Keynote 1: 

Decision and control for smart energy communities


 Prof. Mariagrazia Dotoli (IEEE Fellow)

Politecnico di Bari, Italy


A powerful solution contributing to the green transformation of modern power systems is represented by the so-called energy community. The term ‘energy community’ denotes a community of users (private, public, or mixed) located in a specific reference area, where all stakeholders – such as end-users (e.g., citizens, companies, etc.), market players (e.g., utilities, service providers), practitioners, planners and policy-makers – actively cooperate to develop a ‘smart’ energy system. Independently from the implemented architecture, the success of energy communities relies on the deployment of suitable decision and control mechanisms that efficiently and widespreadly exploit renewable sources and distributed storage, while enabling the application of measures oriented to cost-effectiveness, sustainability, and reliability. In this context, the talk presents innovative decision and control frameworks, such as game-theoretic methodologies, for energy communities composed of heterogenous actors equipped with trading and sharing service oriented energy systems. The effectiveness of the presented approaches is shown through numerical simulations on realistic scenarios.

Biography of Prof. Mariagrazia Dotoli

Dr. Mariagrazia Dotoli ( holds a PhD in Electrical Engineering and is a Full Professor in Automatic Control at Politecnico di Bari, Italy, where she is also the Founder and Coordinator of the Italian National PhD Program on Autonomous Systems (DAUSY,, an interuniversity PhD Program with 25 Universities all over Italy. Prof. Dotoli was the Vice Rector for research of Politecnico di Bari and a member elect of the Academic Senate.

She is the founder and director (2012-) of the Decision and Control Laboratory of Politecnico di Bari, Italy and was the founder and coordinator (2020-2022) of PhD Program on Industry 4.0, Politecnico di Bari, Italy She is the founder (2012) of Politecnico di Bari spin-off company Innolab S.r.l. ( She is member of the Executive Board of the Italian MEDITECH competence center for Industry 4.0 funded by the Italian Ministry for Economic Development.

She has been a visiting scholar at the Paris 6 University, France, and at the Technical University of Denmark. She is currently an expert evaluator of the European Commission and of many more European national research centers.

Her research interests include decision and control approaches for energy systems, smart manufacturing, intelligent logistics, transportation systems and smart cities. She is author of 200+ international publications in these fields, including 1 textbook (in Italian) and 80+ international journal papers. Her h-index in Google Scholar equals 45, with 7000+ citations.


She served in the organization of many well-reputed international conferences. Currently, she is the General chair of the 2024 IEEE Conference on Automation Science and Engineering.

Prof. Dotoli has been a member of the IEEE Systems Man and Cybernetics Society (SMCS) for over two decades. She has volunteered in SMCS in several roles, serving as 2016-2020 Editor in Chief of the SMCS eNewsletter, co-chair and founder of the Technical Committee on Intelligent Systems for Human-Aware Sustainability, 2020-2022 member at large of the SMCS Board of Governors, Chair of the SMCS Diversity and Inclusion Committee, and Liaison of SMCS with IEEE TAB Committee on Diversity and Inclusion. She is currently the SMCS Vice President elect for biennium 2023-2024 for Membership and Student activities.

Prof. Dotoli is listed in the world top 2% scientists list for career-long impact and single-year categories in the “Industrial Engineering & Automation” and “Artificial Intelligence & Image Processing” fields in accordance with the well-known standardized citation metrics author database developed by Ioannidis et al., 2022 and released by Stanford University and Elsevier BV.

Keynote 2: 

Autoencoder-embedded Evolutionary Algorithmsfor High-dimensional Expensive Optimization Problems


 Prof. MengChu Zhou

( Fellow of IEEE, IFAC, AAAS, CAA and NAI)

New Jersey Institute of Technology, USA


High-dimensional computationally Expensive Problems (HEPs) in which a single fitness evaluation consumes hours or even days have attracted much attention from both academia and industry. Exponentially expanding search space and complex landscape brought by numerous decision variables make HEPs extremely challenging to be solved by traditional algorithms with limited physical/computational resources. Therefore, an Autoencoder-embedded Evolutionary Optimization (AEO) framework is invented to deal with them. To be specific, high-dimensional search space can be compressed to informative low-dimensional space by using an autoencoder as an effective dimension reduction tool. The search operation conducted in this low-dimensional space facilitates the population in convergence towards the optima. To balance the exploration and exploitation ability during optimization, two sub-populations are adopted to coevolve in a distributed/parallel fashion, wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process. Dynamic information exchange is conducted between them after each cycle to promote population diversity. Moreover, surrogate models can be incorporated into AEO (SAEO) to further boost its performance by reducing unnecessary fitness evaluation. Compared with the state-of-the-art algorithms for HEPs, AEO shows extraordinarily high efficiency for these challenging problems while SAEO can greatly improve the performance of AEO in most cases, thus opening new directions for various swarm optimization and evolutionary algorithms under both AEO and SAEO to tackle HEPs and greatly advancing the field of high-dimensional computationally expensive optimization. Their recent applications to mobile edge-computing systems, human-cyber-physical systems, and production scheduling are also illustrated.

Biography of Prof. MengChu Zhou

MengChu Zhou received his B.S. degree in Control Engineering from Nanjing University of Science and Technology, Nanjing, China in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology, Beijing, China in 1986, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, NY in 1990.  He joined New Jersey Institute of Technology (NJIT), Newark, NJ in 1990, and has been Distinguished Professor in Electrical and Computer Engineering since 2013. His research interests are in Petri nets, intelligent automation, AI, Cloud/edge Computing, Internet of Things, big data, web services, and intelligent transportation.  He has over 1200 publications including 17 books, 850+ journal papers (650+ in IEEE transactions), 31 patents and 32 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering, and Associate Editor of IEEE Internet of Things Journal, IEEE Transactions on Intelligent Transportation Systems, and IEEE Transactions on Systems, Man, and Cybernetics: Systems. He was Editor-in-Chief of IEEE/CAA Journal of AutomaticaSinica (2018-2022). He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man and Cybernetics Society, Excellence in Research Prize and Medal from NJIT, and Edison Patent Award from the Research & Development Council of New Jersey. He has been among most highly cited scholars since 2012 and ranked top one in the field of engineering worldwide in 2012 by Web of Science. His present Google citation count is well over 67000 with h-index being 132. He was ranked #99 in the world among the 2023 Top 1000 Scientists in Computer Science in the World by He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is a Fellow of IEEE, International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS), Chinese Association of Automation (CAA), and National Academy of Inventors (NAI).