Summary:

In this second module the practical application of Machine Learning (ML) techniques to adjust system behaviour in the occurrence of anomalies and faults is reviewed. This means a ML system is able to detect and adjust HVAC settings or trigger alarm events when a condition is not met. Typical examples are: (1) the use of reinforcement learning applied to monitoring of occupancy patterns and the interaction with thermostats or lighting, (2) use of weather forecast or weather anomalies to predict energy demand and adjust operational settings accordingly.

Hardware-in-the-Loop (HIL) methods for Power System Components (methods)

Lecturer: Georg Lauss, AIT

Test/simulation approaches are expanded and optimized due to the latest technologies such as real time systems, power electronics, analogous/digital measurement devices. Novel simulation techniques gain increasing importance in research and for manufacturers and for international standardisation groups.

Having in mind that 53% percent of residential household energy consumption can be attributed to the space heating, as stated by IEA, significant energy savings and load shifting could be achieved through the control of the smart thermostats and thermostatic radiator valves. Therefore, the main topic of this lecture was presenting NUIG’s approach for optimal thermostats control by utilizing white box building model.

The availability of appropriate, application-oriented standards and codes is a key prerequisite to streamline the integration of PV into the electricity grids and ensure interoperability, reliability and efficiency even under high-penetration scenarios.