The 2nd International Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply

Papers submission:


  • Paper submission: March 7th 2022.March 21st 2022,April 4th 2022,  April 14th 2022 (SHARP)
  • Notification of acceptance: May 10th, 2022
  • Camera-ready copies due: June 1st, 2022
  • Workshop days: August 22nd - 24th, 2022

All accepted AI-CARES 2022 papers will be published by Springer in their Communications in Computer and Information Science (CCIS). CCIS volumes are indexed in the Conference Proceedings Citation Index (CPCI), part of Clarivate Analytics’ Web of Science; Scopus; EI Engineering Index; Google Scholar; DBLP; etc. Best papers of the workshop, after further revisions and independent reviews, will be considered for publication in a special issue of Wiley Journal of Software: Evolution and Process (IF 1.305). 

Societal infrastructure such as power-grids, smart houses/buildings, transport and other energy networks are growing exponentially to meet the rising energy demand and rapid industrialisation. There is a growing need that such cyber-physical systems are designed and operated on human-centered values and energy services are delivered efficiently and resiliently. The recent technical developments in forecasting, learning, control and optimisation applications holds immense potential to transform future energy networks in intelligent systems that address urgent social concerns such as the energy crisis, climate change and environment population. There has been a growing interest in using machine learning, data analytics, and the Internet of Things (IoT) in various energy supply networks, including smart cities, power systems, transportation system etc. in applications related to modelling, automation, preventive maintenance, faults detection, and optimisation of energy, space and cost applications. This has resulted in increased energy efficiency, convenience and comfort in smart buildings, and improved robustness and stability in power systems and energy networks. 

AI has been recently used to generate ultra-accurate forecasts that makes it easier to incorporate more renewable energy into power grids. Several machine learning-based security evaluation tools have been developed for power grids. In addition, computer vision methods for remote power management and control have been developed. AI although is in its early stages of implementation, however it can revolutionise the way we produce, supply and consume energy. It also paves the way for a power grid that is self-healing and robust and can respond to system changes. This is particularly important for today's electricity grid, which is increasingly connected to fluctuating power components, such as distributed generation resources, electric cars and storage systems. AI based applications that can support operations while observing grid intrinsic infrastructure restrictions can provide alternative to exorbitant upgrade costs. Thus, AI in energy systems is highly promising with a wide range of interesting research prospects.

The purpose of this workshop is to bring together researchers from multiple disciplines to present their proposed and ongoing work regarding application of machine learning in energy supply systems including smart cities, smart grids and transport systems suggesting state-of-the-art AI-based solutions to improve energy systems' reliability, efficiency, affordability and resilience. Potential topics of this workshop include but are not limited to the following:

  • Energy for smart city applications
  • Reinforcement learning and deep learning for cyber-physical energy supply systems
  • Distributed and robust monitoring and optimization of energy supply systems
  • Robust and data-driven control of energy supply systems
  • Building automation and control
  • Fault diagnosis, localization and prognosis in energy systems
  • Smart meter big data analysis 
  • Topology identification in energy supply systems
  • Machine learning and AI applications for energy supply restoration
  • Utility and residential level load and renewable energy forecasting
  • Condition monitoring and asset management in energy systems
  • Intelligent wide area monitoring, protection, control, and management
  • Privacy and security for machine learning in energy systems

In order to encourage participation and discussion, this workshop solicits two types of submissions:

  • Regular paper submissions about original work not exceeding 10 pages.
  • Short paper submissions on recent or ongoing work on relevant topics and ideas not exceeding 5 pages.

Formatting guidelines:
Online Papers Submission:

Submissions to the workshop must not have been published or be concurrently considered for publication elsewhere. All submissions will be peer-reviewed by, at least, 3 reviewers and judged on the basis of originality, contribution to the field, technical and presentation quality, and relevance to the workshop. Short papers are meant for timely discussion and feedback at the workshop.

Program Committee co-Chairs

  • Sohail Khan, Sino-Pak Center for Artificial Intelligence, Pakistan
  • Thomas Strasser, Austrian Institute of Technology (AIT), Austria
  • Peter Palensky, TU Delft, Netherlands

Program Committee Members

  • Stefan Übermasser, Center for Energy, AIT Austrian Institute of Technology, Austria
  • Reza Arghandeh, Western Norway University of Applied Sciences, Norway
  • Amir Muhammad, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Pakistan
  • Gul Muhammad Khan, University of Engineering and Technology, Peshawar, Pakistan
  • Kashif Imran, Electrical Power Engineering Department, USPCASE, NUST, Pakistan

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