C  A  L  L     F O R       P A P E R S


1st International Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply (AI-CARES 2021)
September 27 - 30, 2021 - Linz, Austria

http://www.dexa.org/ai-cares2021
email: dexa@iiwas.org
Papers submission: https://easychair.org/conferences/?conf=aicares2021

 
*** IMPORTANT DATES ***
Submission of full papers: April 1, 2021 
Notification of acceptance: June 1, 2021
Camera-ready copies due: June 30, 2021
 
*** PUBLICATION ***
The workshop papers will be published by Springer in "Communications in Computer and Information Science." 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). 

*** SCOPE ***
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.

*** TOPICS OF INTEREST ***
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.
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

*** SUBMISSION GUIDELINES ***
In order to encourage participation and discussion, this workshop solicits two types of submissions - regular papers and short papers:
- 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: http://www.dexa.org/formatting_guidelines
Online Papers Submission: https://easychair.org/my/conference?conf=aicares2021

*** REVIEW PROCESS ***
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.

*** PC Chairs ***
- Sohail Khan, Sino-Pak Center for Artificial Intelligence, Pakistan
- Thomas Strasser, AIT Austrian Institute of Technology, Austria

*** PC Members ***
• Hafsah Ahmed, Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Pakistan
• Muhammad Ehatisham-ul-Haq, Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Pakistan
• Zaffar Haider, Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Pakistan
• Jawad Hussain, Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Pakistan
• Arshad Iqbal, Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Pakistan
• Saima Jabeen, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Pakistan
• Sohail Khan, Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Pakistan
• Fiza Murtaza, Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Pakistan
• Thomas Strasse, Center for Energy, AIT Austrian Institute of Technology, Austria
• Stefan Übermasser, Center for Energy, AIT Austrian Institute of Technology, Austria
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For further inquiries, please contact PC Chair/co-Chairs (ai-cares2021@easychair.org)