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A Customer Baseline Measurement Method for Residential User of Demand Response
ID：292 View Protection：ATTENDEE Updated Time：2021-12-03 10:57:52 Hits：1259 Poster Presentation

Start Time：2021-12-17 14:30（Asia/Shanghai）

Duration：5min

Session：Z Poster Session » Z6Poster Session 6: AI-driven technology

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Abstract
 One of the most important challenges for DR revenue is the calculation of customer baseline load. In this paper a novel CBL (Customer Baseline Load )  calculation method and it’s correction based on error evaluation is proposed theoretically and empirically. A dataset consisting of 2135 residential customers from China is utilized for the case study to test the performance of the algorithm in actual conditions using accuracy and bias metrics. The case study results show that load data is non-stationary, and the baseline method of grey theory can well adapt to this feature. The model proposed in this paper can effectively improve the accuracy of demand response load baseline measurement. The average error is decreased from 6.09% to 3.56%. At the same time, it can also provide data support for adjustable load to participate in market-oriented transactions.
 
Keywords
Speaker
Feixiang gong
China Electric Power Research Institute

Submission Author
Feixiang gong China Electric Power Research Institute
Yuting Xu China Electric Power Research Institute
Songsong Chen China Electric Power Research Institute
Dezhi Li China Electric Power Research Institute
Shiming Tian China Electric Power Research Institute
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Important Date

  • Conference Date

    Jul 11

    2023

    to

    Aug 18

    2023

  • Nov 10 2021

    Draft paper submission deadline

  • Dec 10 2021

    Registration deadline

  • Dec 11 2021

    Contribution Submission Deadline

Sponsored By
IEEE IAS
Organized By
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST
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