Advances in Energy Investment and Services through Mathematical Modeling, Optimization, and Machine Learning

Dear Colleagues,

Two extremely advanced analytics technologies that are employed by a wide range of applications are mathematical optimization and machine learning. Both are convincing illustrations of how mathematics can be employed to solve complex problems because they are founded on a strong mathematical foundation. Energy & financial market prediction, image and discourse recognition, virtual personal assistants, fraud detection, self-driving vehicles, production planning, workforce timetabling, electric power distribution, shipment routing, design optimization, robotics, etc., are just some of the apparently unending uses for both technologies. A well-developed but still in high-demand research direction-mathematical modeling is closely entwined with energy investment and services optimization through machine learning. An intricate mathematical model of a business procedure, technical construction, or physical phenomenon, for instance, is used to perform optimization. When conventional approaches fail because of uncertainty, such as variance or noise in the specific data values, machine learning techniques can be successfully used to estimate the models' parameters.

The subjects of mathematical modeling, optimization techniques, and various machine learning strategies in energy investment are the focus of this Special Issue in the journal “Energies”. The general requirements of the journal should be fulfilled by submitted papers, with a strong emphasis on novel analytical or numerical approaches to difficult issues. Potential subjects could be, but are not restricted to:

• Energy investment
• Machine learning’s mathematical foundations;
• Neural networks' new machine learning algorithms, approaches, and architectures;
• Mathematical models and machine learning;
• Mathematical models, optimization, and machine learning algorithms-based data analysis;
• Statistical models and stochastic processes;
• Continuous and discrete optimization, linear and nonlinear optimization, derivative-free optimization;
• Algorithms’ deterministic and stochastic optimization;
• high-performance computing;
• Scientific and technological application of machine learning, mathematical modeling, and optimization.

Dr. Hossein Moayedi
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.

• • Energy investment • Mathematical modeling • Mathematical optimization • Theory and applications controlling • High-performance computing • Stochastic processes • Numerical analysis and simulation • Machine learning • Data analytics