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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] Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability
Smart grid is a transformative advancement that modernized the traditional power system for
effective electricity management, and involves optimized energy distribution by load …
effective electricity management, and involves optimized energy distribution by load …
Personalized federated learning for buildings energy consumption forecasting
Buildings' energy consumption forecasting is critical for energy saving and building
maintenance. However, most studies only focus on centralized learning of one dataset …
maintenance. However, most studies only focus on centralized learning of one dataset …
Enhancing IoT healthcare with federated learning and variational autoencoder
The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing
data from local hospitals. However, privacy concerns can impede data sharing among third …
data from local hospitals. However, privacy concerns can impede data sharing among third …
Double Robust Federated Digital Twin Modeling in Smart Grid
Y Zhou, Y Ge, L Jia - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Harnessing the advantage of digital twin (DT) technology, smart grid provides tempting
prospects for efficient management of energy manufacturing, conservation, demand …
prospects for efficient management of energy manufacturing, conservation, demand …
Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network
S Peng, L Fan, L Zhang, H Su, Y He, Q He, X Wang… - Energy, 2024 - Elsevier
Energy consumption forecasting is essential for energy system integration and
management. However, existing studies mainly focus on temporal features of energy …
management. However, existing studies mainly focus on temporal features of energy …
FOCCA: Fog–cloud continuum architecture for data imputation and load balancing in Smart Grids
A Smart Grid operates as an advanced electricity network that leverages digital
communications technology to detect and respond to local changes in usage, generation …
communications technology to detect and respond to local changes in usage, generation …
Li-MSA: Power Consumption Prediction of Servers Based on Few-Shot Learning
S Long, Y Li, Z Li, G **e, W Lin… - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Power consumption prediction is one of the keys to optimize the energy consumption of
servers. Existing traditional regression-based methods are too simple and poorly …
servers. Existing traditional regression-based methods are too simple and poorly …
Forecasting of Giresun Hazelnut Quantity in Giresun Province Using Pi-Sigma Artificial Neural Networks
Ö Karahasan - Turkish Journal of Forecasting, 2024 - dergipark.org.tr
Artificial neural networks are frequently used to solve many problems and give successful
results. Artificial neural networks, which we frequently encounter in solving forecasting …
results. Artificial neural networks, which we frequently encounter in solving forecasting …
Privacy-Preserving Energy Forecasting in Smart Homes using Federated Learning and SecureBoost
S Sadanand, P Vinothiyalakshmi - … on I-SMAC (IoT in Social …, 2024 - ieeexplore.ieee.org
The quick rise in smart home technologies calls for the development of advanced energy
forecasting models that can accurately predict consumption patterns while maintaining user …
forecasting models that can accurately predict consumption patterns while maintaining user …