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Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory

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  • Tang, Lei
  • Wang, Xifan
  • Wang, Xiuli
  • Shao, Chengcheng
  • Liu, Shiyu
  • Tian, Shijun

Abstract

Long-term electricity consumption (EC) forecasting is a very important part for the expansion planning of power system. Instead of point forecasting, based on fuzzy Bayesian theory and expert prediction, a novel long-term probability forecasting model is proposed to predict the Chinese per-capita electricity consumption (PEC) and its variation interval over the period 2010–2030. The special model structure can improve the reliability and accuracy of expert prediction through econometric methodology. It contains three components: fuzzy relation matrix, prior prediction, and fuzzy Bayesian formula. To contend with the long-term uncertainty, the prior prediction is implemented to combine the advantages of expert's experience with other time-based methods from the perspective of probability. With the utilization of fuzzy technique, the multiple effects of influencing factors (IFs) on PEC can be expressed as a fuzzy relation matrix. It can rule the results of prior prediction to obey the long-run equilibrium relationship of natural evolution thorough probability calibration. To demonstrate its efficiency and applicability, the result of this method is compared with that of other 6 approaches and 4 agencies. The case study shows that the proposed methodology has higher accuracy and adaptability.

Suggested Citation

  • Tang, Lei & Wang, Xifan & Wang, Xiuli & Shao, Chengcheng & Liu, Shiyu & Tian, Shijun, 2019. "Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory," Energy, Elsevier, vol. 167(C), pages 1144-1154.
    • Handle: RePEc:eee:energy:v:167:y:2019:i:c:p:1144-1154
    DOI: 10.1016/j.energy.2018.10.073
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    as
    1. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    2. Hu, Zhongyi & Bao, Yukun & Chiong, Raymond & Xiong, Tao, 2015. "Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection," Energy, Elsevier, vol. 84(C), pages 419-431.
    3. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    4. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    5. Morris, Peter & Vine, Desley & Buys, Laurie, 2015. "Application of a Bayesian Network complex system model to a successful community electricity demand reduction program," Energy, Elsevier, vol. 84(C), pages 63-74.
    6. Shao, Zhen & Gao, Fei & Zhang, Qiang & Yang, Shan-Lin, 2015. "Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting i," Applied Energy, Elsevier, vol. 156(C), pages 502-518.
    7. ToksarI, M. Duran, 2009. "Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey," Energy Policy, Elsevier, vol. 37(3), pages 1181-1187, March.
    8. Ozturk, Ilhan & Aslan, Alper & Kalyoncu, Huseyin, 2010. "Energy consumption and economic growth relationship: Evidence from panel data for low and middle income countries," Energy Policy, Elsevier, vol. 38(8), pages 4422-4428, August.
    9. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
    10. Liddle, Brantley, 2010. "Revisiting world energy intensity convergence for regional differences," Applied Energy, Elsevier, vol. 87(10), pages 3218-3225, October.
    11. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    12. 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.
    13. Henze, Gregor P. & Pavlak, Gregory S. & Florita, Anthony R. & Dodier, Robert H. & Hirsch, Adam I., 2015. "An energy signal tool for decision support in building energy systems," Applied Energy, Elsevier, vol. 138(C), pages 51-70.
    14. Zhou, Nan & Fridley, David & Khanna, Nina Zheng & Ke, Jing & McNeil, Michael & Levine, Mark, 2013. "China's energy and emissions outlook to 2050: Perspectives from bottom-up energy end-use model," Energy Policy, Elsevier, vol. 53(C), pages 51-62.
    15. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    16. Elliott, Robert J.R. & Sun, Puyang & Zhu, Tong, 2017. "The direct and indirect effect of urbanization on energy intensity: A province-level study for China," Energy, Elsevier, vol. 123(C), pages 677-692.
    17. Wang, S.S. & Zhou, D.Q. & Zhou, P. & Wang, Q.W., 2011. "CO2 emissions, energy consumption and economic growth in China: A panel data analysis," Energy Policy, Elsevier, vol. 39(9), pages 4870-4875, September.
    18. Xu, Weijun & Gu, Ren & Liu, Youzhu & Dai, Yongwu, 2015. "Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China," Economic Modelling, Elsevier, vol. 45(C), pages 127-135.
    19. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
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    5. Xingcai Zhou & Jiangyan Wang, 2021. "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers 2103.00711, arXiv.org.
    6. Liu, Yitong & Xue, Dingyu & Yang, Yang, 2021. "Two types of conformable fractional grey interval models and their applications in regional electricity consumption prediction," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    7. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    8. Tang, Lei & Guo, Jue & Zhao, Boyang & Wang, Xiuli & Shao, Chengcheng & Wang, Yifei, 2021. "Power generation mix evolution based on rolling horizon optimal approach: A system dynamics analysis," Energy, Elsevier, vol. 224(C).
    9. Hamed, Mohammad M. & Ali, Hesham & Abdelal, Qasem, 2022. "Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances," Energy, Elsevier, vol. 255(C).
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    12. Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).

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