Analysis of dynamic appliance flexibility considering user behavior via non-intrusive load monitoring and deep user modeling

Shaopeng Zhai; Huan Zhou; Zhihua Wang; Guangyu He

doi:10.17775/CSEEJPES.2019.02100

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Analysis of dynamic appliance flexibility considering user behavior via non-intrusive load monitoring and deep user modeling

Special Section Papers | 更新时间：2025-12-19

- Analysis of dynamic appliance flexibility considering user behavior via non-intrusive load monitoring and deep user modeling
- Analysis of dynamic appliance flexibility considering user behavior via non-intrusive load monitoring and deep user modeling
- 中国电机工程学会电力与能源系统学报(英文) 2020年6卷第1期页码：41-51
- 作者机构：
  
  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,Shanghai,China
  2. Electric Power Dispatching and Communication Center,Shanghai,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2019.02100
  中图分类号：
- 纸质出版：2020
- 稿件说明：
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Shaopeng Zhai, Huan Zhou, Zhihua Wang, 等. Analysis of dynamic appliance flexibility considering user behavior via non-intrusive load monitoring and deep user modeling[J]. 中国电机工程学会电力与能源系统学报(英文), 2020,6(1):41-51.

Shaopeng Zhai, Huan Zhou, Zhihua Wang, et al. Analysis of dynamic appliance flexibility considering user behavior via non-intrusive load monitoring and deep user modeling[J]. CSEE Journal of Power and Energy Systems, 2020, 6(1): 41-51.
Shaopeng Zhai, Huan Zhou, Zhihua Wang, 等. Analysis of dynamic appliance flexibility considering user behavior via non-intrusive load monitoring and deep user modeling[J]. 中国电机工程学会电力与能源系统学报(英文), 2020,6(1):41-51. DOI： 10.17775/CSEEJPES.2019.02100.

Shaopeng Zhai, Huan Zhou, Zhihua Wang, et al. Analysis of dynamic appliance flexibility considering user behavior via non-intrusive load monitoring and deep user modeling[J]. CSEE Journal of Power and Energy Systems, 2020, 6(1): 41-51. DOI： 10.17775/CSEEJPES.2019.02100.

摘要

Abstract

The research on non-intrusive load monitoring (NILM) and the growing deployment of home energy management system (HEMS) have made it possible for households to have a detailed understanding of their power usage and to make appliances participate in demand response (DR) programs. Appliance flexibility analysis helps the HEMS dispatching appliances to participate in DR programs without violating user's comfort level. In this paper

a dynamic appliance flexibility analysis approach using the smart meter data is presented. In the training phase

the smart meter data is preprocessed by NILM to obtain user's appliances usage behaviors

which is used to train the user model. During operation

the NILM is used to infer recent appliances usage behaviors

and then the user model predicts user's appliances usage behaviors in the DR period considering long-term behaviors dependences

correlations between appliances and temporal information. The flexibility of each appliance is calculated based on the appliance characteristics as well as the predicted user's appliances usage behaviors caused by the control of the appliance. The HEMS can choose the appliance with high flexibility to participate in the DR programs. The case study demonstrates the performance of the user model and illustrates how the appliance flexibility analysis is performed using a real-world case.

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