Applications of Machine Learning and Optimization in Energy Sectors

Dear Colleagues,

This Special Issue, entitled “Applications of Machine Learning and Optimization in Energy Sectors”, aims to explore the innovative and transformative potential of machine learning (ML) and optimization techniques to address critical challenges within the energy industry. With the increasing global demand for sustainable and efficient energy solutions, there is a pressing need for cutting-edge research that leverages ML and optimization to enhance energy production, consumption, and management.

This Special Issue seeks to provide a platform for researchers, practitioners, and experts from various disciplines to showcase their work, share insights, and contribute to our collective knowledge in the field of energy. The primary goals include:

Advancing Sustainable Energy Production: Explore ML and optimization methods to optimize energy production processes, increase the efficiency of renewable energy sources, and reduce environmental impacts.

Enhancing Energy Distribution and Grid Management: Investigate ML-driven solutions for smart grid management, demand forecasting, grid stability, and energy distribution optimization.

Optimizing Energy Consumption: Address the challenges of energy efficiency in buildings, industries, and transportation through ML-driven strategies for consumption optimization and demand-side management.

Energy Market and Policy Analysis: Examine ML applications for energy market analysis, pricing prediction, and policy formulation to foster competitive and sustainable energy markets.

Integration of Emerging Technologies: Explore how ML and optimization can facilitate the integration of emerging technologies, such as electric vehicles, energy storage systems, and microgrids into the energy ecosystem.

Topics of interest for publication include, but are not limited to, the following:

• ML-based predictive modeling for energy systems;
• Optimization algorithms for energy resource allocation;
• Data-driven approaches for energy efficiency;
• Autonomous and adaptive control systems for energy infrastructure;
• Decision support systems for energy planning;
• Risk assessment and management in the energy sector using ML;
• Cross-disciplinary applications of ML and optimization in energy.

Dr. Ricardo Cajo Diaz
Dr. Angel Recalde Lino
Dr. Washington Velasquez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning (ML)
• optimization technique
• renewable energy
• grid management
• electric vehicles (EVs)
• decision support systems
• microgrids