C.J. Lu
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Real-time Monitoring of Building Energy Systems
Bayesian Network-based Fault Detection and Diagnosis of an Air-handling Unit in a Dutch University Building
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 this thesis, an FDD method is proposed 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.
This DBN is implemented in Python to diagnose a case study AHU in a Dutch university building.
The method was validated with fault experiments, which were diagnosed with diagnosis periods ranging from one 10-minute sample to three hours.
All of the faults that were introduced and included in the DBN were accurately diagnosed for all diagnosis periods.
For the AHU operation data of 2022 and 2023, mostly control and temperature faults were diagnosed.
Incorrect control of the heating coil valve was found in 25\% and incorrect control of the ERW in 11\% of the days considered.
Additionally, a deviation between the measured intended supply temperature of more than 1 $\deg$ was found for 79\% of the days in the dataset.
Finally, the application of the method for real-time fault diagnosis has also been demonstrated, by collecting sensor data through the data streaming platform Apache Kafka.
This data was consequently stored locally in the time-series database InfluxDB, which could be queried for performing the diagnosis.
The processing time of performing the diagnosis was approximately ten seconds, demonstrating the potential for performing diagnoses per sample.
Recommendations for future work include extending the DBN to diagnose faults related to the cooling coil and faults occurring outside of operation hours.
Additionally, the developed diagnosis method should be applied to other AHUs, to further research the generalisability of the DBN.
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 this thesis, an FDD method is proposed 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.
This DBN is implemented in Python to diagnose a case study AHU in a Dutch university building.
The method was validated with fault experiments, which were diagnosed with diagnosis periods ranging from one 10-minute sample to three hours.
All of the faults that were introduced and included in the DBN were accurately diagnosed for all diagnosis periods.
For the AHU operation data of 2022 and 2023, mostly control and temperature faults were diagnosed.
Incorrect control of the heating coil valve was found in 25\% and incorrect control of the ERW in 11\% of the days considered.
Additionally, a deviation between the measured intended supply temperature of more than 1 $\deg$ was found for 79\% of the days in the dataset.
Finally, the application of the method for real-time fault diagnosis has also been demonstrated, by collecting sensor data through the data streaming platform Apache Kafka.
This data was consequently stored locally in the time-series database InfluxDB, which could be queried for performing the diagnosis.
The processing time of performing the diagnosis was approximately ten seconds, demonstrating the potential for performing diagnoses per sample.
Recommendations for future work include extending the DBN to diagnose faults related to the cooling coil and faults occurring outside of operation hours.
Additionally, the developed diagnosis method should be applied to other AHUs, to further research the generalisability of the DBN.
Inefficiencies in HVAC control and poorly designed glass façades can significantly impact energy demand and thermal comfort in buildings with extensive glass façades. Two potential solutions are the implementation of smart systems and retrofitting the glass façade. However, when considering these options in building design, it is often unclear which strategy is more effective. This thesis investigates the performance of smart systems using grey-box model predictive control (MPC) and the effectiveness of glass façade retrofitting strategies concerning energy savings and occupant thermal comfort. The case study for this research is an educational building with a vast glass façade. The study commenced with a review of relevant literature, followed by the development of the MPC system and an evaluation of its performance against various retrofitting strategies. The MPC system utilized a grey-box model for predictions and local optimization algorithms for control. The retrofitting strategies included the addition of films and curtains on the interior side of the glass façade. Results indicated a trade-off regarding the energy saving and thermal comfort between the two approaches. Compared to MPC, curtains did not reduce energy demand as significantly, but they provided better indoor thermal comfort. The study also found that the lumped model used in the MPC system had limitations for summer calculations and that some algorithms tended to be aggressive. In terms of retrofitting strategies, the application of curtains enhanced thermal comfort more effectively than adding films to the glass.
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Inefficiencies in HVAC control and poorly designed glass façades can significantly impact energy demand and thermal comfort in buildings with extensive glass façades. Two potential solutions are the implementation of smart systems and retrofitting the glass façade. However, when considering these options in building design, it is often unclear which strategy is more effective. This thesis investigates the performance of smart systems using grey-box model predictive control (MPC) and the effectiveness of glass façade retrofitting strategies concerning energy savings and occupant thermal comfort. The case study for this research is an educational building with a vast glass façade. The study commenced with a review of relevant literature, followed by the development of the MPC system and an evaluation of its performance against various retrofitting strategies. The MPC system utilized a grey-box model for predictions and local optimization algorithms for control. The retrofitting strategies included the addition of films and curtains on the interior side of the glass façade. Results indicated a trade-off regarding the energy saving and thermal comfort between the two approaches. Compared to MPC, curtains did not reduce energy demand as significantly, but they provided better indoor thermal comfort. The study also found that the lumped model used in the MPC system had limitations for summer calculations and that some algorithms tended to be aggressive. In terms of retrofitting strategies, the application of curtains enhanced thermal comfort more effectively than adding films to the glass.