Fault detection and diagnosis (FDD) are crucial to improving the efficiency of heating, ventilation, and air conditioning (HVAC) systems, reducing energy waste, and maintaining indoor comfort. Diagnostic Bayesian Networks (DBNs) present a compelling approach, offering robustness to uncertainty, adaptability to different sensor configurations, and interpretable inference. Existing FDD studies for air handling units (AHUs), however, are often limited to simulation or laboratory settings, seldom consider AHUs with heat recovery wheel (HRW) in operation, and rarely analyze how diagnostic performance changes under diverse sensor configurations. This study defined three practical sensor configurations (Sensor-Rich, Standard, and Limited) based on international guidelines and a practical survey, developed a corresponding DBN framework, and evaluated its performance on seventeen common faults using real-world data from an AHU in a Dutch office building. Existing FDD studies are often limited to simulation or specific Air Handling Unit (AHU) types with fixed sensor configurations, rarely investigating AHUs with heat recovery wheels, which are common in Europe. This study addresses these gaps by first defining three sensor configurations (Sensor-Rich, Standard, and Limited) based on international guidelines and a practitioner survey. A DBN-based FDD model was then developed for these configurations using historical data, expert knowledge and subsequently evaluated for its ability to diagnose seventeen common faults in an operational AHU with heat recovery wheel.The DBN correctly diagnosed fifteen, nine, and four faults for these configurations, respectively. The results show that increasing sensor availability improves overall diagnostic performance. However, certain cases demonstrate that additional measurements can also introduce conflicting evidence and reduce diagnostic accuracy. The study suggests that sensor selection must be combined with effective DBN modeling strategies to achieve robust diagnosis. Taken together, the analysis of key sensors and DBN modeling practices provides practical guidance for designing and implementing DBN-based FDD in common European AHU systems under diverse sensor configurations.The results indicate that increasing sensor quantity alone does not improve FDD performance; strategic sensor selection, placement, and effective data processing are also crucial.

Buildings account for approximately 40% of energy consumption in the European Union and over one-third of energy-related greenhouse gas emissions, with a significant portion attributed to heating, ventilation, and air conditioning (HVAC) systems. Effective fault detection and diagnosis (FDD) are essential for reducing energy waste and lowering maintenance costs in HVAC operations. FDD methods for HVAC systems have been extensively studied and can be broadly classified into two categories: knowledge-based and data-driven approaches. Knowledge-based approaches heavily rely on predefined rules and domain expertise and remain the most widely used in existing HVAC systems. Over the past decade, data-driven FDD approaches have gained popularity. However, data-driven FDD approaches require highquality labelled fault datasets for model training, which can be time-consuming and costly to obtain. To address this challenge, various studies have explored the use of generative adversarial networks (GANs) and other data augmentation techniques to synthesize realistic fault data and improve model performance. Despite these advancements, challenges related to generalization, scalability, and the interpretability of black-box models remain key concerns in the adoption of data-driven FDD approaches. [...]

New data science technologies used in building management systems (BMS) bring not only many technical challenges but also raise very significant educational challenges for professionals who work in the field of energy management systems in the energy transition. As part of the Brains4Buildings project, we have developed an Open Knowledge Platform that aims to support professionals and researchers who want to know more about the use of BMS data to optimize the operation of HVAC systems. This paper introduces the platform, its key features and content, and serves as an open invitation to wider community to make use of it.

