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.@en

Buildings need to be carefully operated and maintained for optimum health, comfort, energy performance, and utility costs. The increasing use of Machine Learning combined with Big Data in the building services sector has shown the potential to bring energy efficiency and cost-effectiveness. Therefore, upskilling and reskilling the current workforce is required to realize new possibilities. In addition, sharing and preserving knowledge are also required for the sustainable growth of professionals and companies. This formed the basis for the Dutch Research Council funded TransAct project. To increase access to education on the job, online learning is experiencing phenomenal growth. A study was conducted with two focus groups - professionals of a building service company and university researchers - to understand the existing challenges and the ways to improve knowledge sharing and upskilling through learning on the job. This study introduced an Enterprise Social Network platform that connects members and may facilitate knowledge sharing. As a community forum, Yammer from office 365 was used. For hosting project files, a SharePoint page was created. For online courses, the company’s online learning site was utilized. The log data from the online tools were analysed, semi-structured interviews and webinars were conducted and feedback was collected with google forms. Incentive models like social recognition and innovative project results were used to motivate the professionals for online activities. This paper distinguishes the impacts of initiatives on the behaviour of university researchers vs company employees.@en

Automated energy performance diagnosis systems are seldom applied in practice, leading to excessive energy use and poor indoor environment. One reason for this is that HVAC and energy performance diagnosis systems are designed separately by different experts. Current frameworks for energy performance diagnosis are not consistent with (or based on) HVAC schematic diagrams, as used by HVAC designers to design and operate these systems. We propose a generic reference architecture that decreases the gap between the design of systems for HVAC and energy performance diagnosis. The detection of symptoms and the diagnosis of faults finds place separately. In the first stage, symptom detection, generic symptoms are used and the complete list of possible symptoms is generated during the construction of the HVAC diagrams and their control systems. In the second stage, the diagnosis itself, possible faults are identified by listing all components, controls and models in the HVAC diagram. Symptoms and faults are then connected to each other in a Diagnostic Bayesian Network (DBN) that estimates automatically the fault probabilities leading to the observed symptoms. The diagnose takes place simultaneously through all levels of the system. A case study with actual measurements shows the capabilities of the reference architecture.

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