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Forecasting Energy CO 2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

Author

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  • Hong Chang

    (Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China)

  • Wei Sun

    (School of Economics and Management, North China Electric Power University, Baoding, Hebei 071003, China)

  • Xingsheng Gu

    (Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China)

Abstract

he accurate forecasting of carbon dioxide (CO 2 ) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β) arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn’t consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE) objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO 2 emission of the World top‒5 CO 2 emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE) and mean absolute error (MAE) are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree.

Suggested Citation

  • Hong Chang & Wei Sun & Xingsheng Gu, 2013. "Forecasting Energy CO 2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model," Energies, MDPI, vol. 6(3), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:3:p:1456-1477:d:24016
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    References listed on IDEAS

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    1. Wenxiu Wang & Yaoqiu Kuang & Ningsheng Huang, 2011. "Study on the Decomposition of Factors Affecting Energy-Related Carbon Emissions in Guangdong Province, China," Energies, MDPI, vol. 4(12), pages 1-24, December.
    2. Lin, Chiun-Sin & Liou, Fen-May & Huang, Chih-Pin, 2011. "Grey forecasting model for CO2 emissions: A Taiwan study," Applied Energy, Elsevier, vol. 88(11), pages 3816-3820.
    3. Rout, Ullash K. & Voβ, Alfred & Singh, Anoop & Fahl, Ulrich & Blesl, Markus & Ó Gallachóir, Brian P., 2011. "Energy and emissions forecast of China over a long-time horizon," Energy, Elsevier, vol. 36(1), pages 1-11.
    4. Köne, Aylin Çigdem & Büke, Tayfun, 2010. "Forecasting of CO2 emissions from fuel combustion using trend analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2906-2915, December.
    5. Sun, J.W., 2006. "An alternative viewpoint on the forecasting of energy-related CO2 emissions in the OECD countries," Energy Policy, Elsevier, vol. 34(4), pages 377-382, March.
    6. Hibon, Michele & Evgeniou, Theodoros, 2005. "To combine or not to combine: selecting among forecasts and their combinations," International Journal of Forecasting, Elsevier, vol. 21(1), pages 15-24.
    7. Liang, Qiao-Mei & Fan, Ying & Wei, Yi-Ming, 2007. "Multi-regional input-output model for regional energy requirements and CO2 emissions in China," Energy Policy, Elsevier, vol. 35(3), pages 1685-1700, March.
    8. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    9. Ramanathan, Ramakrishnan, 2005. "An analysis of energy consumption and carbon dioxide emissions in countries of the Middle East and North Africa," Energy, Elsevier, vol. 30(15), pages 2831-2842.
    10. AkbostancI, Elif & Tunç, Gül Ipek & Türüt-AsIk, Serap, 2011. "CO2 emissions of Turkish manufacturing industry: A decomposition analysis," Applied Energy, Elsevier, vol. 88(6), pages 2273-2278, June.
    11. Meng, Ming & Niu, Dongxiao, 2011. "Modeling CO2 emissions from fossil fuel combustion using the logistic equation," Energy, Elsevier, vol. 36(5), pages 3355-3359.
    12. He, Jiankun & Deng, Jing & Su, Mingshan, 2010. "CO2 emission from China's energy sector and strategy for its control," Energy, Elsevier, vol. 35(11), pages 4494-4498.
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