Fault detection and diagnosis (FDD) are essential for enhancing the performance of heating, ventilation, and air conditioning (HVAC) systems, preventing energy waste, and ensuring indoor comfort. However, popular data-driven FDD approaches encounter challenges, such as the lack of high-quality labeled data, poor generalization, and the black-box nature of the models, which hinder their adoption in the market. Moreover, most existing studies only developed and validated their FDD models in simulation environments or laboratory settings, overlooking practical challenges of operational HVAC systems. First, this study focuses on implementing Diagnosis Bayesian networks (DBNs) in real-world settings, specifically, air handling units with heat recovery wheels in a campus building in the Netherlands. DBNs have been proven to be promising solutions with advantages in interpretability, robustness to uncertainties, and flexibility. Second, a Kafka-based framework is introduced for real-time monitoring in HVAC systems, enabling continuous and scalable data processing. Third, a comprehensive diagnosis analysis is conducted using both historical operational data and experimental fault data. The results reveal significant discrepancies between design documents and the actual operation of the HVAC system, and the DBN successfully identifies eight out of nine injected faults during experimentation. Additionally, the results uncover issues such as false positives due to DBN's limitations, inherent system faults, and unexpected HVAC system behaviors triggered by the simulated faults, offering critical insights into the operational challenges and diagnostic potential of DBNs in real-world HVAC systems. These contributions can advance the practical deployment of interpretable and robust FDD tools in building energy systems.

This study investigates the integration of green hydrogen into building energy systems using local solar power, with the electricity grid serving as a backup plan. A comprehensive bottom-up analysis compares six energy system configurations: the natural gas grid boiler system, all-electric heat pump system, natural gas and hydrogen blended system, hydrogen microgrid boiler system, cogeneration hydrogen fuel cell system, and hybrid hydrogen heat pump system. Energy efficiency evaluations were conducted for 25 homes within one block in a neighborhood across five typological house stocks located in Stoke-on-Trent, UK. This research was modeled using a spreadsheet-based approach. The results highlight that while the all-electric heat pump system still demonstrates the highest energy efficiency with the lowest consumption, the hybrid hydrogen heat pump system emerges as the most efficient hydrogen-based solution. Further optimization, through the implementation of a peak-shaving strategy, shows promise in enhancing system performance. In this approach, hybrid hydrogen serves as a heating source during peak demand hours (evenings and cold seasons), complemented by a solar energy powered heat pump during summer and daytime. An hourly operational configuration is recommended to ensure consistent performance and sustainability. This study focuses on energy performance, excluding cost-effectiveness analysis. Therefore, the cost of the energy is not taken into consideration, requiring further development for future research in these areas.

Many buildings suffer from operational inefficiencies, leading to uncomfortable indoor environments, poor air quality, and significant energy waste. Developing automatic fault detection and diagnosis (FDD) tools in building energy systems is essential to mitigate these issues, reducing both energy waste and maintenance costs. Diagnostic Bayesian networks (DBNs), as probabilistic graphical models, offer a promising solution due to their interpretability, robustness to uncertainty, scalability, and flexibility. In this paper, the practical applications of DBNs for FDD in building energy systems are comprehensively reviewed. The generic modeling procedure is systematically examined and summarized, covering problem formulation, structure modeling, parameter modeling, and fault isolation and evaluation. Then, the paper provides insights into DBN modeling objectives, modeling types, diagnostic samples, and modeling software based on the 43 key relevant papers. Furthermore, the paper discusses practical challenges such as sensor configuration, baseline estimation, threshold determination, and expert knowledge integration. Finally, the recommendations are provided to guide further research, aiming to enhance DBN implementation for building energy systems in real-world scenarios, thereby supporting the transformation of the building service industry into a smart sector and ultimately improving building energy performance.

Modern building systems generate vast sensor data for monitoring and control, yet faults in sensors, controls and documentation often undermine performance. Using Diagnostic Bayesian Networks (DBN)1, this study demonstrates whole-building fault detection and diagnosis (FDD) in a Dutch office and explores how occupant feedback can complement unreliable sensor data for resilient building operation.

Digitalization of HVAC piping and instrumentation diagrams (P&IDs) is essential for advancing the intelligent transformation of building systems and the building services industry. This work explores Large Language Models (LLMs) for zero-shot P&ID digitization, focusing on symbol detection. Three LLM-assisted approaches are evaluated. The results show that directly applying LLMs to P&ID digitization is highly challenging. By segmenting P&IDs into local crops and pairing them with the full diagram annotated with bounding boxes to provide global context, the LLM achieves improved symbol recognition. The findings highlight both the promise of LLMs and the need for further refinement to enable reliable HVAC P&ID digitization.

