Data assimilation in renewable energy industry and power systems

  • Date: –10:00
  • Location: Polacksbacken Mässen, 6140, ITC (MAP)
  • Lecturer: Bahri Uzunoğlu
  • Organiser: Bahri Uzunoğlu
  • Contact person: Bahri Uzunoğlu
  • Phone: 734606585
  • Docentföreläsning

The Department of Engineering Sciences hereby invite all interested to a docent lecture in subject of engineering science with specialization in science of electricity /teknisk fysik med inriktning mot elektricitetslära.

Data assimilation is a discipline that aims to optimally fuse numerical/theoretical model of processes with sparse and inaccurate data, irregularly distributed in space and time to infer the evolving state of the system being modeled. Some of the physical and artificial processes in renewable energy industry and power systems can be atmosphere, ocean, power system, electricity market, etc. while the data of these processes can be wind and water velocity, radiation, voltage, electricity price, etc. Data assimilation can be distinguished from other types of machine learning, statistical methods and  image analysis  since it mainly employs a dynamical model of the system being analysed. In this framework of  dynamical models, there is an ever-increasing need for improved accuracy, which leads to models of high complexity. 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, improving initial conditions, prediction, filtering, smoothing and control. An introduction to data assimilation methods with its successful application examples in renewable energy industry and power systems will be presented.