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. In this project, a self-adaptive AI tool will be developed to provide scientific support and investigate the optimal energy demand solution that prioritizes human and equity concerns.
The project will make predictions and build human behaviour scenarios for energy demand reduction that underpins well-being and sustainable living in intersectional socio-demographic groups. 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, thus providing suggestions for actions in complex situations with multiple objectives.
Methods/analysis: forecasting and scenario building, machine learning and deep learning models, multi-objective optimisation models.
- What will future sustainable living look like with consideration of energy demand reduction?
- How to design and deploy energy demand reduction solutions to maximise equity and wellbeing?
- How could AI help to address the diversity and complexity in various communities?