A Decomposition and Reconstruction-based Approach to Power User Demand Analysis

DOI

10.2991/978-94-6463-502-7_106How to use a DOI?

Keywords

demand analysis; decomposition; reconstruction; emergency respons

Abstract

To address issues such as the continuously expanding electricity demand and worsening environmental conditions, extensive research on demand-side management methods has emerged, aiming to optimize the allocation of electricity resources by leveraging demand-side factors. However, in the context of the internet environment, demand information from electricity users exhibits characteristics of complexity, dynamism, and big data, thereby placing higher demands on the capability of power companies in demand processing and analysis. Consequently, this paper proposes a demand analysis method based on decomposition and reconstruction. By establishing rules for demand decomposition, demand ontology models, and mapping relationships between demand and actual engineering characteristics, it establishes the importance ranking of electricity user demands and response engineering characteristics. This assists decision-makers in formulating electricity scheduling schemes to ensure timely and orderly demand response and electricity supply.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ying Zhang
AU  - Kejia Pan
AU  - Dongguo Liu
AU  - Bingyan Deng
AU  - Chengcheng Yao
PY  - 2024
DA  - 2024/08/31
TI  - A Decomposition and Reconstruction-based Approach to Power User Demand Analysis
BT  - Proceedings of the 2024 5th International Conference on Education, Knowledge and Information Management (ICEKIM 2024）
PB  - Atlantis Press
SP  - 987
EP  - 1004
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-502-7_106
DO  - 10.2991/978-94-6463-502-7_106
ID  - Zhang2024
ER  -