With the increasing penetration of distributed energy resources, such as electric vehicles (EVs), photovoltaics (PVs) and prosumers, it becomes more challenging for the central system to balance the demand and supply in real-time. The rising of emerging technologies in key sectors such as the rapid development of AI technology and uptake of data centres across the world further complicates the problem and situation.  Local energy markets and smart network services are seen as an efficient and promising solution to enable autonomous and decentralised demand and supply balancing at local level to share the burden of whole system balancing without hindering the evolution of emerging technologies. As a result, there are greater interests in investigating new market mechanisms and transactive energy management to support local energy trading and balancing, and how these mechanisms may impact on wholesale energy markets and incumbent suppliers, generators and network operators, and how they may support the integration of high volume of renewable energy and demand responses. These new requirements in transactive energy management and energy markets call for advanced machine learning and computational intelligence techniques to find efficient solutions from advanced energy forecasting to distributed and large-scale energy management. This timely special session aims to showcase the latest development in advanced applications of machine learning and computational intelligence to smart energy management and smart energy markets especially in key sectors such as in AI infrastructure and manufacturing, contributing to the ultimate objective of achieving a net-zero society.

Topics of interest include, but are not limited to:

- Energy forecasting

- Distributed machine learning and computational intelligence approaches for smart energy applications 

- Privacy-preserving machine learning and computational intelligence approaches for smart energy applications 

- Green and energy-efficient AI

- Energy management in AI infrastructures such as data centres

- Carbon emission-aware energy management

- Computational intelligence approaches for energy management in energy-intensive sectors such as transportation and manufacturing 

- Demand response/ renewable energy integration in the wholesale market 

- Data-driven demand-side management and demand response applications 

- Transactive approaches to the demand flexibility management 

- Computational intelligence approaches for integrated energy systems 

- Computational intelligence approaches for multi-energy systems

- Computational intelligence approaches for smart building/home energy management

- Computational intelligence approaches for applications in smart energy systems/ energy markets 

- Computational intelligence approaches for local energy trading with distributed energy resources 

- Computational intelligence approaches in the market-driven smart energy systems 

- Hybrid computational intelligence approaches in the market-driven smart energy systems 

- Other advanced computational intelligence approaches in the market-driven smart energy systems