Special Issue "Modelling and Simulation of Smart Energy Management Systems"
Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 15280
Interests: metaheuristic algorithm; deep learning; artificial intelligence in renewable energy; smart electricity grids; energy loads or demand model; energy informatics or economics; green or cleaner energy solutions; energy generation; utilization; conversion; storage; transmission; management; and sustainability; sources such as mechanical; thermal; nuclear; chemical; electromagnetic; magnetic; electricity; solar; bio; hydro; wind; geothermal; tidal and ocean energy; fossil fuels and nuclear resources
Special Issues, Collections and Topics in MDPI journals
Special Issue in Remote Sensing: Smart Data Assimilations, Crop Modelling and Remote Sensing in Agriculture Monitoring
Special Issue in Energies: Deep Learning Artificial Intelligence Methods and Applications in Renewable Energy Management System
Topics: Energy Consumption, Demand and Price Forecasting with Artificial Intelligence
Artificial intelligence approaches are attractive for the modelling and simulation of energy systems. National electricity markets, energy utilities, climate–energy policy makers and electronic, electrical and mechatronic engineers employ optimizations to improve energy systems and possibly, develop ways to integrate renewable energies into the grid to provide both optimal energy security and also the environmentally-friendly and sustainable operation of the national energy market. Artificial intelligence algorithms are implemented in power management to utilize renewable energies such as solar, wind and hydropower.
We welcomes original and high quality submissions in the modelling and simulation of real energy systems that build a responsive management platform. It entails complex consumer markets including real-time prediction and management tools with smart adaptive modelling incorporated in energy management environments.
This Special Issue focuses on the modelling, analysis, design and the implementation of such systems with advanced algorithms, recent theoretical developments, novel applications, target case studies, extensive reviews and discussion on machine learning for energy forecasting, and renewable energies in grids and decision systems designed for a smart energy management platform with advancing big data techniques.
Prof. Dr. Ravinesh C Deo
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 2200 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.
- Energy Informatics
- Energy Modelling and Simulation
- Power Grid Systems
- Renewable Energy Systems
- Artificial Intelligence
- Smart Energy Market
- Energy Security, Climate Change & Sustainability
- Machine Learning Applications