Nowadays, residential households, including both consumers and emerging prosumers,
have exhibited a growing demand for active/reactive power. This demand surge arises from …

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

Electricity consumption is increasing rapidly, and the limited availability of natural resources
necessitates efficient energy usage. Predicting and managing electricity costs is …

Non-intrusive load monitoring systems that are based on deep learning methods produce
high-accuracy end use detection; however, they are mainly designed with the one vs. one …

Renewable energy production and the balance between production and demand have
become increasingly crucial in modern power systems, necessitating accurate forecasting …

The rationale for using enhanced Deep Neural Networks (DNNs) in the power distribution
system for short-term load forecasting (STLF) originates from a thorough analysis of current …

This study presents an in‐depth overview of deep neural networks (DNN) and their hybrid
applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from …

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

The integration of renewable energy sources (RES) into modern power systems has
become increasingly important due to climate change and macroeconomic and geopolitical …

Efficient energy management relies heavily on accurate load forecasting, particularly in the
face of increasing energy demands and the imperative for sustainable operations. However …