Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

Smart homes, powered mostly by Internet of Things (IoT) devices, have become very
popular nowadays due to their ability to provide a holistic approach towards effective energy …

Electric load forecasting is essential for power management and stability in smart grids. This
is mainly achieved via advanced metering infrastructure, where smart meters (SMs) are …

Federated learning (FL) facilitates collaborative model training in the autonomous Internet of
Things (IoT) system while preserving the privacy of local data on IoT clients. Nonetheless …

Federated learning (FL) enables multiple Internet of Things (IoT) devices to collaboratively
train a global model without centralizing raw data. However, achieving optimal performance …

Federated learning (FL) has become increasingly popular and distributes machine learning
models among a large set of resource-constraint edge devices without transferring data to …

Federated Learning (FL) in load forecasting improves predictive accuracy by leveraging
data from distributed load networks while preserving data privacy. However, the …

With the rising awareness of health and wellness, accurate ultraviolet (UV) radiation
forecasts have become crucial for planning and conducting outdoor activities safely …

Household consumption accounts for about one-third of global electricity. Accurate results of
household load prediction would help in energy management at both the building and the …

With the continuous development of 6G technology and the Internet of Things, small terminal
devices are gradually joining deep model training through wireless networks, leading to the …