Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities

Data models; Deep learning; Differential privacy; Federated learning; Forecasting; Heterogeneous data; Predictive models; Privacy; Probabilistic logic; Training; Voltage; Voltage forecasting; Differential privacies; Local energy; Performance; Privacy preserving; Computer Science (all); General Computer Science

Abstract :

[en] This paper presents a new privacy-preserving framework for the short-term (multi-horizon) probabilistic forecasting of nodal voltages in local energy communities. This task is indeed becoming increasingly important for cost-effectively managing network constraints in the context of the massive integration of distributed energy resources. However, traditional forecasting tasks are carried out centrally, by gathering raw data of end-users in a single database that exposes their private information. To avoid such privacy issues, this work relies on a distributed learning scheme, known as federated learning wherein individuals’ data are kept decentralized. The learning procedure is then augmented with differential privacy, which offers formal guarantees that the trained model cannot be reversed-engineered to infer sensitive local information. Moreover, the problem is framed using cross-series learning, which allows to smoothly integrate any new client joining the community (i.e., cold-start forecasting) without being plagued by data scarcity. Outcomes show that the proposed approach achieves improved performance compared to non-collaborative (locally trained) models, and is able to reach a trade-off between privacy and performance for different architectures of deep learning networks.

Disciplines :

Energy

Author, co-author :

Toubeau, Jean-François ; Université de Mons - UMONS > Faculté Polytechnique > Service de Génie Electrique

Teng, Fei

Morstyn, Thomas

Krannichfeldt, Leandro Von

Wang, Yi

Language :

English

Title :

Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities

Publication date :

January 2023

Journal title :

IEEE Transactions on Smart Grid

ISSN :

1949-3053

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Volume :

Issue :

Pages :

798-809

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx7/5165411/9997165/09811096.pdf?arnumber=9811096

Research unit :

Electrical Power Engineering

Research institute :

R200 - Institut de Recherche en Energie

Funders :

FPS Economy, S.M.E.s, Self-Employed and Energy through the Energy Transition Funds Project “Adabel”

Available on ORBi UMONS :

since 04 January 2023

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