Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression

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Energy and Power Engineering

ISSN Print: 1949-243X
ISSN Online: 1947-3818
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"Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression"
written by Shijie Ye, Guangfu Zhu, Zhi Xiao,
published by Energy and Power Engineering, Vol.4 No.5, 2012

has been cited by the following article(s):

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[1]	Ultra Short-Term Power Load Forecasting Based on Similar Day Clustering and Ensemble Empirical Mode Decomposition Energies, 2023 DOI:10.3390/en16041989

[2]	Load Forecasting Techniques for Power System: Research Challenges and Survey IEEE Access, 2022 DOI:10.1109/ACCESS.2022.3187839

[3]	Smart Electrical and Mechanical Systems 2022 DOI:10.1016/B978-0-323-90789-7.00006-3

[4]	Load Forecasting Techniques for Power System: Research Challenges and Survey IEEE Access, 2022 DOI:10.1109/ACCESS.2022.3187839

[5]	A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models IEEE Access, 2020 DOI:10.1109/ACCESS.2020.3010702

[6]	Forecasting Indonesia's electricity load through 2030 and peak demand reductions from appliance and lighting efficiency Energy for Sustainable Development, 2019 DOI:10.1016/j.esd.2019.01.001

[7]	Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting Energies, 2019 DOI:10.3390/en12183588

[8]	Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation Mathematical Problems in Engineering, 2016 DOI:10.1155/2016/6212951

[9]	Subspace Projection Method Based Clustering Analysis in Load Profiling IEEE Transactions on Power Systems, 2014 DOI:10.1109/TPWRS.2014.2309697

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