Special Issue "Predicting the Future—Big Data and Machine Learning"
A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 10421
Interests: machine learning; deep learning; atmospheric turbulence; astronomy; adaptive optics; solar observation
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The Guest Editor is inviting submissions for a Special Issue of Energies on the subject of “Predicting the Future—Big Data and Machine Learning”. Due to the increase in the capabilities of microprocessors and to the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that, nowadays, machine learning and Big Data have an important presence in the field of Energy.
This Special Issue will focus on applications of Big Data architectures and machine learning methodologies in the field of energy. Topics of interest for publication include, but are not limited to:
- Big data architectures of power supply systems;
- Energy exploration and exploitation: energy management modeling;
- Energy in physical cosmology;
- Energy saving and efficiency models;
- Environmental effects of energy consumption;
- Pollution forecasting related to the generation of energy;
- Prediction of occupational health and safety outcomes in the energy industry;
- Price forecast prediction of raw materials for energy production: coal, gas, oil, uranium, etc.;
- Predictive analysis of energy resources;
- Energy management of smart buildings.
We invite submission detailing innovative technical developments, reviews, case studies, and analyses, as well as assessments and papers from other disciplines which are relevant to the field of energy from the point of view of Big Data and machine learning applications.
Dr. Fernando Sánchez Lasheras
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.
- big data architectures
- machine learning
- pollution forecast
- raw materials price forecast
- deep learning
- convolutional neural networks