Monash University has leading capability to develop the optimisation and visualisation technology that can help people make better and more informed decisions. This can enable decision makers in large and small organisations to improve the quality and efficiency of their services.
Another key area in this space is around probability distribution modelling, which utilises historical data patterns to develop reliable forecasting models. Machine-learning takes these approaches to the next-level by incorporating these within self-learning models, which adapt to the data being presented. These research efforts provide the engine for the Data Science Centre.
Simulation and visualisation of these data in 2D or 3D environments provide an ability to better understand and interact with the data. These capabilities provide the backbone for the Cave2 immersive visualisation and sensiLab platforms. Specific to energy, Monash is currently applying these broad approaches towards developing aggregation models of electricity networks with integrated mobile apps, geospatially predicting battery viability for residential households, forecasting electricity demand for AEMO and electrification of rural communities in Indonesia.