Special Issue "Machine Learning Prediction Models in Energy Systems"
Deadline for manuscript submissions: 10 February 2021.
Interests: machine learning; soft computing techniques; big data analysis; IoT; predictive analytics; hybrid techniques in intelligent measurement; signal and image processing; modeling and diagnostics; fault diagnostics; optimization
2. Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest 1034, Hungary
Interests: machine learning; deep learning; ensemble and hybrid models; applied mathematics; soft computing; deep reinforcement learning; machine learning for big data; mathematical IT; hydropower modeling; prediction models; time series prediction; climate models; machine learning for remote sensing; hazard models; extreme events; atmospheric models; forecasting models; predictive analytics; Internet of Things
This Special Issue is devoted to the latest advancements in prediction models used in energy systems. We invite scientists from around the world to contribute to developing a comprehensive collection of papers on the progressive and high-impact realm of prediction models and diagnostics methods for energy applications. Novel algorithms, new applications, comparative analysis of models, case studies, and state-of-the-art review papers are particularly welcomed.
Very recently, prediction models have been fundamentally revolutionized thanks to affordable computational power, big data technologies, efficient data handling, pre-processing methods, and most importantly, intelligent learning algorithms. Novel machine learning methods, hybrids, ensembles, and deep learning methods integrated with intelligent optimization, various soft computing techniques, and/or advanced statistical methods are rapidly emerging to deliver models with higher accuracy. Today, prediction models are becoming essential in modelling, handling, and diagnosing energy systems with a growing widespread popularity. From energy generation, conversion, distribution, consumption, power, price, loss, load, and demand forecasting to control, diagnosis, failure identification, performance, and maintenance, novel prediction models have shown great progress with promising results.
Prediction models greatly contribute to empowering energy solutions in a broad range of applications. Sustainable and clean energy, smart grids and networks, NetZero, energy selection, energy-saving, emissions estimation/monitoring, fault detection, batteries, buildings, fuels, wind power, smart energy systems, lighting, solar photovoltaic power, heating systems, fuel cells, energy prices, biofuels, power prediction, safety, security, energy theft, performance, production, energy management, Internet of Things (IoT), smart cities, facility maintenance (including mobility and transportation), renewable energies, and nuclear and other energy wastes management are among the challenging applications of prediction models, and are relevant to this Special Issue. As a response to the recent advancements in this domain, the objective of this collection is to present notable methods and applications of prediction models.
Prof. Annamária R. Várkonyi-Kóczy
Dr. Amir Mosavi
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 1800 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 systems
- prediction models
- machine learning
- deep learning
- deep reinforcement learning
- hybrid and ensemble models
- soft computing
- big data
- Internet of Things (IoT)
- demand prediction
- consumption prediction
- load prediction
- renewable energy production
- artificial intelligence
- explainable artificial intelligence (XAI)
- smart grids
- cost prediction
- systems maintenance
- short-term and long-term prediction models
- energy performance
- solar energy prediction
- wind energy prediction
- building energy
- sustainable energy
- anomaly detection
- energy saving
- energy price prediction
- energy conversion and management
- energy conversion efficiency
- energy and climate change
- smart urban energy systems
- nature-inspired optimization algorithms