Human-centred self-adaptive Ai tool for Net Zero community

Individual energy consumption behaviour heavily influences the amount of national carbon emissions, making it imperative to change consumer culture and promote low-carbon lifestyles to achieve decarbonization. This project focuses on the development of a self-adaptive AI tool to provide scientific support and investigate the optimal energy demand solution that prioritizes human and equity concerns.  

We explore how different energy demand reduction solutions affect human well-being and sustainable living in intersectional socio-demographic groups. We then move on to multi-objective optimisation model based on our work in “Maximising equity outcomes of energy demand solutions” project. 

Development of Human-centred self-adaptive AI tool for the Net Zero community 

We build future energy demand reduction scenarios, as well as their relationships with well-being, through machine learning and deep learning models. We develop self-adaptive AI tool based on self-supervised learning techniques to support the real time decision making. The self-adaptive AI algorithms used in this project can adjust their processing methods, sequences, parameters, boundary conditions, and constraints to match the unique characteristics of the data they are analysing.

Outline the future direction of the project or potential areas of impact. Highlight any research gaps: 

The team will do a review about the research landscape to  

• Create a Comprehensive database about energy demand solutions and human well-being 

• Design the framework of the self-adaptative AI tool 

Lirong organising a special issue in the journal of Energy and AI. 

The special issue is “AI for Energy Sustainability”, aims to explore cutting-edge research and applications of AI in advancing sustainable energy systems. The details could be found here: https://www.sciencedirect.com/special-issue/317840/ai-for-energy-sustainability