Privacy-preserving HE-based clustering for load profiling over encrypted smart meter data

Yang, Haomiao; Liang, Shaopeng; Zhou, Qing; Li, Hongwei

doi:10.1109/icc40277.2020.9148669

Cited by 2 publications

(1 citation statement)

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“…Several recent approaches have also focused on privacy issues related to the design of DRM models targeted at consumers or prosumers [19]. A modified vector homomorphic encryption is analyzed in [20] in order to perform a secure load profiling of consumers based on encrypted meter data. Aiming at protecting consumers' privacy, a DRM model is designed in [21] to learn an intelligent multi-microgrid system's energy price response by implementing a deep neural network without direct access to consumers' private energy consumption information.…”

Section: Related Workmentioning

confidence: 99%

Coalitional Demand Response Management in Community Energy Management Systems

Kemp,

Siraj,

Tsiropoulou

2023

Energies

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With the advent of the Distributed Energy Resources within smart grid systems, traditional demand response management (DRM) models need to be redesigned to capture prosumers’ energy consumption requests and dynamic behavior within the energy market. In this paper, a coalitional DRM model is introduced based on the principles of Game Theory and reinforcement learning to dynamically determine prosumers’ formation in local energy trading communities and their optimal energy consumption. A hedonic game-theoretic model is introduced to enable prosumers to autonomously and dynamically select an energy trading community considering the partially available information regarding prosumers’ energy generation and consumption characteristics and utility companies’ provided rewards per community. Then, a log-linear reinforcement learning model is proposed to enable each prosumer to distributedly determine their optimal energy consumption. A detailed evaluation of the proposed coalitional DRM model is performed based on real data collected from the southwest area of the USA.

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Section: Related Workmentioning

confidence: 99%