Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters

• URL: http://arxiv.org/abs/2012.07449v1
• Date: Mon, 14 Dec 2020 12:04:34 GMT
• Title: Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters
• Authors: Christopher Briggs, Zhong Fan, Peter Andras
• Abstract summary: We highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer's household such as occupancy, habits and individual appliance usage. We propose the application of a distributed machine learning setting known as federated learning for energy demand forecasting at various scales to make load prediction possible.
• Score: 4.769747792846004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer's household such as occupancy, habits and individual appliance usage. Yet smart metering infrastructure has the potential to vastly reduce carbon emissions from the energy sector through improved operating efficiencies. We propose the application of a distributed machine learning setting known as federated learning for energy demand forecasting at various scales to make load prediction possible whilst retaining the privacy of consumers' raw energy consumption data.

Related papers

• Just In Time Transformers [2.7350304370706797]
  JITtrans is a novel transformer deep learning model that significantly improves energy consumption forecasting accuracy. Our findings highlight the potential of advanced predictive technologies to revolutionize energy management and advance sustainable power systems.
  arXiv  Detail & Related papers  (2024-10-22T10:33:00Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Optimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept [50.591267188664666]
  The production of paper from waste paper is still a highly resource intensive task, especially in terms of energy consumption. We have identified a lack of utilization of it and implement a concept using an operator assistance system and state-of-the-art machine learning techniques. Our main objective is to provide situation-specific knowledge to machine operators utilizing available data.
  arXiv  Detail & Related papers  (2022-06-23T09:54:35Z)
• FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers [1.1254693939127909]
  We propose a novel horizontal privacy-preserving federated learning framework for energy load forecasting, namely FedREP. We consider a federated learning system consisting of a control centre and multiple retailers by enabling multiple REPs to build a common, robust machine learning model without sharing data. For forecasting, we use a state-of-the-art Long Short-Term Memory (LSTM) neural network due to its ability to learn long term sequences of observations.
  arXiv  Detail & Related papers  (2022-03-01T04:16:19Z)
• Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
  Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
  arXiv  Detail & Related papers  (2021-02-16T16:56:27Z)
• Privacy Protection of Grid Users Data with Blockchain and Adversarial Machine Learning [0.8029049649310213]
  Utilities around the world are reported to invest a total of around 30 billion over the next few years for installation of more than 300 million smart meters. With full country wide deployment, there will be almost 1.3 billion smart meters in place. All these perks associated with fine grained energy usage data collection threaten the privacy of users. This research paper addresses privacy violation of consumers' energy usage data collected from smart meters.
  arXiv  Detail & Related papers  (2021-01-15T21:54:55Z)
• Reshaping consumption habits by exploiting energy-related micro-moment recommendations: A case study [2.741120981602367]
  This work builds on the detection of repeated usage patterns from consumption logs. It presents the structure and operation of an energy consumption reduction system, which employs a set of sensors, smart-meters and actuators. The system recommends to the user the proper energy saving action at the right moment and gradually shapes user's habits.
  arXiv  Detail & Related papers  (2020-10-09T17:29:56Z)
• A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids [58.666456917115056]
  This paper presents a Reinforcement Learning based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers.
  arXiv  Detail & Related papers  (2020-09-23T02:17:51Z)
• Energy Disaggregation with Semi-supervised Sparse Coding [0.0]
  Energy disaggregation research aims to decompose the aggregated energy consumption data into its component appliances. In this paper, a discriminative disaggregation model based on sparse coding has been evaluated on large-scale household power usage dataset for energy conservation.
  arXiv  Detail & Related papers  (2020-04-20T21:05:25Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.