This paper presents a Diagnostic Bayesian Network (DBN) for whole-building fault detection and diagnosis (FDD) incorporating occupant feedback as potential symptoms of faulty operation and occupant behaviors as potential faults in building performance. The methodology is applied on a seven-floor office building in Delft, the Netherlands, and the DBN's fault isolation capabilities for three different levels of information are compared.

Fault detection and diagnosis (FDD) provides several interrelated benefits, including reducing energy waste, enhanced operational efficiency, and maintaining indoor comfort. The initial step in FDD is to detect deviations from normal or expected operation. However, establishing a reliable baseline can be challenging, especially when there is a lack of sufficient system documents or when complex control strategies are involved. This study investigates three feature selection methods for the baseline estimation: expert knowledge-based, correlation-based, and causality-guided, using heating coil valve control estimation as an example. These methods were tested in an office building in the Netherlands. The results show that while the correlation-based method achieved the best estimation, it may lead to false negatives due to features with reverse causality. This study aims to emphasize the necessity of causal analysis in the baseline estimation to achieve reliable FDD in buildings.

Automated fault detection and diagnostics (FDD) can support building energy performance and predictive maintenance by leveraging the vast amounts of data generated by modern building management systems. Diagnostic Bayesian Networks (DBN) offer a particularly promising approach due to their robustness, flexibility and scalability. However, FDD applications in whole building systems are rare, as they require the integration of different building subsystems, with their own potential faults and symptoms, which increases complexity and makes the resulting DBNs system-specific. In order to overcome these limitations, the 4S3F (four symptoms and three faults) method offers a simplified, adaptable framework for FDD implementation across building systems. In this paper, we implement the 4S3F methodology to a whole-building HVAC system in a case study office building located in the Netherlands. Our methodology uses generic, aggregated representations of individual subsystems within the building, such that FDD methods for specific subcomponents can later be incorporated where available. We first define aggregated building system groups (boiler group, chiller group, hydronic groups, ventilation groups, and end user groups) and subsequently define generic faults that can be detected with the existing sensor infrastructure. This simplified system representation is then used to define a DBN to isolate the most probable system-level faults that lead to building-level symptoms. By focusing on the whole building system, this work aims to provide the groundwork to incorporate occupant feedback and behavior in FDD.

This study investigates the diagnostic capabilities of a Diagnostic Bayesian Network (DBN) for air handling unit (AHU) components, particularly focusing on the heat recovery wheel (HRW) and heating coil valve (HCV). Unlike data-driven methods relying heavily on high-quality labeled data, this knowledge-based DBN is more suitable for real-world applications, where labeled faulty and normal data are hard to obtain. Notably, existing studies predominantly concentrate on developing DBN for AHU with recirculated air, neglecting thorough investigations into AHU with HRW, a prevalent system in North Europe and increasingly recommended post-COVID-19 for mitigating viral propagation. This paper presents a DBN setup with expert knowledge for an AHU with HRW, which is evaluated using experimental data from an office building in the Netherlands. The results show that the proposed DBN can successfully diagnose typical faults in HRW and HCV.

Energy waste in buildings can range from 5% to 30% due to faults and inadequate controls. To effectively mitigate energy waste and reduce maintenance costs, the development of Fault Detection and Diagnosis (FDD) algorithms for building energy systems is crucial. Diagnostic Bayesian Networks (DBNs), as graphical probability models, are particularly useful in scenarios where high-quality data is not always available. While many studies have focused on single fault detection using DBNs, the occurrence of multiple simultaneous faults is common, yet the versatility of DBNs in handling such cases is rarely explored. This study adapts a DBN, initially designed for single fault diagnosis, to perform simultaneous fault diagnosis Experiments were conducted on an air handling unit (AHU) in the Netherlands, using implemented simultaneous faults to test the model. The results suggest that the DBN can detect both single and multiple faults effectively.

