Workshop AI for Energy – CAI 2024

IEEE International Conference on Artifical Intelligence 2024 – IEEE CAI 2024

June 25-27, 2024, Sands Expo & Convention Centre, Marina Bay Sands, Singapore

Organized by Zita Vale, G. Kumar Venaygamoorthy, João Soares

Kindly select the option “Workshop AI for Energy” during your registration process for CAI 2024

Energy is the cornerstone of societal prosperity, and its production, delivery, and management are essential to mankind. As the dynamic energy field evolves, we must broaden our perspective to include control and optimization within intricate energy systems. The escalating energy needs of developing economies are undeniable. However, considering our planet’s finite resources and the significant climate impact from the power sector, it’s essential to devise strategies that maintain environmental integrity and champion sustainability.

To navigate the multifaceted socio-economic dimensions of the energy sector, trust in AI becomes crucial. Trust is necessary to ensure that AI systems are reliable, secure, and capable of making informed decisions. Additionally, transparency and accountability are key components of trust in AI. Users and stakeholders must be confident in the algorithms and models used in energy systems to make fair and unbiased decisions.

Explainability is another key factor in energy AI. As AI becomes increasingly common in energy production and consumption, understanding how AI systems make decisions is crucial. Explainability clarifies AI decision-making, allowing stakeholders to trust and confirm results. Explainability also helps discover AI biases and unforeseen repercussions, enabling changes and improvements.

Fairness is crucial in energy AI adoption. National and global energy production and consumption are political considerations, and AI might greatly affect global energy markets. AI systems must not worsen or create inequality. Fairness in AI implies race, gender, and socioeconomic position do not affect outcomes. We can guarantee energy sector AI gains are distributed fairly by supporting fairness in AI system design and deployment.

AI can revolutionize energy. Smart sensors and off-peak machine use cut single-house energy demand. Cities and governments may use AI to distribute electricity, balance load based on expected and real demands, and optimize energy use. AI-controlled energy plant regulation reduces emissions and boosts efficiency. With vast volumes of data, market stakeholders are using AI to foresee alternative futures and make educated decisions on renewable energy.

Integrating AI into the energy business requires trust, explainability, and fairness. By making AI systems trustworthy, transparent, and fair, we can use AI to solve difficult energy production, distribution, and management problems while protecting the environment and promoting sustainability.

Scope and Topics

We are interested in the following topics (but not limited to):

  • AI in autonomous control for wind and solar farms and energy resources
  • AI reduces carbon emissions by enhancing industrial processes and emissions monitoring and compliance.
  • AI in Consumer Products: this helps users directly regulate energy consumption, reducing demand and aiding power networks.
  • AI in Demand Forecasting: improves load balancing, dynamic distribution, and energy resource utilization to maximize consumer benefit and grid utility.
  • AI for Digital Twins: Real-time virtual representations of actual grid assets may be used to examine wind turbines and power plants with. Digital twins enhance energy network maintenance, experimentation, and optimization.
  • AI for Energy Communities enables fair energy trading and benefit distribution, effective energy resource management (production, storage, demand flexibility), and efficient wholesale and local energy market participation.
  • AI for Energy Consumers: it will aid energy efficiency decisions and active customer involvement in demand response programs and neighbor energy exchanges.
  • AI for Energy Efficient Industrial Plants: it promotes renewable energy consumption to make companies more sustainable and energy efficient.
  • AI for Energy Efficient Transportation: it optimizes electric car charging, routing, and interaction with the electric grid, including V2X.
  • AI for Energy Markets enables realistic energy market simulation, player decision-support, renewable energy intensive market models, and local energy market coordination.
  • AI for Oil and Gas. In upstream operations (exploration and production), AI may evaluate reservoir value, adjust drilling and completion plans to local geology, and evaluate well hazards. AI can optimize pipeline and refining scheduling, commodity and product market pricing, trading, and hedging in midstream and refining. AI saves money and boosts spreads in downstream processes.
  • AI in Plant Management reduces operation costs and carbon emissions by estimating system lifespan, predictive maintenance scheduling, and real production capacity.
  • AI to safeguard vital energy infrastructure from network access and viruses.
  • AI for Sensor Fusion, which transforms data from hundreds to millions of sensors for better city, state, and national tracking and decision-making.
  • AI in Smart Grid Operation, Control, and Management to predict outages, maximize power yield, and improve demand-side management to reduce energy demand peaks. Smart Grid optimizes power flow for economic efficiency, dependability, and sustainability by allowing providers and consumers to exchange power and data.
  • AI in Transition to Renewables provides real-time power grid monitoring, precise power fluctuations forecasts, and novel geothermal energy solutions.
  • AI for Sustainability and the Environment. Most of the past applications of this sector focused on normal AI applications for energy, but AI can minimize energy use. Massive language model training (AI energy use), data center energy utilization, etc.

Call for book abstracts (potentially to be published in IEEE-Willey -TBC)

Those interested in giving a speech at the Workshop are invited to submit a proposal/abstract on related topics (see above topics). If your abstract/talk is selected, you can present your research and speech at the workshop in CAI 2024.

Abstracts submission due: February 29 2024. 
Notification of selected proposals: March 7 2024.
Author confirmation to submit full book chapter and present in workshop above: March 10 2024.
Full book chapter ~25 pages in IEEE-Willey format: May 15 2024. 
Revised book chapter: June 15 2024.
Camera-ready: July 31 2024.

How to submit the book chapter

Kindly submit your your proposal by mail to the organizers of the workshop, namely G. Kumar Vennaygamoorthy (, Zita Vale ( Joso Soares (