Digitization of Energy Supply and Demand Sides
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".
Deadline for manuscript submissions: closed (22 June 2023) | Viewed by 13328
Special Issue Editors
2. RCM2+ Research Centre for Asset Management and Systems Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Interests: machine learning; artificial intelligence; computer vision; data mining
Special Issues, Collections and Topics in MDPI journals
Interests: automatic development of (web) applications through AI; intelligent systems in maintenance and electrical engineering; computer vision
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
From objects associated with the sun and sky, to nano-sensors in small animals, modern civilization revolves around data, producing gigabytes every second. The whole system can be visualized as a river of pulsating bytes, flowing into modern data centers, data lakes, and data warehouses that utilize terawatts of power.
It is our aim and duty to ensure that data are ethically harvested, safely stored, and correctly used to extract information and knowledge.
Increasingly smart approaches to harvesting energy and data, faster networks, and smaller devices, feed and expand the human senses, and intertwine with and enhance human lives in the modern world, which is increasingly more virtual, less physical, and more about energy, data, and connections.
The Internet of Things will consist of billions of smart appliances, draining small amounts of power from the electrical grid and feeding terabytes of data to the internet. Electric vehicles are expected to add additional strain to an already complex power grid, which increasingly relies on micro-production from renewable sources. Intelligent and efficient grid management algorithms are urgently required for the smart grid of the future.
The sole topic of this Special Issue is data, and the wondrous process by which the world becomes increasingly more digital. From the use of smart devices to the latest data analysis algorithms, we are feeding, sensing, and shaping a world of data. We invite you to contribute your work and views regarding the processes of efficiently producing and harvesting energy and data; monitoring processes; transferring and storing data; interoperability; data pre-processing; image and text classification; augmented and virtual reality; and related topics.
The scope includes, but is not limited to, the following:
- Augmented and virtual reality;
- Data analysis;
- Energy consumption forecast;
- Energy production forecast;
- Impact of digitization on institutions;
- Impact of energy demand and production on social activity;
- Intelligent automation systems;
- Intelligent data processing;
- Intelligent sensors for real-time decisions;
- Intelligent system integration and inter-operation;
- IoT;
- On-demand smart decisions;
- Smart embedded systems;
- Smart energy use at home and in industries.
Dr. Mateus Mendes
Dr. Inácio Fonseca
Guest Editors
Manuscript Submission Information
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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.
Keywords
- artificial neural networks
- augmented reality
- computer vision
- condition monitoring
- data farming
- data mining
- deep learning
- object detection
- nanotechnology
- pattern recognition
- predictive maintenance
- time series analysis
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