The energy management systems industry in the built environment is currently an important topic. Buildings use about 40% of the total global energy worldwide. Therefore, the energy management system’s sector is one of the most influential sectors to realize changes and transformation of energy use. New data science technologies used in building energy management systems might not only bring many technical challenges, but also they raise significant educational challenges for professionals who work in the field of energy management systems. Learning and educational issues are mainly due to the transformation of professional practices and networks, emerging technologies, and a big shift in how people work, communicate, and share their knowledge across the professional and academic sectors. In this study, we have investigated three different companies active in the building services sector to identify the main motivation and barriers to knowledge adoption, transfer, and exchange between different professionals in the energy management sector and explore the technologies that have been used in this field using the boundary-crossing framework. The results of our study show the importance of understanding professional learning networks in the building services sector. Additionally, the role of learning culture, incentive structure, and technologies behind the educational system of each organization are explained. Boundary-crossing helps to analyze the barriers and challenges in the educational setting and how new educational technologies can be embedded. Based on our results, future studies with a bigger sample and deeper analysis of technologies are needed to have a better understanding of current educational problems.

The HVAC sector is essential to realize the energy transition and is facing numerous challenges like educating enough HVAC engineers to carry out the task and being able to integrate knowledge from the construction, energy, IT and health sectors and to cope with rapid technological changes. The availability of structured and easy-to-follow courses on HVAC and energy systems for buildings at higher education level could help to motivate (future) engineers to contribute to the HVAC sector, and to understand how challenging and high-tech it is. Such a course program would ideally also bring a basic understanding of the field to architects and building engineers, in such a way that a better common ground is created for collaboration and integrated design. It would also be useful to Machine Learning and Artificial Intelligence experts joining the HVAC sector. Last but not least, it could help bridging the gap between engineering and policy making, by here too, offering common views on primary energy, resource depletion and CO2 emissions relating to HVAC systems. The paper describes the structure and content of such an on-line course program. It was developed based on years of teaching experience with international master students of Mechanical Engineering, Civil Engineering, Architecture, Technical Management and Policy, Electrical Engineering and with professionals from housing associations, ministries and municipalities. The choices for the program structure, based on systems engineering, are underpinned and explained, as well as the choices for specific contents. Additionally, experience with the development of self-assessment tools for students, and self-paced courses is shared, as well as the feed-back from students. A first version of the course program was tested on the edX platform with more than 5000 students participating in each module and is publicly available.

In practice, automated energy performance fault diagnosis systems are seldom installed in HVAC systems. The main reason is that a specific Fault Detection and Diagnosis (FDD) setup is time-consuming and expensive because the existing methods are component-specific, not aligned with HVAC design practices, and not fully automated. 4S3F (four symptoms three faults) method, based on system engineering and Diagnostic Bayesian Networks (DBN), was proposed to decrease the gap between the design of HVAC systems for buildings and energy performance diagnosis, and proofs of concepts were tested on diverse parts of the HVAC system of one specific building. In order to test the further applicability potential of the method, it is necessary to expand these tests and to study possible problems arising in practice, like the lack of sensors installed in a specific system or practical difficulties in the construction of the 4S3F Bayesian network by HVAC or control. However, due to the small number of validations carried out on the environment, parameters, and installation process of this method still need further discussion and refinements. In this paper, we investigate how to construct the DBN for the quite generic AHU (Air Handling Unit) of a, with mechanical supply and exhaust, heating and cooling coils, and heat recovery. The paper describes the possible DBN's depending on the technical design and the measurement points. The diverse Bayesians networks are compared, and it is concluded that also, with a limited number of sensors, a diagnostic network can be set up. It is also concluded that step-by-step instructions would be needed to facilitate the work of HVAC engineers when setting up the diagnosis model.

