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Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques

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  • Wang, Qiang
  • Li, Shuyu
  • Li, Rongrong

Abstract

Better forecasting energy demand in China and India can help those countries meet future challenges caused by the changes in that demand, as well as inform future global energy needs. In this study, the single-linear, hybrid-linear, and non-linear forecasting techniques based on grey theory are developed to more accurately forecasting energy demand in China and India. These prosed techniques were applied to simulate China’s and India’s energy consumption of China and India between 1990 and 2016. Three standards (trend map, error measure, and fit method) of analyzing quality of forecast technique are used to quantify the quality of these proposed technique. The results show these proposed techniques have a very high degree of fit, a low error rate, and high fitting precision. For example, the mean absolute percent error of single-linear, hybrid-linear, and non-linear techniques are 1.30–3.08%, 0.80–2.57%, and 2.06–2.19%, respectively. The results of optimality analysis show these proposed models can produce reliable forecasting results in China and India, which might be used to forecasting energy demand in other countries/regions. Our forecasting results show the annual growth rate of India’s energy demand from 2017 to 2026 will be 4.49%–5.21% (single-linear), 2.42%–7.04% (hybrid-linear), 0.58%–4.02% (non-linear), respectively. The annual growth rate of China’s energy demand from 2017 to 2026 will be 1.36%–1.70% (single-linear), 1.04%–1.49% (hybrid-linear), 1.80%–2.34% (non-linear), respectively. The growth rate of India’s energy consumption is expected to be 2–4 times that of China from 2017 to 2026, indicating India will become even more important in the global energy market.

Suggested Citation

  • Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    • Handle: RePEc:eee:energy:v:161:y:2018:i:c:p:821-831
    DOI: 10.1016/j.energy.2018.07.168
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    as
    1. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    2. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    3. 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.
    4. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    5. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
    6. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    7. Wang, Qiang & Li, Rongrong, 2016. "Drivers for energy consumption: A comparative analysis of China and India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 954-962.
    8. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
    9. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    10. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    11. 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.
    12. 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.
    13. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    14. Azadeh, A. & Babazadeh, R. & Asadzadeh, S.M., 2013. "Optimum estimation and forecasting of renewable energy consumption by artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 605-612.
    15. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    16. 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.
    17. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    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. Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
    20. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    21. 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.
    22. Wu, Lifeng & Liu, Sifeng & Liu, Dinglin & Fang, Zhigeng & Xu, Haiyan, 2015. "Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model," Energy, Elsevier, vol. 79(C), pages 489-495.
    23. Garg, Amit & Naswa, Prakriti & Shukla, P.R., 2015. "Energy infrastructure in India: Profile and risks under climate change," Energy Policy, Elsevier, vol. 81(C), pages 226-238.
    24. Li, Yanting & He, Yong & Su, Yan & Shu, Lianjie, 2016. "Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines," Applied Energy, Elsevier, vol. 180(C), pages 392-401.
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