Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in ....Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in Australia's electricity distribution network planning and operation by considering future challenges such as integrating battery storage and electric vehicles into the grid, and thus providing reliable energy. The project expects to train next generation expert workforce for Australia's future power grid.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101277
Funder
Australian Research Council
Funding Amount
$427,600.00
Summary
Temporal-Spatial Data Analytics for Stochastic Power System Stability. The modern power system is evolving towards a renewable-energy dominated, digitalized "data-intensive" system, where enormous data are measured in multiple timescales, different locations, and in diverse structures. This project will develop a novel data-driven framework for power system stability analysis. This project will deliver new knowledge about instability phenomena and mechanism of power systems with high-level renew ....Temporal-Spatial Data Analytics for Stochastic Power System Stability. The modern power system is evolving towards a renewable-energy dominated, digitalized "data-intensive" system, where enormous data are measured in multiple timescales, different locations, and in diverse structures. This project will develop a novel data-driven framework for power system stability analysis. This project will deliver new knowledge about instability phenomena and mechanism of power systems with high-level renewable energies, faster-than-real-time system instability risk detection, and rule-based stability control. These research outcomes will form the basis of an innovative theoretical foundation to guide new technologies for power utilities for stability assessment and enhancement in the digitalized era.Read moreRead less
Exploring Emerging Collective Behaviours in Large-Scale Data-Driven Networked Systems. Understanding emerging collective behaviours in large-scale data-driven networked systems and developing methodology and approach for pattern identification and intervention are very important for high impact applications such as smart energy supply using smart meters. This project will propose a new theory for the developments, which will enhance Australia's leading position in this research and provide a cut ....Exploring Emerging Collective Behaviours in Large-Scale Data-Driven Networked Systems. Understanding emerging collective behaviours in large-scale data-driven networked systems and developing methodology and approach for pattern identification and intervention are very important for high impact applications such as smart energy supply using smart meters. This project will propose a new theory for the developments, which will enhance Australia's leading position in this research and provide a cutting-edge technology for industrial applications and training of the next generation of leading researchers.Read moreRead less
Dynamics and Resilience of Complex Network Systems with Switching Topology . This project aims to develop a breakthrough methodology and new technology to analyse and integrate large-scale network systems, such as power grids, that involve large networks of components with switching connections. The project expects to create a new theoretical framework to tackle the challenges arising from switching topology resulted from switching connections, and methods to understand their behaviours and desi ....Dynamics and Resilience of Complex Network Systems with Switching Topology . This project aims to develop a breakthrough methodology and new technology to analyse and integrate large-scale network systems, such as power grids, that involve large networks of components with switching connections. The project expects to create a new theoretical framework to tackle the challenges arising from switching topology resulted from switching connections, and methods to understand their behaviours and design intervention strategies to achieve optimal outcomes. The expected outcome is a practical technology for industry applications, such as smart power grids. This should increase the reliability and resilience of the electricity networks against faults and cyber attacks.
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Discovery Early Career Researcher Award - Grant ID: DE180101268
Funder
Australian Research Council
Funding Amount
$367,446.00
Summary
Inference and resilient control of complex cyber-physical networks. This project aims to establish a fundamental framework to efficiently analyse and control critical, modern infrastructure networks such as power grids and the Internet. The project expects to bridge the gap between cyber-physical network theory and network resilience engineering through developing a body of knowledge about cyber-physical systems, security analysis and emergence of network behaviours. The project will develop des ....Inference and resilient control of complex cyber-physical networks. This project aims to establish a fundamental framework to efficiently analyse and control critical, modern infrastructure networks such as power grids and the Internet. The project expects to bridge the gap between cyber-physical network theory and network resilience engineering through developing a body of knowledge about cyber-physical systems, security analysis and emergence of network behaviours. The project will develop design methodologies to improve the resilience of these networks against internal faults and external attacks. This should improve the robustness and invulnerability of Australian power grids and the Internet against random failures and malicious cyber-physical attacks.Read moreRead less
Novel data mining techniques for complex network analysis and control. This project will develop novel data mining theories and algorithms to analyse complex networks for safe information publishing and sharing across networks. It will enable smart information use in bioinformatics, social science and business intelligence, help protect against cybercrime and promote Australia's international research profile.
Discovery Early Career Researcher Award - Grant ID: DE210100274
Funder
Australian Research Council
Funding Amount
$415,675.00
Summary
Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes wil ....Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes will advance big spatiotemporal data analytics and nonlinear optimisation theory for solving decision-making tasks towards a future energy system. This should promote the Australian power industry transition to a sustainable future grid based on a digitalisation approach to efficient energy management against climate changes.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL110100281
Funder
Australian Research Council
Funding Amount
$2,777,066.00
Summary
Large-scale statistical machine learning. This research program aims to develop the science behind statistical decision problems as varied as web retrieval, genomic data analysis and financial portfolio optimisation. Advances will have a very significant practical impact in the many areas of science and technology that need to make sense of large, complex data streams.
Algorithms for collaborative micro-navigation based on spatio-temporal data management and data mining. Traffic congestion coupled with greenhouse gas emissions is a major challenge for modern society. This project will tackle this challenge by developing computer-assisted smart vehicles that can access and exchange real-time information about traffic conditions, leading to improved driving experience, safety and environmental sustainability.