[HTML][HTML] Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation
Energy consumption data are crucial for various smart energy management applications,
such as demand forecasting, customer segmentation, and energy efficiency analysis …
such as demand forecasting, customer segmentation, and energy efficiency analysis …
[HTML][HTML] Energy data generation with wasserstein deep convolutional generative adversarial networks
Residential energy consumption data and related sociodemographic information are critical
for energy demand management, including providing personalized services, ensuring …
for energy demand management, including providing personalized services, ensuring …
[HTML][HTML] Privacy-preserving federated learning for residential short-term load forecasting
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 …
forecasts for residential loads have become essential. Smart meters can play an important …
DPWGAN: high-quality load profiles synthesis with differential privacy guarantees
Smart meters have collected massive amounts of fine-grained load data from users,
enabling various load profile analyses that can help improve the efficiency of smart grids …
enabling various load profile analyses that can help improve the efficiency of smart grids …
Fedrep: Towards horizontal federated load forecasting for retail energy providers
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 …
Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine …
Multiple households energy consumption forecasting using consistent modeling with privacy preservation
Traditional data-driven energy consumption forecasting models, including machine learning
and deep learning methods, showed outstanding performance in terms of forecasting …
and deep learning methods, showed outstanding performance in terms of forecasting …
Privacy-preserving federated-learning-based net-energy forecasting
MM Badr, MI Ibrahem, M Mahmoud… - SoutheastCon …, 2022 - ieeexplore.ieee.org
Energy forecasting not only enables infrastructure planning and power dispatching but also
reduces power outages and equipment failures. To preserve the customers' privacy …
reduces power outages and equipment failures. To preserve the customers' privacy …
Differentially private deep learning for load forecasting on smart grid
Load forecasting is vital for a reliable and sustainable smart grid as it is used to predict the
demand and make price adjustment accordingly. Electric consumption data which is …
demand and make price adjustment accordingly. Electric consumption data which is …
Federated learning for short-term residential energy demand forecasting
Energy demand forecasting is an essential task performed within the energy industry to help
balance supply with demand and maintain a stable load on the electricity grid. As supply …
balance supply with demand and maintain a stable load on the electricity grid. As supply …
Privacy-preserving federated learning for value-added service model in advanced metering infrastructure
XY Zhang, JR Córdoba-Pachón, P Guo… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Advanced metering infrastructure (AMI) is the backbone of the next generation smart city and
smart grid; it not only provides near real-time two-way communication between the …
smart grid; it not only provides near real-time two-way communication between the …