Application of Machine Learning Tools for Energy System

Dear Colleagues,

It is challenging to imagine today a modern world without artificial intelligence. Currently, artificial intelligence surrounds us at every step. Its application is increasing not only in traditional application areas, but also in newer areas, including energy management systems, renewable energy conversion systems, electric aircrafts, aviation, electric vehicles, unmanned propulsion systems, robotics, etc.

An energy system can be a combination of mechanical, chemical, and electrical features, and it can cover various dimensions of energy types that include renewables and other alternative energy systems as well.

As the demand for energy continues to increase, smart energy systems are becoming more prevalent in addressing the challenges associated with energy generation, distribution, and consumption. Artificial intelligence and machine learning have been identified as promising approaches to address these challenges as they improve the efficiency, reliability, and sustainability of smart energy systems.

The main goal of this Special Issue is to bring together the latest research and developments in the areas of artificial intelligence and machine learning for smart energy systems.

Original research articles, review papers, and case studies that demonstrate innovative applications of artificial intelligence and machine learning in energy systems are welcome.

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

• Energy management system algorithms;
• Machine learning for energy forecasting;
• Load forecasting;
• Energy consumption/production analysis, modelling and prediction by means of neural networks;
• Data processing in energy management systems;
• Neural network models and relations in energy management systems;
• Novel applications in energy management systems;
• Advanced modelling approaches of energy systems;
• User-oriented energy management systems designs;
• IoT—Internet of Things (industrial Internet of Things);
• Renewable energy sources;
• Artificial intelligence in demand response;
• Intelligent control and optimization of energy systems;
• Big data analytics for smart grids;
• Reinforcement learning for energy management;
• Deep learning for energy system modelling and simulation;
• Human–machine interactions and decision making in smart energy systems.
• Energy systems’ flexibility, efficiency, and power quality;
• Machine learning and deep learning models for mitigation of wind power fluctuation and methods for power generation;
• Prediction of levelized cost of electricity;
• Classifications using deep learning or advanced machine learning for power quality disturbances;
• Electricity market price prediction using advanced machine learning;
• Case studies can include the following topics: electric vehicles, energy investments, network planning, etc.
• Case study on combined applications of machine learning, IoT, and big data for energy efficiency.

Dr. Sara Carcangiu
Guest Editor

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.

• optimization
• prediction
• performance evaluation
• IoT
• classification
• deep learning
• machine learning
• power systems