Machine learning (ML) sees an increasing prevalence of being used in the Internet of Things
(IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be …

Applying large language models (LLMs) to modern power systems presents a promising
avenue for enhancing decision-making and operational efficiency. However, this action may …

Recent sensor, communication, and computing technological advancements facilitate smart
grid use. The heavy reliance on developed data and communication technology increases …

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 …

Introduction The increasing penetration of distributed generation (eg, solar power and wind
power) in the energy market has caused unpredictable disturbances in power systems and …

The increasing integration of renewable energy sources, such as solar photovoltaic (PV),
into the power grid has heightened the significance of accurate solar radiation forecasting …

With the high penetration of renewable energies connected to smart grids, accurate wind
power forecasting becomes more and more crucial to cope with its intermittency to achieve …

Modern society's reliance on power systems is at risk from the escalating effects of wind-
related climate change. Yet, failure to identify the intricate relationship between wind-related …