Building automation is concerned with closed- and open-loop control of building services such as heating, cooling, ventilation and air conditioning, lighting and shading. The ultimate goal is to reduce energy consumption while providing comfort for the occupants. However, ensuring human comfort is a complex affair. In case of dissatisfaction, users need to inform the building operators about apparently badly adjusted setpoints. Then, service units of the facility management have to manually analyze how to improve the situation. Due to the complex characteristics of human perception and derived feedback, this can become a troublesome and time-consuming task. This paper describes the main results of our investigations to improve occupant comfort in office buildings using environmental information monitored by a wsn and human perception collected from a feedback tool. A joint information base aligned with static data from building information modeling integrates the information gathered. Reasoning on these data sources allows adjustments of the bas to automatically enhance the tenant’s comfort or suggest necessary adjustments for facility managers. Communication between the different system components is handled via mqtt. A real-world field study shows the potential of the developed approach, proves its feasibility, and demonstrates the functionality of the feedback tool.

Digitalization and concepts such as digital twins (DT) are expected to have huge potential to improve efficiency in industry, in particular, in the energy sector. Although the number and maturity of DT concepts is increasing, there is still no standardized framework available for the implementation of DTs for industrial energy systems (IES). On the one hand, most proposals focus on the conceptual side of components and leave most implementation details unaddressed. Specific implementations, on the other hand, rarely follow recognized reference architectures and standards. Furthermore, most related work on DTs is done in manufacturing, which differs from DTs in energy systems in various aspects, regarding, for example, multiple time-scales, strong nonlinearities and uncertainties. In the present work, we identify the most important requirements for DTs of IES. We propose a DT platform based on the five-dimensional DT modeling concept with a low level of abstraction that is tailored to the identified requirements. We address current technical implementation barriers and provide practical solutions for them. Our work should pave the way to standardized DT platforms and the efficient encapsulation of DT service engineering by domain experts. Thus, DTs could be easy to implement in various IES-related use cases, host any desired models and services, and help get the most out of the individual applications. This ultimately helps bridge the interdisciplinary gap between the latest research on DTs in the domain of computer science and industrial automation and the actual implementation and value creation in the traditional energy sector.

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