Predicting China's energy consumption using a novel grey Riccati model

Abstract This paper studies the China’s oil consumption and the China’s nuclear energy consumption by a grey Riccati model. The newly developed model is analysed by the trapezoidal formula of definite integrals, the theory of ordinary differential equations and the grey technique. And some special cases including the GM(1,1) model, the grey Verhulst model and the grey Bass model are all discussed. Meanwhile, the hybrid of the simulated annealing algorithm and the genetic algorithm is utilized to search optimal background values. Further, the performance of the new model is verified through some experiments. Finally, the model is applied to study China’s energy consumption with original sequences from 2001 to 2018 claimed by British Petroleum Statistical Review of World Energy 2019, and the results show that the new model can obtain competitive results and better than other comparative models.

[1]  Bo Zeng,et al.  Modeling Method of the Grey GM(1, 1) Model with Interval Grey Action Quantity and Its Application , 2020, Complex..

[2]  Ping-ping Xiong,et al.  Grey extended prediction model based on IRLS and its application on smog pollution , 2019, Appl. Soft Comput..

[3]  Yong Wang,et al.  MODELING THE NONLINEAR FLOW FOR A MULTIPLE-FRACTURED HORIZONTAL WELL WITH MULTIPLE FINITE-CONDUCTIVITY FRACTURES IN TRIPLE MEDIA CARBONATE RESERVOIR , 2018 .

[4]  Lee-Ing Tong,et al.  Forecasting energy consumption using a grey model improved by incorporating genetic programming , 2011 .

[5]  Jianzhou Wang,et al.  A novel hybrid model for short-term wind power forecasting , 2019, Appl. Soft Comput..

[6]  Minda Ma,et al.  Do commercial building sector-derived carbon emissions decouple from the economic growth in Tertiary Industry? A case study of four municipalities in China. , 2019, The Science of the total environment.

[7]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[8]  Yi Lin,et al.  Grey Systems: Theory and Applications , 2010 .

[9]  Jianzhou Wang,et al.  Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China , 2019, Journal of Cleaner Production.

[10]  Wenqing Wu,et al.  Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model , 2019, Renewable Energy.

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Gary R. Weckman,et al.  An overview of energy demand forecasting methods published in 2005–2015 , 2017 .

[13]  Sifeng Liu,et al.  Discrete grey forecasting model and its optimization , 2009 .

[14]  Yong Wang,et al.  A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries. , 2019, The Science of the total environment.

[15]  E. Castro,et al.  Modified and enhanced replacement method for the selection of molecular descriptors in QSAR and QSPR theories , 2008 .

[16]  Jie Cui,et al.  A novel grey forecasting model and its optimization , 2013 .

[17]  Mao Shuhua,et al.  Estimation of Chinese CO2 Emission Based on A Discrete Fractional Accumulation Grey Model , 2015 .

[18]  Abdelkader Benyettou,et al.  Symbolic interpretation of artificial neural networks based on multiobjective genetic algorithms and association rules mining , 2018, Appl. Soft Comput..

[19]  Shuo-Pei Chen,et al.  Forecasting of foreign exchange rates of Taiwan’s major trading partners by novel nonlinear Grey Bernoulli model NGBM(1, 1) , 2008 .

[20]  F. Bass.  A new product growth model for consumer durables , 1976 .

[21]  Xin Ma,et al.  Predicting primary energy consumption using NDGM(1,1,k,c) model with Simpson formula , 2019, Scientia Iranica.

[22]  Haruna Chiroma,et al.  A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm , 2016, Appl. Soft Comput..

[23]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[24]  Xin Ma,et al.  A brief introduction to the Grey Machine Learning , 2018, ArXiv.

[25]  Xin Ma,et al.  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 , 2019, Energy.

[26]  Wenqing Wu,et al.  Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption , 2018, Energy.

[27]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[28]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[29]  Lifeng Wu,et al.  Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions , 2020 .

[30]  Bo Zeng,et al.  A new multivariable grey prediction model with structure compatibility , 2019, Applied Mathematical Modelling.

[31]  Huiming Duan,et al.  A novel car-following inertia gray model and its application in forecasting short-term traffic flow , 2020 .

[32]  Xin Ma,et al.  Carbon-dioxide mitigation in the residential building sector: A household scale-based assessment , 2019, Energy Conversion and Management.

[33]  Sifeng Liu,et al.  Grey system model with the fractional order accumulation , 2013, Commun. Nonlinear Sci. Numer. Simul..

[34]  Do-Hyeun Kim,et al.  A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model , 2018 .

[35]  Wenqing Wu,et al.  Improved GM(1,1) model based on Simpson formula and its applications. , 2019, 1908.03493.

[36]  Keith W. Hipel,et al.  Forecasting China's electricity consumption using a new grey prediction model , 2018 .

[37]  Joseph Y.-T. Leung,et al.  Bi-criteria ant colony optimization algorithm for minimizing makespan and energy consumption on parallel batch machines , 2017, Appl. Soft Comput..

[38]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[39]  Bo Zeng,et al.  A Hybrid Grey Prediction Model for Small Oscillation Sequence Based on Information Decomposition , 2020, Complex..

[40]  Yong Wang,et al.  The novel fractional discrete multivariate grey system model and its applications , 2019, Applied Mathematical Modelling.

[41]  Guifang Shao,et al.  An improved genetic algorithm for structural optimization of Au-Ag bimetallic nanoparticles , 2018, Appl. Soft Comput..

[42]  Zheng-Xin Wang,et al.  The NLS-based Grey Bass Model for Simulating New Product Diffusion , 2017 .

[43]  Shu Wang,et al.  Predicting Beijing's tertiary industry with an improved grey model , 2017, Appl. Soft Comput..

[44]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[45]  Wenqing Wu,et al.  The conformable fractional grey system model. , 2018, ISA transactions.