AI in Clean Energy Engineering

Submission deadline: 1 June 2027
Special Issue Editors

Alexey Mikhaylov   
Department of Financial Technologies, Financial University under the Government of the Russian Federation, Moscow, Russia
Interests: AI; Energy; Economics, Econometrics and Finance; Business, Management and Accounting; Environmental Science; Materials Science; Mathematics; Computer Science; Psychology; Social Sciences; Neuroscience.
Website: Click Here

Special Issue Information

Dear Colleagues:

The profound impact of AI-driven, data-centric methodologies on energy efficiency is fostering a new era of operational excellence and strategic foresight. This shift is predicated on the exploitation of large-scale datasets to inform and refine every facet of energy, from sourcing to final delivery. The application of rigorous AI and machine learning frameworks to these data streams enables a more granular understanding of energy variables, culminating in highly accurate demand projections and optimized resource allocation. Beyond forecasting, these intelligent systems provide the capability for continuous logistics optimization, including dynamic route generation and automated operational workflows. By providing a clear and comprehensive view of complex operational networks, this data-driven approach enables a transition from reactive problem-solving to proactive, resilient, and highly efficient energy management.

Keywords

Data-Centric Methodologies; Artificial Intelligence; Machine Learning; Demand Forecasting; Logistics Optimization; Large-Scale Dataset;