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J-GLOBAL ID：202002276227811429   Reference number：20A0440225

Decomposition-Accumulation Principle-Based Monthly Electricity Consumption Forecasting Approach Using EMD-XGBoost Hybrid Model

EMD-XGBoostハイブリッドモデルを用いた分解蓄積原理に基づく月間電力消費予測手法【JST・京大機械翻訳】

Author (5)： Li Zhenghui (North China Electric Power University,Department of Electrical Engineering,Baoding,China) ,  Li Kangping (North China Electric Power University,Department of Electrical Engineering,Baoding,China) ,  Xuan Zhiming (North China Electric Power University,Department of Electrical Engineering,Baoding,China) ,  Zhen Zhao (North China Electric Power University,Department of Electrical Engineering,Baoding,China) ,  Wang Fei (North China Electric Power University,Department of Electrical Engineering,Baoding,China)
Material： IEEE Conference Proceedings  (IEEE Conf Proc)
Volume： 2019  Issue： iSPEC  Page： 1096-1101  Publication year： 2019 
JST Material Number： W2441A  Document type： Proceedings
Article type： 原著論文  Country of issue： United States (USA)  Language： ENGLISH (EN)

Abstract/Point： Accurate monthly electricity c...

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Thesaurus term： *electric power consumption ,  high-resolution ,  *prediction technique ,  effectiveness ,  electric power market ,  case study ,  *electric power ,  retail trade
Semi thesaurus term： 影響因子 ,  予測精度 ,  採算性 ,  *ハイブリッドモデル , 【Automatic Indexing@JST】

JST classification (1)： Graphic and image processing in general  (JE04010I)

Terms in the title (6)： ハイブリッドモデル ,  分解 ,  蓄積 ,  原理 ,  電力消費 ,  予測手法