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A hybrid model based on selective ensemble for energy consumption forecasting in China · Study

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ArticleA hybrid model based on selective ensemble for energy consumption forecasting in China

Jin Xiao2018

Article2018

A hybrid model based on selective ensemble for energy consumption forecasting in China

Jin Xiao · Yuxi Li · Ling Xie · Dunhu Liu

126 citations·0/0 files stored·Energy

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Forecasting Techniques and Applications Energy Load and Power Forecasting Grey System Theory Applications

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References

2016Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model 2012Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology 2017Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression 2015A novel hybrid method for crude oil price forecasting 2018Forecasting China's electricity consumption using a new grey prediction model 2016Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization 2015A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting 2017Forecasting China’s natural gas demand based on optimised nonlinear grey models

Cited by

2021Evaluating the impact of carbon emissions trading scheme on Chinese firms’ total factor productivity 2021Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic 2023Forecasting crude oil futures price using machine learning methods: Evidence from China

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Year2018

Typearticle

Published2018-06-27

VenueEnergy

ISSN-L0360-5442

Languageen

Citations126

Files0 public / 0

ShelfForecasting Techniques and Applications

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doi10.1016/j.energy.2018.06.161

openalex_workW2810273060

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