[HTML][HTML] Federated learning and non-federated learning based power forecasting of photovoltaic/wind power energy systems: A systematic review
Renewable energy sources, particularly photovoltaic and wind power, are essential in
meeting global energy demands while minimising environmental impact. Accurate …
meeting global energy demands while minimising environmental impact. Accurate …
A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems
Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will
eventually lead to a range of HVAC system failures, resulting in excessive energy …
eventually lead to a range of HVAC system failures, resulting in excessive energy …
[HTML][HTML] Federated transfer learning with orchard-optimized Conv-SGRU: A novel approach to secure and accurate photovoltaic power forecasting
Accurate photovoltaic (PV) power forecasting is pivotal for optimizing the integration of RES
into the grid and guaranteeing proficient energy management. Concurrently, the sensitive …
into the grid and guaranteeing proficient energy management. Concurrently, the sensitive …
[HTML][HTML] Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?
The integration of AI and ML into energy forecasting is crucial for modern energy
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
Ultra-short-term wind power forecasting based on personalized robust federated learning with spatial collaboration
Y Zhao, S Pan, Y Zhao, H Liao, L Ye, Y Zheng - Energy, 2024 - Elsevier
An ultra-short-term wind power forecasting method based on personalized robust federated
learning (PRFL) is proposed to exploit spatio-temporal correlation in a privacy-preserving …
learning (PRFL) is proposed to exploit spatio-temporal correlation in a privacy-preserving …
[HTML][HTML] A review of federated learning in renewable energy applications: Potential, challenges, and future directions
Federated learning has recently emerged as a privacy-preserving distributed machine
learning approach. Federated learning enables collaborative training of multiple clients and …
learning approach. Federated learning enables collaborative training of multiple clients and …
FedWindT: Federated learning assisted transformer architecture for collaborative and secure wind power forecasting in diverse conditions
Q Arooj - Energy, 2024 - Elsevier
Accurate wind power forecasting is crucial for efficient grid management and maximizing the
utilization of wind energy. This study introduces the FedWindT, an innovative model that …
utilization of wind energy. This study introduces the FedWindT, an innovative model that …
Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture
With the emergence of intelligent services and applications powered by artificial intelligence
(AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional …
(AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional …
Privacy-Preserving and Adaptive Federated Deep Learning for Multiparty Wind Power Forecasting
Y Wang, Q Guo - IEEE Transactions on Industry Applications, 2024 - ieeexplore.ieee.org
Advanced forecasting tools are essential for modern power systems to mitigate the
uncertainty of renewable energy sources. Although data-driven approaches have achieved …
uncertainty of renewable energy sources. Although data-driven approaches have achieved …
FedGrid: A Secure Framework with Federated Learning for Energy Optimization in the Smart Grid
In the contemporary energy landscape, power generation comprises a blend of renewable
and non-renewable resources, with the major supply of electrical energy fulfilled by non …
and non-renewable resources, with the major supply of electrical energy fulfilled by non …