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

2020-12-14 · Christopher Briggs, Zhong Fan, Peter Andras

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.

📄 PDF Abstract BibTeX arXiv:2012.07449

Code (0)

등록된 구현이 없습니다.

Tasks

BIG-bench Machine LearningDemand ForecastingFederated LearningPrivacy Preserving

FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers

2022-03-01 · Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh, Shama Naz Islam 외

As Smart Meters are collecting and transmitting household energy consumption data to Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine-grained consumer data while ensuring data priv…

Federated Learning Load Forecasting Management Privacy Preserving+1

A Secure Federated Learning Framework for Residential Short Term Load Forecasting

2022-09-29 · Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh, Shama Naz Islam 외

Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as…

Demand Forecasting Federated Learning Load Forecasting Privacy Preserving+1

Short-term Load Forecasting with Distributed Long Short-Term Memory

2022-08-01 · Yi Dong, Yang Chen, Xingyu Zhao, Xiaowei Huang

With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile information and implement demand response. Wh…

Load Forecasting

Privacy-preserving Federated Learning for Residential Short Term Load Forecasting

2021-11-17 · Joaquin Delgado Fernandez, Sergio Potenciano Menci, Charles Lee, Gilbert Fridgen

With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they p…

Federated Learning Load Forecasting Privacy Preserving

PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data

2025-03-15 · Yunbo Long, Liming Xu, Ge Zheng, Alexandra Brintrup

Federated learning (FL) enables retailers to share model parameters for demand forecasting while maintaining privacy. However, heterogeneous data across diverse regions, driven by factors such as varying consumer behavio…

Demand Forecasting Feature Importance Federated Learning Privacy Preserving+1