Machine and Deep Learning Techniques in Sustainable Energy Systems

  in Special Issue   Posted on September 28, 2020

Information for the Special Issue

Special Issue Call for Papers:

In recent years, an extensive amount of research has been conducted in optimizing the operation of sustainable energy systems. The goal of the sustainable energy management is to fulfill the energy needs of the world population and the demands of billions of devices connected in the Internet of Things (IoT) infrastructure with cost-effectiveness and environmental sustainability. The rapid development of machine learning techniques, such as Deep Belief Networks (DBN) and Convolutional Neural Network (CNN), has driven researchers and engineers to design efficient sustainable energy solutions. These solutions will further help in providing an uninterrupted power supply to smart homes/buildings/cities while helping to complete the energy needs with green energy. However, the nature of sustainable energy is highly dynamic due to environment dependency, multivariate structure, and uncertainty of non-linear energy systems. Thus, intelligent solutions are required to address the uncertainty problems and optimize the operation of sustainable energy systems. Also, traditional machine learning techniques such as perceptron and logistic regression have limitations of performing inappropriately in uncertain environments. In this regard, a promising machine learning technique called Reinforcement Learning (RL) has emerged with the advantages of self-learning and high adaptability. The RL does not depend on the system model and, therefore, this property can be a better solution in managing the uncertainties in sustainable energy systems. Thus, keeping these advantages in mind, the RL and similar machine and deep learning algorithms can be effective techniques in overcoming the challenges that exist in designing and planning of sustainable energy management systems. This special issue aims to provide insight into the latest research going on in the domain of machine and deep learning for sustainable energy management with theoretical and experimental analysis, by publishing papers reporting the state-of-the-art research work.

Scope and Significance of the Special Issue:

The purpose of this SI is to report the most recent techniques and developments in the areas related to sustainable energy, green energy, renewable power systems, the Internet of Energy, energy management in smart homes and buildings, and smart grids. This special issue will elaborate the use of the powerful ML and RL concepts in controlling the uncertainty that arises due to the integration of renewable energy sources with the main power supply. Papers reporting research studies and findings related to the following points are sought:

  • Real-time load management in smart homes/cities/grids
  • Machine Learning techniques in designing sustainable energy systems
  • Roles of reinforcement learning in sustainable energy management
  • Real-time energy decision making in HEMS using Reinforcement learning
  • Energy data management of renewable power systems
  • Real-time management of uncertainties in sustainable energy
  • Smart home energy management systems
  • Energy supply to smart homes/cities using sustainable energy sources
  • Energy-efficient sustainable cities and societies
  • Virtual power plants and sustainable energy
  • Uncertainties in renewable power forecasts
  • Theoretical and algorithmic aspects of sustainable energy models
  • Optimization of HEMS using the Reinforcement Learning
  • Design of intelligent household appliances using reinforcement learning
  • Design of power electronics in the domain of sustainable energy
  • Enabling computational sustainability with reinforcement learning

Important Dates

Submission Deadline: November 30, 2020

Acceptance Deadline: February 28, 2021

Murad Khan received a B.S. degree in computer science from the University of Peshawar Pakistan in 2008. He has completed his Master and Ph.D. degrees both in computer science and engineering from the School of Computer Science and Engineering at Kyungpook National University, Daegu, South Korea in 2017. Dr. Khan is currently a Korea Research Fellow with the School of Computer Science and Engineering at Kyungpook National University, Daegu, South Korea. His area of expertise includes ad-hoc and wireless networks, architecture designing for Internet of Things, and Communication Protocols designing for Sustainable Smart Cities and Homes, Sustainable Energy Management, Smart Grids, Reinforcement Learning, etc. Dr. Khan has published various international conference papers in IEEE Globecom, IEEE LCN, IEEE ICC, IEEE WCNC, ACM SAC, etc. and world’s top-tier Journals such as IEEE Wireless Communications, IEEE Communications Magazine, IEEE ACCESS, Sustainable Cities and Society, Future Generation Computer Systems, Computers & Electrical Engineering, etc. Also, Dr. Khan edited two book chapters and a book in Springer and CRC Press. Dr. Khan has served as a lead guest editor of the special sections on “Designing and Planning of Energy Efficient Sustainable Cities and Societies: A Smart Energy Approach” in Transactions on Emerging Telecommunications Technologies (Wiley) and “Internet of Energy – designing and planning energy-efficient smart control systems” in Computer & Electrical Engineering Journal (Elsevier) and a guest editor of the special section “New Advances of Vehicular ad hoc Networks (VANETs) in Electronics Journal (MDPI) ”. He has also served as a TPC member of ACM SAC 2017- Human-Computer Interaction Track, SMARTTECH 2020 – Internet of Things Track, WOSTech 2017, and an organizer of the Intelligent Communication Network Technologies workshop, VTC Fall 2020. During his Ph.D. studies, Dr. Khan received three prestigious scholarships, i.e., Center for Self-Organized Software fellowship, BK21 Plus Program fellowship, and Kyungpook National University International Graduate Scholarship (KINGS). Also, Dr. Khan was awarded a bronze medal in ACM SAC Student Research Competition (2015), the best research paper award in the 2nd International Conference on Networking and Computer Application (2016), and an excellent researcher in Qualcomm Innovation Award’s competition (2016).



