Special Issue "Data Analytics in Energy Systems"
Deadline for manuscript submissions: closed (30 December 2020).
Interests: energy and environmental engineering systems; air pollution modeling, simulation and control; sustainable development, planning and scheduling of process operations; dynamic modeling and optimization; combinatorial optimization
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Interests: big data analytics for fault detection and diagnosis for large scale systems; time series analysis of energy systems; multivariate statistics; data-driven modeling and optimization
The incorporation of big data in the energy industry is receiving renewed interest due to the rise of pervasive computing devices (i.e., sensors that collect and transmit data) and new algorithmic developments in data analysis, data storage capabilities, and machine learning. The integration of data analytics in energy systems can be used to improve the control, monitoring, and efficiency of the industry. These improvements can be applied in the design phase where the nominal operation is defined. Data-based models can also supplement mechanistic models to estimate operating parameters. In the production stage, scheduling (through optimization) can be used as another source of process improvement. However, despite the setting of operational parameters during the design stage, during the actual production and processing stages, undesired disturbances or unforeseen behaviors often take place. Examples of these undesired disturbances are the variations in energy sources, malfunctioning of process instruments, and varying processing times.
Data analytics has drawn a great deal of attention in today’s energy system studies. A large number of viewpoints in ranging from economic studies (cost-benefit analysis, optimal operation, scheduling, etc.) to technical studies (renewable energy modeling, electric vehicles modeling, energy hubs, etc.) involve big data problems. The big data in energy systems have brought several opportunities and challenges simultaneously for researchers. The main challenges in big data analytics and mining include data inconsistency and incompleteness, scalability, timeliness, data reduction and integration, and data security. To deal with these challenges, the big data should be transformed into a reasonable structure using data mining algorithms. The characteristics of big data should be considered in the transformation algorithms that includes “volume”, “velocity”, “variety” and “value”.
This Special Issue is intended to present original research papers with high quality and novelty on “Data Analytics in Energy Systems”.
Topics of interest include, but are not limited to:
- Data classification
- Data Clustering
- Distributed data mining
- Machine learning
- Internet of thing
- Data cleaning
- Data reduction
- Data integration
- Data transformation
- Cloud data
- Data forecasting
- Data management
- Data visualization
- Data statistical analysis
- Data collection
- Fault detection and diagnosis for energy systems.
Prof. Ali Elkamel
Dr. Ali Ahmadian
Dr. Mohamed Bin Shams
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.
- big data analytics
- parallel and distributed computing
- data mining
- cloud computing
- machine learning