Data-Centric Methodologies for Energy Efficiency


Submission deadline: 01 June 2027
Special Issue Editor

Dr. Alexey Mikhaylov   
Department of Financial Technologies, Financial University under the Government of the Russian Federation,
Moscow, Russia

Special Issue Information

Dear Colleagues:

The special issue's focus on AI-driven demand forecasting, resource allocation, logistics optimization, and operational workflow automation directly supports CEST's stated interest in "system optimisation, energy system optimisation and energy conservation" (explicitly listed in CEST's scope) as well as "smart energy" and "development and application" . The emphasis on improving energy efficiency through large-scale datasets and machine learning frameworks contributes meaningfully to SDG 7 (Affordable and Clean Energy) , a priority for CEST.

The special issue explicitly encourage submissions that apply data-centric methods to concrete clean energy technologies listed in CEST's scope (e.g., solar forecasting, biomass supply chain optimization, hydrogen logistics). 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