Privacy-Preserving Baseline Load Reconstruction for Residential Demand Response Considering Distributed Energy Resources

Customer baseline load (CBL) reconstruction is a critical problem in residential demand response. The difficulty of residential CBL lies in the variability of both irregular consumption and on-site distributed energy resources. Targeting the CBL reconstruction of residential prosumers, a regression-...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 18; no. 5; pp. 3541 - 3550
Main Authors: Chen, Yang, Chen, Chunyu, Zhang, Xiao, Cui, Mingjian, Li, Fangxing, Wang, Xinan, Yin, Shengfei
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Customer baseline load (CBL)
Customers
Data models
demand response (DR)
distributed energy resource (DER)
Distributed power generation
Energy management
Energy resources
Energy sources
Error reduction
Estimation
federated learning (FL)
Load management
Load modeling
Privacy
Reconstruction
stacked autoencoders (SAEs)
Training
ISSN:1551-3203, 1941-0050
Online Access:Get full text
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Summary:Customer baseline load (CBL) reconstruction is a critical problem in residential demand response. The difficulty of residential CBL lies in the variability of both irregular consumption and on-site distributed energy resources. Targeting the CBL reconstruction of residential prosumers, a regression-based estimation scheme is proposed using stacked autoencoders (SAEs) under the federated learning (FL) framework. In the FL framework, each residential unit (RU) stores and trains data locally without sharing them with neighboring RUs or the independent third party (ITP) responsible for CBL reconstruction. Local updates containing no load information are exchanged with the ITP (server) for the training improvement. The FL framework can, thus, protect the privacy of customers. Experimental results show that the proposed FL-based cascaded SAE outperforms the baseline on all tests and achieves up to 62.5% improvement in reducing reconstruction error. Moreover, it has enhanced privacy-preserving knowledge-sharing ability, higher efficiency, and better stability.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3107400