Special Issue "Machine Learning and Deep Learning for Energy Systems"
Deadline for manuscript submissions: 30 June 2021.
Interests: intelligent systems; soft computing; fuzzy control; modeling and simulation; biometrics
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An energy system can be a combination of mechanical, chemical, and electrical, and it can cover various dimensions of energy types that include renewables and other alternative energy systems as well. High-scale advancement, however, is facing a critical decision-making crisis, as most energy systems are not able to satisfy the demand–supply ratio and performance optimization, do not know how to deal with performance efficiency, are less understanding of the impact of energy outcomes to the environment, and are not of use in the renewable energy front. Energy firms are generating huge data, both structured and unstructured. IoT alongside smart sensors are participating in the collection of massive data on energy production and consumption. As data are getting bigger and bigger, the number of challenges is also growing at a rate never seen before.
Recently, it has been noted that the machine learning and deep learning models are growing in popularity when it comes to handling big data for energy optimization, and decision-making processes. Moreover, a lot of prediction models proposed in the last two years based on machine learning and, very recently, deep learning have performed considerably well and led toward energy-data-related predictions. The reason is that in the case of extraction of functional dependencies from observations of energy-related projects, these data-driven models have experienced a leap in performance. Today, the scenarios are such that the machine learning, data science, and deep learning models are almost essential for predictive modeling of energy consumption and production rate maintenance, and, finally, accurate demand analysis with high speed. The proposed models now understand the functionalities of energy much better than earlier ones. In addition, machine learning, data science, and deep learning are providing considerable performance efficiency on renewable energy related projects as well. In fact, scientists have started to organize top-level conferences on deep learning technology adaptations on energy-related high-value projects.
This Special Issue aims to provide comprehensive coverage on cutting-edge research and state-of-the-art methods on machine learning, data science, and deep learning applications on energy-related projects. Authors are requested to submit papers on (but not limited to) the following topics:
- Optimization of renewable energy using machine learning and deep learning;
- Machine learning and deep learning models for mitigation of wind power fluctuation and methods for power generation;
- Prediction of levelized cost of electricity;
- Forecasting model for wind speed and hourly and daily solar radiation;
- Predictive models for smart building with heating and cooling load prediction;
- Saving energy using predictive models;
- Prediction of hourly global solar irradiation;
- Forecasting of PV power generation;
- Performance evaluation of solar thermal energy systems;
- Classifications using deep learning or advanced machine learning for power quality disturbances;
- Electricity market price prediction using advanced machine learning;
- Case study on combined applications of machine learning, IoT and big data for energy efficiency.
Prof. Dr. Valentina Emilia Balas
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 papers will be 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 2000 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.
- Performance evaluation