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Short-term Forecasting of Individual Residential Load Based on Deep Learning and K-means Clustering

Fujia Han Tianjiao Pu Maozhen Li Gareth Taylor

中国电机工程学会电力与能源系统学报（英文版）2021，Vol.7Issue(2)：261-269,9.

中国电机工程学会电力与能源系统学报（英文版）2021，Vol.7Issue(2)：261-269,9.DOI:10.17775/CSEEJPES.2020.04060

Short-term Forecasting of Individual Residential Load Based on Deep Learning and K-means Clustering

Fujia Han ¹Tianjiao Pu ¹Maozhen Li ²Gareth Taylor³

作者信息

1. Artificial Intelligence Application Department,China Electric Power Research Institute,Beijing 100192,China
2. Department of Electronic and Computer Engineering,Brunel University London,Uxbridge,UK,UB8 3PH
3. Brunel Institute of Power Systems,Brunel University London,Uxbridge,UK,UB8 3PH
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摘要

关键词

Deep learning/demand side response(DSR)/interactions/k-means clustering/residential load forecasting/similarity

Key words

Deep learning/demand side response(DSR)/interactions/k-means clustering/residential load forecasting/similarity

引用本文复制引用

Fujia Han,Tianjiao Pu,Maozhen Li,Gareth Taylor..Short-term Forecasting of Individual Residential Load Based on Deep Learning and K-means Clustering[J].中国电机工程学会电力与能源系统学报（英文版）,2021,7(2):261-269,9.

基金项目

This work was supported by the Science and Technology Program of State Grid Corporation of China(Data Mining Technology of Potential High-Value Industrial Users for Data Operations,No.5700-202055267A-0-0-00). （Data Mining Technology of Potential High-Value Industrial Users for Data Operations,No.5700-202055267A-0-0-00）

中国电机工程学会电力与能源系统学报（英文版）

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ISSN：2096-0042

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