Optimization of Energy Systems Using Intelligent Methods

Dear Colleagues,

Energy use is fundamental for any civilization. Abundant, safe, and inexpensive energy is the basis for economic development and progress. As civilizations advance, they require increasingly large amounts of energy. On a global scale, however, there is now an effort to move from an oil-centered economy with growing needs to an economy which is more efficient and uses less energy to achieve the same degree of human satisfaction. Ideally, in the future, we will use less energy and still be able to improve human life conditions.

This apparent contradiction is solved by making use of the significant knowledge and technology advances which lead to process optimization, namely those which are achieved mainly through applications of classical and deep machine learning algorithms over large datasets.

 Now that increasingly large amounts of data are available, modern data analysis techniques can be applied in order to extract patterns which would otherwise be impossible to find. This makes it possible to make smarter decisions in different areas, such as energy production, life cycle optimization, energy distribution, and many others.

This Special Issue is mostly dedicated to this topic, whereby modern algorithms are applied in order to optimize systems and processes, from production to consumption, aiming to increase efficiency and profitability while still keeping energy demands low, or by taking advantage of otherwise wasted energy.

  Papers are encouraged in the following areas:

• Applications of classical machine learning and deep learning;
• Consumption optimization;
• Data analysis;
• Energy efficiency;
• Energy production;
• Forecasting;
• Process and product optimization.

Dr. Mateus Mendes
Dr. Inácio Fonseca
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.

• artificial intelligence
• big data
• biomass energy
• energy management
• systems computer
• vision convolutional
• neural networks
• data analysis
• deep learning
• energy forecasting
• evolutionary algorithms feature engineering
• IoT
• machine learning
• neural networks optimization
• performance evaluation
• pollution forecast
• power safety
• prediction renewable
• energy smart
• energy systems
• sustainability