Multi-Agent Solutions for the Development of Self-Organised and Self-Adapted Distributed Energy Generation Systems. The project aims to develop a self-organised multi-agent framework for modelling Marco-Smart Grid (SMG), dynamic coordination mechanisms between SMGs in distributed energy systems, and self-adaptation approaches for SMGs and restoration strategies to detect and recover an SMG network from faults and outages. The significance of this project lies in its promise to solve the challeng ....Multi-Agent Solutions for the Development of Self-Organised and Self-Adapted Distributed Energy Generation Systems. The project aims to develop a self-organised multi-agent framework for modelling Marco-Smart Grid (SMG), dynamic coordination mechanisms between SMGs in distributed energy systems, and self-adaptation approaches for SMGs and restoration strategies to detect and recover an SMG network from faults and outages. The significance of this project lies in its promise to solve the challenging issues of Smart Grid (SG) in multi-agent research and provide practical solutions to the development of effective and higher-quality distributed energy-generation systems with renewable energy resources. The expected outcomes are a framework, models, mechanisms and approaches in SG research and their practical applications.Read moreRead less
Quantification, optimisation, and application of deep uncertainty. This project aims to develop a framework for deep uncertainty quantification. There is currently a fundamental gap between deep learning research and the methods required to quantify and manage uncertainties. The research will propose a novel distribution-free methodology to generate deep predictive uncertainty estimates to avoid the assumptions of existing methods. The quality of estimates will be enhanced by applying an interva ....Quantification, optimisation, and application of deep uncertainty. This project aims to develop a framework for deep uncertainty quantification. There is currently a fundamental gap between deep learning research and the methods required to quantify and manage uncertainties. The research will propose a novel distribution-free methodology to generate deep predictive uncertainty estimates to avoid the assumptions of existing methods. The quality of estimates will be enhanced by applying an interval-based adversarial training step. The project is expected to help data-driven Australian organisations and industries to better quantify and manage forecasting uncertainties. This project will provide them with significant cost savings through better decision making and more robust planning.Read moreRead less
Advanced analytics utilising conjoint mining of data and content with applications in business, bio-medicine and electrical power systems. This project will provide techniques that enable effective analysis of unstructured content and related information from relational databases in a conjoint manner. These techniques will be applied in the business, bio-medicine and electrical power systems domains.