The convergence of hydrogen energy with artificial intelligence (AI) in smart infrastructure
has significant potential to revolutionise the worldwide energy sector. This article thoroughly …

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

Urban areas are increasingly pivotal in the global transition towards sustainable energy,
driven by rapid urbanization and environmental imperatives. This paper explores the …

Technological advancements, urbanization, high energy demand, and global requirements
to mitigate carbon footprints have led to the adoption of innovative green technologies for …

This research introduces a groundbreaking strategy for urban microgrid (MG) management
and social economics, focusing on enhancing energy efficiency, reliability, and steering …

This review offers an in-depth examination of Deep Learning (DL) and Machine Learning
(ML) techniques for smart grid load forecasting, emphasizing language precision …

The smart grid is an innovation that employs two-way communications to give innovative
services to end consumers. Due to the severe contradictions in this connection, this system …

Intelligent predictive models are fundamental in peer-to-peer (P2P) energy trading as they
properly estimate supply and demand variations and optimize energy distribution, and the …

In the smart grid, it is critical to collect dynamic and time-dependent information on energy
demand and consumption and compare it to current supply conditions. The decentral smart …

Background Smart grids, characterized by their ability to integrate renewable energy
sources and manage the dynamic balance between supply and demand, require …