On-location / Digital Conference

International Conference on Data Mining for Energy Engineering and Smart Grids (ICDMEESG-27)

19th - 20th Jan 2027,Quito, Ecuador

In Association With:

Call for Paper


Important Dates


Early Bird Registration

20th Dec 2026

Paper Submission Deadline

25th December 2026

Registration Deadline

4th January 2027

Conference Date

19th - 20th Jan 2027

Conference Updates:

"Stay updated with Science Cite Conference news."

  • Early-Bird Registration Reminder:
    Early-bird registration for the Science Cite Conference in Quito ends soon! Register Now!
  • Certificate of Presentation – Recognizing Your Contribution:
    Receive a Certificate of Presentation to recognize your participation in Quito conference.
  • Peer Review Process:
    The peer review process will begin soon for Quito conference.
  • Networking with Global Experts:
    Join global experts at our conference in Quito.
  • Opportunity for Scopus-Indexed Journal Publication:
    Your research could be published in a Scopus-Indexed Journal. Submit Your Abstract
  • SDG-Inspired Conference Focus:
    Present your work aligned with Sustainable Development Goals.

Call For Papers

The ICDMEESG bridges the gap between academia and industry by promoting research with practical applications. It provides a platform for professionals and researchers to share insights that drive real-world impact.

The conference focuses on Data Mining for Energy Engineering and Smart Grids, encouraging applied research, case studies, and industry-driven innovations.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Data mining for energy engineering applications
  • Machine learning in smart grid technologies
  • Predictive analytics for energy consumption
  • Data-driven approaches to renewable energy
  • Big data challenges in energy systems
  • Data visualization for energy management
  • Real-time monitoring in energy engineering
  • Data mining for energy efficiency solutions
  • Integration of AI in energy systems
  • Collaborative energy management through data sharing
  • Case studies in energy engineering analytics
  • Data-driven methodologies in energy research
  • Ethics in energy data usage
  • Future trends in energy engineering analytics
  • Data mining for energy storage optimization
  • Machine learning for demand forecasting
  • Data-driven innovations in energy technologies
  • Data mining for grid reliability assessment
  • Applications of data mining in energy systems
  • Data-driven decision making in energy engineering

Assessment

Submissions will be evaluated based on applicability, innovation, and research contribution. Accepted papers will be presented and considered for publication in relevant journals and proceedings.

Registration

Complete your registration to participate in discussions that bridge academia and industry, and gain exposure to practical insights.

Publication

Selected papers will be considered for publication platforms that support academic and industry collaboration.

Indexed / Supported By

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Academic Institutions Whose Scholars Have Contributed

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