IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v118y2017icp473-480.html
   My bibliography  Save this article

Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China

Author

Listed:
  • Xu, Ning
  • Dang, Yaoguo
  • Gong, Yande

Abstract

Forecasting of electricity energy consumption (EEC) has been always playing a vital role in China's power system management, and requires promising prediction techniques. This paper proposed an optimized hybrid GM(1,1) model to improve prediction accuracy of EEC in short term. GM(1,1) model, in spite of successful employing in various fields, sometimes gives rise to inaccurate solution in practical applications. Time response function (TRF) is an important factor deeply influencing modeling precision. Aiming to enhance forecasting performance, this paper proposed a novel grey model with optimal time response function, referred to as IRGM(1,1) model. As of unknown variables in TRF, a nonlinear optimization method, based on particle swarm algorithm, is constructed to obtain optimal values, for shrinking simulation errors and improving adaptability to characteristics of raw data. The forecasting performance has been confirmed by electricity consumption data of China, comparing with three alternative grey models. Application demonstrates that the proposed method can significantly promote modeling accuracy.

Suggested Citation

  • Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.
    • Handle: RePEc:eee:energy:v:118:y:2017:i:c:p:473-480
    DOI: 10.1016/j.energy.2016.10.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216314244
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.10.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    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. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    3. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    4. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    5. Truong, D.Q. & Ahn, K.K., 2012. "Wave prediction based on a modified grey model MGM(1,1) for real-time control of wave energy converters in irregular waves," Renewable Energy, Elsevier, vol. 43(C), pages 242-255.
    6. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    7. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.
    8. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    9. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    10. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
    11. 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.
    12. Xiong, Ping-ping & Dang, Yao-guo & Yao, Tian-xiang & Wang, Zheng-xin, 2014. "Optimal modeling and forecasting of the energy consumption and production in China," Energy, Elsevier, vol. 77(C), pages 623-634.
    13. Li, Guo-Dong & Masuda, Shiro & Nagai, Masatake, 2012. "An optimal hybrid model for atomic power generation prediction in Japan," Energy, Elsevier, vol. 45(1), pages 655-661.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    3. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    4. Weiwei Pan & Lirong Jian & Tao Liu, 2019. "Grey system theory trends from 1991 to 2018: a bibliometric analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1407-1434, December.
    5. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    6. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    7. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    8. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy," Energies, MDPI, vol. 11(8), pages 1-18, August.
    9. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
    10. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
    11. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    12. Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60, February.
    13. Nana Geng & Yong Zhang & Yixiang Sun & Yunjian Jiang & Dandan Chen, 2015. "Forecasting China’s Annual Biofuel Production Using an Improved Grey Model," Energies, MDPI, vol. 8(10), pages 1-20, October.
    14. Ling-Ling Pei & Qin Li & Zheng-Xin Wang, 2018. "The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China," IJERPH, MDPI, vol. 15(3), pages 1-17, March.
    15. Liu, Xiuli & Moreno, Blanca & García, Ana Salomé, 2016. "A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors," Energy, Elsevier, vol. 115(P1), pages 1042-1054.
    16. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
    17. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    18. Wang, Jianzhou & Jiang, Haiyan & Zhou, Qingping & Wu, Jie & Qin, Shanshan, 2016. "China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1149-1167.
    19. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    20. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:118:y:2017:i:c:p:473-480. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.