A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction
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- repec:gam:jsusta:v:10:y:2018:i:1:p:118-:d:125663 is not listed on IDEAS
- repec:gam:jsusta:v:9:y:2017:i:12:p:2299-:d:122514 is not listed on IDEAS
- repec:eee:energy:v:140:y:2017:i:p1:p:601-611 is not listed on IDEAS
- repec:eee:rensus:v:75:y:2017:i:c:p:123-136 is not listed on IDEAS
- Shao, Zhen & Yang, ShanLin & Gao, Fei & Zhou, KaiLe & Lin, Peng, 2017. "A new electricity price prediction strategy using mutual information-based SVM-RFE classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 330-341.
- repec:eee:energy:v:165:y:2018:i:pb:p:1220-1227 is not listed on IDEAS
More about this item
KeywordsMid-term electricity demand; Forecasting; Semi-parametric regression; Ensemble Empirical Mode Decomposition; Probability density forecasts;
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