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.

References:

# https://doi.org/10.1016/j.scs.2021.103445

# https://doi.org/10.1016/j.egyai.2020.100043

# https://doi.org/10.1016/j.jobe.2020.101692

# https://doi.org/10.1016/j.rser.2021.111530