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Special Issue Editors

Dr. Israa Medlej

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Guest Editor

Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: artificial intelligence devices; quantum physics; solid state physics; condensed matter physics; spintronics; magnonics; ferroelectric and multiferroic materials; spintronics and magnonics based neuromorphic computing; unconventional computing; thin film magnetism; magnetic properties; THz; magnetic insulators; antiferromagnetic material; ferrimagnet; skyrmions; anti-skyrmions; antiferromagnetic skyrmions; vortices; domain walls; superconductivity and superconductors; neutron scattering; neutron diffraction; superconducting quantum design; vibrating sample magnetometer; micromagnetic simulations
Special Issues, Collections and Topics in MDPI journals

Dr. Ambra Fioravanti

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Guest Editor

Institute of Sciences and Technologies for Sustainable Energy and Mobility—CNR–STEMS, Via Canal Bianco 28, 44124 Ferrara, Italy
Interests: metal oxide nanostructures; material synthesis and characterizations; thick films deposition; chemoresistive gas sensors; industrial and environmental monitoring; hydraulic fluids properties and characterizations; Life Cycle Assessment (LCA)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue of Energies on “Artificial Intelligence and Smart Energy: The Future Approach”.

Artificial intelligence (AI) is used in creating machine learning, deep learning, and predictive analytics by taking inspiration from the human brain. For example, the construction of circuits that can achieve computational function with more efficient power compared to conventional computers leads to the establishment of new fields such as neuromorphic computing. Nowadays, the large amount of complex data generated by both humans and machines far exceeds our ability to manage and interpret them. Artificial intelligence is likely to help us in solving complex problems and in decision making. AI devoted to developing smart energy systems is paving intelligent avenues to help society in building more sustainable, advanced, and technological communities.

For this Special Issue, full-length articles, short communications, perspectives, and review articles will be considered for publication. Topics of interest include, but are not limited to:

Quantum computer;
Neuromorphic computing;
Neural network;
Convolutional neural network;
Advanced AI algorithms;
AI applications and based devices;
Nuclear energy
Energy storage and saving;
Energy sustainability;
Energy modeling.

Dr. Israa Medlej
Dr. Ambra Fioravanti
Guest Editors

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 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 intelligence
artificial operations
smart energy system
intelligent approximations
AI technology
data analysis and mining

Published Papers (2 papers)

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Research

Jump to: Review

20 pages, 1138 KiB

Open AccessArticle

Al-Biruni Earth Radius Optimization Based Algorithm for Improving Prediction of Hybrid Solar Desalination System

by Abdelhameed Ibrahim, El-Sayed M. El-kenawy, A. E. Kabeel, Faten Khalid Karim, Marwa M. Eid, Abdelaziz A. Abdelhamid, Sayed A. Ward, Emad M. S. El-Said, M. El-Said and Doaa Sami Khafaga

Energies 2023, 16(3), 1185; https://0-doi-org.brum.beds.ac.uk/10.3390/en16031185 - 21 Jan 2023

Cited by 2 | Viewed by 1651

Abstract

The performance of a hybrid solar desalination system is predicted in this work using an enhanced prediction method based on a supervised machine-learning algorithm. A humidification–dehumidification (HDH) unit and a single-stage flashing evaporation (SSF) unit make up the hybrid solar desalination system. The Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) algorithms serve as the foundation for the suggested algorithm. Using experimental data, the BER–PSO algorithm is trained and evaluated. The cold fluid and injected air volume flow rates were the algorithms’ inputs, and their outputs were the hot and cold fluids’ outlet temperatures as well as the pressure drop across the heat exchanger. Both the volume mass flow rate of hot fluid and the input temperatures of hot and cold fluids are regarded as constants. The results obtained show the great ability of the proposed BER–PSO method to identify the nonlinear link between operating circumstances and process responses. In addition, compared to the other analyzed models, it offers better statistical performance measures for the prediction of the outlet temperature of hot and cold fluids and pressure drop values. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Energy: The Future Approach)

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Review

Jump to: Research

37 pages, 3006 KiB

Open AccessReview

Reinforcement Learning: Theory and Applications in HEMS

by Omar Al-Ani and Sanjoy Das

Energies 2022, 15(17), 6392; https://0-doi-org.brum.beds.ac.uk/10.3390/en15176392 - 01 Sep 2022

Cited by 8 | Viewed by 2052

Abstract

The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Energy: The Future Approach)

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