Personalized federated learning for cross-building energy knowledge sharing: Cost-effective strategies and model architectures
Sufficient building operational data serve as the key premise to enable the development of
reliable data-driven technologies for building energy management. Considering that …
reliable data-driven technologies for building energy management. Considering that …
[HTML][HTML] Optimizing energy efficiency and comfort in smart homes through predictive optimization: A case study with indoor environmental parameter consideration
Recently, a noticeable increase in the shortage of energy resources has been observed,
coupled with a rapidly escalating demand for energy. In response to this challenge, this …
coupled with a rapidly escalating demand for energy. In response to this challenge, this …
Power Quality Forecasting of Microgrids Using Adaptive Privacy-Preserving Machine Learning
Microgrids face challenges in monitoring and controlling the power quality (PQ) of integrated
electrical systems to make timely decisions. Inverter-based technologies handle small-scale …
electrical systems to make timely decisions. Inverter-based technologies handle small-scale …
Fairness in Continual Federated Learning
N Noor - 2024 - search.proquest.com
Abstract Continual Federated Learning (CFL) is a distributed machine learning technique
that enables multiple clients to collaboratively train a shared model without sharing their …
that enables multiple clients to collaboratively train a shared model without sharing their …