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A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy

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  • Luo, Xilin
  • Duan, Huiming
  • He, Leiyuhang

Abstract

and accurate prediction of clean energy can supply an important reference for governments to formulate social and economic development policies. This paper begins with the logistic equation which is the whitening equation of the Verhulst model, introduces the Riccati equation with constant coefficients to optimize the whitening equation, and establishes a grey prediction model (CCRGM(1,1)) based on the Riccati equation. This model organically combines the characteristics of the grey model, and flexibly improves the modelling precision. Furthermore, the nonlinear term is optimized by the simulated annealing algorithm. To illustrate the validation of the new model, two kinds of clean energy consumption in the actual area are selected as the research objects. Compared with six other grey prediction models, CCRGM(1,1) model has the highest accuracy in simulation and prediction. Finally, this model is used to predict the nuclear and hydroelectricity energy consumption in North America from 2019 to 2028. The results predict that nuclear energy consumption will keep rising in the next decade, while hydroelectricity energy consumption will rise to a peak and subsequently fall back, which offers important information for the governments of North America to formulate energy measures.

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  • Luo, Xilin & Duan, Huiming & He, Leiyuhang, 2020. "A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy," Energy, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:energy:v:205:y:2020:i:c:s0360544220311920
    DOI: 10.1016/j.energy.2020.118085
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    as
    1. Irene Samora & Mário J. Franca & Anton J. Schleiss & Helena M. Ramos, 2016. "Simulated Annealing in Optimization of Energy Production in a Water Supply Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1533-1547, March.
    2. Huiming Duan & Guang Rong Lei & Kailiang Shao, 2018. "Forecasting Crude Oil Consumption in China Using a Grey Prediction Model with an Optimal Fractional-Order Accumulating Operator," Complexity, Hindawi, vol. 2018, pages 1-12, August.
    3. Shuyu Li & Xue Yang & Rongrong Li, 2018. "Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
    4. Liu, Xiaoyong & Fu, Hui, 2016. "Volatility forecasting for interbank offered rate using grey extreme learning machine: The case of China," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 249-254.
    5. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    6. Irene Samora & Mário Franca & Anton Schleiss & Helena Ramos, 2016. "Simulated Annealing in Optimization of Energy Production in a Water Supply Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1533-1547, March.
    7. Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
    8. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    9. Midilli, Adnan & Dincer, Ibrahim & Ay, Murat, 2006. "Green energy strategies for sustainable development," Energy Policy, Elsevier, vol. 34(18), pages 3623-3633, December.
    10. 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.
    11. Huiming Duan & Xinping Xiao, 2019. "A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors," Complexity, Hindawi, vol. 2019, pages 1-18, June.
    12. Wang, Li & Xie, Yuxin & Wang, Xiaoyi & Xu, Jiping & Zhang, Huiyan & Yu, Jiabin & Sun, Qian & Zhao, Zhiyao, 2019. "Meteorological sequence prediction based on multivariate space-time auto regression model and fractional calculus grey model," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 203-209.
    13. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
    14. Ma, Xin & Mei, Xie & Wu, Wenqing & Wu, Xinxing & Zeng, Bo, 2019. "A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China," Energy, Elsevier, vol. 178(C), pages 487-507.
    15. Nowotny, Janusz & Dodson, John & Fiechter, Sebastian & Gür, Turgut M. & Kennedy, Brendan & Macyk, Wojciech & Bak, Tadeusz & Sigmund, Wolfgang & Yamawaki, Michio & Rahman, Kazi A., 2018. "Towards global sustainability: Education on environmentally clean energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2541-2551.
    16. Zeng, Bo & Duan, Huiming & Bai, Yun & Meng, Wei, 2018. "Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator," Energy, Elsevier, vol. 151(C), pages 238-249.
    17. 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.
    18. 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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