Josep M. Guerrero (S’01-M’04-SM’08-FM’15) received the B.S. degree in telecommunications engineering, an M.S. degree in electronics engineering, and the Ph.D. degree in power electronics from the Technical University of Catalonia, Barcelona, in 1997, 2000 and 2003, respectively. Since 2011, he has been a Full Professor with the Department of Energy Technology, Aalborg University, Denmark, where he is responsible for the Microgrid Research Program. From 2014 he is chair Professor at Shandong University; from 2015 he is a distinguished guest professor at Hunan University; and from 2016 he is a visiting professor fellow at Aston University, UK, and a guest Professor at the Nanjing University of Posts and Telecommunications. From 2019, he became a Villum Investigator by The Villum Fonden, which supports the Center for Research on Microgrids (CROM) at Aalborg University, being Prof. Guerrero the founder and Director of the same center.

His research interests are oriented towards different microgrid aspects, including power electronics, distributed energy-storage systems, hierarchical and cooperative control, energy management systems, smart metering, sustainable energy, and the internet of things for AC/DC microgrid clusters and islanded mini-grids. Specially focused on microgrid technologies applied to offshore wind, maritime microgrids for electrical ships, vessels, ferries and seaports, and space microgrids applied to nanosatellites and spacecraft. Prof. Guerrero is an Associate Editor for a number of IEEE TRANSACTIONS. He has published more than 600 journal papers in the fields of microgrids and renewable energy systems, which are cited more than 50,000 times. He received the best paper award of the IEEE Transactions on Energy Conversion for the period 2014-2015, and the best paper prize of IEEE-PES in 2015. As well, he received the best paper award of the Journal of Power Electronics in 2016. During six consecutive years, from 2014 to 2019, he was awarded by Clarivate Analytics (former Thomson Reuters) as a Highly Cited Researcher with 50 highly cited papers. In 2015 he was elevated as IEEE Fellow for his contributions on “distributed power systems and microgrids.”


Tamer Khatib is a researcher in photovoltaic power systems and solar radiation. He holds a B.Sc. degree in electrical engineering with a specialization in electrical power systems from An-Najah National University, Palestine as well as an M.Sc. degree and a Ph. D degree in electrical, electronic, and systems engineering with a specialization in photovoltaic power systems from the National University of Malaysia, Malaysia. In addition, he holds Habilitation (the highest academic degree in German-speaking countries) in renewable and sustainable energy from Alpen Adria Universitat, Klagenfurt, Austria.

Currently, he is an Associate professor of renewable energy at An-Najah National University and the director of An-Najah Company for Consultancy and Technical Studies. He is also a chair of two master programs at An-Najah National University which are the Clean Energy and Energy Conversion Engineering Master Program and Electrical Power Engineering Program. He is also the chair of the IEEE Palestine sub-section.

He has 2 patents, 4 books, and 120 research articles, while his current h-index is 34. He is a senior member of the IEEE, IEEE Power and Energy Society, the International Solar Energy Society, the Jordanian Engineers Association, and the International Association of Engineers.

His research interests mainly fall in the scope of photovoltaic systems, sustainable energy management, and solar energy fundamentals. These interests include PV systems design and optimization, modeling and control of PV systems, hybrid PV/Wind systems, hybrid PV/diesel systems, Grid-connected PV systems, sun trackers, MPPT technology, inverters in a PV system, solar chargers, batteries and charge controllers, solar energy fundamentals, solar energy prediction, AI applications for solar energy and PV systems, wind power systems, wind chargers, wind energy modeling, and prediction.


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