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A Non-intrusive Appliances Load Monitoring Method Based on Hourly Smart Meter Data
Song CH(宋纯贺)1,2; Wang ZF(王忠锋)1,2; Liu, Shuji3; Xu, Libo3; Zhou, Dapeng3; Zeng P(曾鹏)1,2
Department工业控制网络与系统研究室
Conference Name9th International Conference on Computer Engineering and Networks, CENet2019
Conference DateOctober 18-20, 2019
Conference PlaceChangsha, China
Source PublicationProceedings of the 9th International Conference on Computer Engineering and Networks, CENet2019
PublisherSpringer
Publication PlaceBerlin
2019
Pages785-799
Indexed ByEI
EI Accession number20203008974140
Contribution Rank1
ISSN2194-5357
ISBN978-981-15-3752-3
KeywordSmart grid Non-intrusive appliances load monitoring Peak load
AbstractPeak load management is very important for the electric power system. This paper analyzes the impact of residential swimming pool pumps (RSPPs) on the peak load. First, this paper analyzes the challenges of non-intrusive energy consumption estimation for SPPs. Second, a novel reference-based change-point (RCP) model is proposed for non-intrusive SPPs energy consumption estimation. The advantages of the proposed RCP model are that it does not require high sampling rate data or prior information of the appliance. We show that during pool season, under the assumption that the ratio of base loads (defined as the power consumption which is independent of the outdoor temperature) of houses with and with PPs remains the same during no-pool season and pool season, 6.3% of the total energy is consumed by PPs, while under the assumption that for houses with and without PPs, the ratio of base loads is equal to the ratio of the temperature-dependent power consumption during pool season, 9.08% of the total energy is consumed by PPs. Furthermore, we show that by shifting PPs activity period, under the first assumption, at least 1.27% of peak demand can be reduced, while under the second assumption, at least 4.53% of peak demand can be reduced.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/27374
Collection工业控制网络与系统研究室
Corresponding AuthorSong CH(宋纯贺)
Affiliation1.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China
Recommended Citation
GB/T 7714
Song CH,Wang ZF,Liu, Shuji,et al. A Non-intrusive Appliances Load Monitoring Method Based on Hourly Smart Meter Data[C]. Berlin:Springer,2019:785-799.
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