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A hybrid coal prediction model based on grey Markov optimized by GWO – A case study of Hebei province in China
16 references·1 citing·44 second hop
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References

  • 2012Grey system model with the fractional order accumulation622 cited
  • 2010Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India356 cited
  • 2012A novel grey forecasting model and its optimization205 cited
  • 2020Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and PSO178 cited
  • 2021Nonlinear influence of urbanization on China's urban residential building carbon emissions: New evidence from panel threshold model172 cited
  • 2018An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting163 cited
  • 2020A novel fractional grey Riccati model for carbon emission prediction142 cited
  • 2021Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China142 cited
  • 2005Discrete GM(1,1) and Mechanism of Grey Forecasting Model136 cited
  • 2012Analysis of South Korea’s economic growth, carbon dioxide emission, and energy consumption using the Markov switching model116 cited
  • 2016Carbon dioxide emission prediction using support vector machine100 cited
  • 2016A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors96 cited
  • 2021A novel multivariable grey prediction model and its application in forecasting coal consumption

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89 cited
  • 2018ARIMA forecasting of China’s coal consumption, price and investment by 203083 cited
  • 2020Forecast of coal consumption in Gansu Province based on Grey-Markov chain model78 cited
  • 2021An unequal adjacent grey forecasting air pollution urban model78 cited
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    • 2023An enhanced interval-valued decomposition integration model for stock price prediction based on comprehensive feature extraction and optimized deep learning33 cited