda & Minako Hara & Atsushi Sakurai & Hiroki Oka & Yuriko Tanaka, 2016. "Describing Long-Term Electricity Demand Scenarios in the Telecommunications Industry: A Case Study of Japan," Sustainability, MDPI, vol. 8(1), pages 1-16, January.
    7. Zeng, Ming & Yang, Yongqi & Wang, Lihua & Sun, Jinghui, 2016. "The power industry reform in China 2015: Policies, evaluations and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 94-110.
    8. Trotter, Ian M. & Bolkesjø, Torjus Folsland & Féres, José Gustavo & Hollanda, Lavinia, 2016. "Climate change and electricity demand in Brazil: A stochastic approach," Energy, Elsevier, vol. 102(C), pages 596-604.
    9. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    10. Hussain, Anwar & Rahman, Muhammad & Memon, Junaid Alam, 2016. "Forecasting electricity consumption in Pakistan: the way forward," Energy Policy, Elsevier, vol. 90(C), pages 73-80.
    11. Schweizer, Vanessa J. & Morgan, M. Granger, 2016. "Bounding US electricity demand in 2050," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 215-223.
    12. Karin Kandananond, 2011. "Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach," Energies, MDPI, vol. 4(8), pages 1-12, August.
    13. Zhang, Chi & Yan, Jinyue, 2015. "CDM’s influence on technology transfers: A study of the implemented clean development mechanism projects in China," Applied Energy, Elsevier, vol. 158(C), pages 355-365.
    14. Xie, Chunping & Hawkes, Adam D., 2015. "Estimation of inter-fuel substitution possibilities in China's transport industry using ridge regression," Energy, Elsevier, vol. 88(C), pages 260-267.
    15. Torrini, Fabiano Castro & Souza, Reinaldo Castro & Cyrino Oliveira, Fernando Luiz & Moreira Pessanha, Jose Francisco, 2016. "Long term electricity consumption forecast in Brazil: A fuzzy logic approach," Socio-Economic Planning Sciences, Elsevier, vol. 54(C), pages 18-27.
    16. Maria Chiara D'Errico & Carlo Andrea Bollino, 2015. "Bayesian Analysis of Demand Elasticity in the Italian Electricity Market," Sustainability, MDPI, vol. 7(9), pages 1-22, September.
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