In practice, faults in building installations are seldom noticed because automated systems to diagnose such faults are not common use, despite many proposed methods: they are cumbersome to apply and not matching the way of thinking of HVAC engineers. Additionally, fault diagnosis and energy performance diagnosis are seldom combined, while energy wastage is mostly a consequence of component, sensors or control faults. In this paper new advances on the 4S3F diagnose framework for automated diagnostic of energy waste in HVAC systems are presented. The architecture of HVAC systems can be derived from a process and instrumentation diagram (P&ID) usually set up by HVAC designers. The paper demonstrates how all possible faults and symptoms can be extracted on a very structured way from the P&ID, and classified in 4 types of symptoms (deviations from balance equations, operational states, energy performances or additional information) and 3 types of faults (component, control and model faults). Symptoms and faults are related to each other through Diagnostic Bayesian Networks (DBNs) which work as an expert system. During operation of the HVAC system the data from the BMS is converted to symptoms, which are fed to the DBN. The DBN analyses the symptoms and determines the probability of faults. Generic indicators are proposed for the 4 types of symptoms. Standard DBN models for common components, controls and models are developed and it is demonstrated how to combine them in order to represent the complete HVAC system. Both the symptom and the fault identification parts are tested on historical BMS data of an ATES system including heat pump, boiler, solar panels, and hydronic systems. The energy savings resulting from fault corrections are estimated and amount 25%. Finally, the 4S3F method is extended to hard and soft sensor faults. Sensors are the core of any FDD system and any control system. Automated diagnostic of sensor faults is therefore essential. By considering hard sensors as components and soft sensors as models, they can be integrated into the 4S3F method.

Low Temperature Heating (LTH) of buildings is a key feature when switching to renewable energy. Even when the capacity of LTH is high enough, LTH may adversely affect indoor thermal comfort in case buildings are not suitably insulated. This paper goes deeper into methodological issues when conducting a thermal comfort assessment. Thermal comfort is either quantified by Fanger’s Predicted Mean Vote (PMV) or ranked in building comfort classes in the adaptive model. In both cases, one of the main parameters influencing comfort is the Mean Radiant Temperature (MRT). This study addresses issues with common MRT and PMV calculations in energy simulation software. The case study is TRNSYS 17. Several MRT and PMV calculation methods are compared, showing possible draw-backs and deviations from comfort standards NEN-EN ISO 7726 and 7730. For instance, in the standard heating settings in TRNBuild only the total heating capacity is specified. The radiative part is then distributed area-weighted over opaque surfaces. A more detailed option in TRNBuild is to specify the locations of radiative gains as points. In both cases, the MRT at a comfort sphere is calculated with Gebhardt-factors instead of view-factors. The standard settings may be considered too simplified for detailed comfort studies whereas the detailed model shows deviations from comfort standards NEN-EN ISO 7726 and 7730. Therefore, two additions to these models are proposed to increase accuracy. One addition is an ordinary detailed model with radiative gains as point sources in order to retrieve all surface temperatures during a desired period of time. In the second addition walls with radiators are split-up and planes are added at the locations of radiators to generate a view-factor matrix. This can be done in TRNBuild, but also in other view-factor calculation software. From model 1 all surface temperatures are retrieved. Combined with the view-factors from model 2, the MRT can be calculated.

In this review article, our main goal is understanding the Networked Learnings used for professional development. Networked learning can be defined as a form of learning where information and communication technology (ICT) can be used to promote connections between learners and their peers, learners and tutors and learners and learning resources. Such networks play an important role in professional development of employees in different sectors, from high tech industries to traditional businesses, and in both formal teaching and educational programs and informal learning activities. In this review, we explore how networked learning contexts, domains, and levels of scale are practiced and reported in the academic literature. And finally, we will investigate support technologies that have been used to facilitate networked learning for professional development.

Learning and educational challenges in the field of indoor climate and building services like energy systems are mainly due to the transformation of professional practices and learning networks, a big shift in the way in which people work, communicate, and share their knowledge and the need for additional workforce, either juniors or coming from other disciplines. One of the most important factors that highly influence professional development and workplace learning is networked learning. Our goal in this study, is understanding the learning networks characteristics and patterns of interaction using Social Network Analysis techniques in three MOOCs discussion forums. The result of this study shows not only the importance of Learning networks and peer support on professionalization of learners, but also how pedagogical approach of instructors in MOOCs can foster learning networks. This novel approach in developing learning networks and communities is not only able to help connect young professionals and experienced practitioners digitally, but also it can promote professional development and innovation in the energy installation sector.