CoSy seminar: "Data assimilation in resilient energy systems within statistical, machine learning computing"

Everyone is welcome and the first 40 people to register will be treated to a free lunch sandwich. If you do not want lunch, you are still welcome to join.

Register for a lunch sandwich (deadline: Sunday 31 March)

Abstract: Data assimilation is a discipline that aims to optimally fuse numerical/theoretical models of processes with sparse and inaccurate data, irregularly distributed in space and time to infer the evolving state of the system being modelled. Some of the physical and artificial processes in the energy/power systems can be the atmosphere, ocean, power system, electricity market, energy critical power supply systems etc. while the data of these processes can be wind speed and water speed, radiation, voltage, electricity price, navigation data, neuronal data etc. Data assimilation serves to achieve the balance between the complexity of the model and available data to reduce both the complexity of the model and the data to achieve better accuracy. This serves different goals such as state estimation, parameter estimation, improving initial conditions, prediction, filtering, smoothing, global sensitivity and control. An introduction to data assimilation methods (Machine Learning equivalents) with its application examples in critical power systems will be presented. The implications within the context of energy/power system resilience, energy essential systems, and how they are utilized in computational instances within emerging computational approaches, will be discussed in relation to the applications.

This is a lecture in the seminar series held by CIM (Centre for Interdisciplinary Mathematics).