Graph Neural Networks for Efficient Decision-making towards Future Grids

该项目旨在通过整合可解释的图神经网络和高效的计算方法，为以决策为中心的学习开发一个突破性的框架。它希望创建新的图表示学习方法，以利用时空知识解锁数据洞察力，同时建立新的加速优化理论，以加速以决策为中心的学习模型。预期成果将推动大时空数据分析和非线性优化理论，以解决未来能源系统的决策任务。这将促进澳大利亚电力行业向基于数字化方法的可持续未来电网过渡，以应对气候变化的有效能源管理。

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.