Title webinar

Real-time monitoring of Building Energy Systems: Bayesian Network-based Fault Detection and Diagnosis of an Air-handling Unit in a Dutch university Building

Description

A significant part of worldwide energy consumption is used to provide a comfortable climate inside buildings. This energy is mostly used by heating, ventilation and air-conditioning (HVAC) systems. A part of this energy is currently wasted due to faults, such as incorrect control signals or component failures. In the field of HVAC fault detection and diagnosis (FDD), methods are developed that aim to detect that a fault is present in the system, and consequently diagnose which fault this is. However, most of the methods that are currently being developed are data-driven, which require large amounts of labelled data. In practice, this data often is not available, leading to a lack of adoption of FDD methods by building operators. Additionally, most of the FDD approaches proposed in the literature have not been tested in real-time, instead validation has been performed on already existing datasets.

In his MSc thesis, Lars van Koetsveld van Ankeren proposed a FDD method to diagnose part of the HVAC system, air-handling units (AHUs), in real-time. Air-handling units are an important part of the HVAC system, responsible for a significant part of the energy consumption. The method applies the four symptoms three faults (4S3F) framework, to construct a diagnostic Bayesian network (DBN) without relying on historical data for symptom detection or fault diagnosis. The DBN was implemented in Python to diagnose a case study AHU in a Dutch university building.

In this webinar, the diagnosis model is, as well as the results of applying the model to the case study data.

Speakers:  Lars Koetsveld van Ankeren  (TU Delft) - Master student/researcher at the Faulty of Architecture and the Built Environment at TU Delft.