Rojicek, J; Fisera, R; Lilis, G; Kontes, GD; Giannakis, G; Rovas, DV; (2013) Functional and technological definition of BIM-aware services to assess, predict and optimize energy performance of buildings. In: Mahdavi, Ardeshir and Gawin, Dariusz and Cerny, Robert and Grunewald, John and Matiasovsky, Peter, (eds.) Proceedings of the 2nd Central European Symposium on Building Physics (CESBP). (pp. pp. 43-50). OKK Editions: Vienna, Austria.

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Abstract

There are a number of important elements in designing of building energy management systems – when data collection, aggregation and management is usually well addressed by existing building management systems, actual analytical components allowing to diagnose energy-prone and/or user comfort compromising behaviors are far less mature. It is not only about developing algorithms for such tools, but also proper design of a hosting platform and its viability – it should not only enable access to sensor readings, but also provide access to other building data like Building Information Models and allow collaboration and interconnection of such analytics. BaaS project calls such tools Assess, Predict and Optimize services. Developing a smart platform supporting these services naturally leads to a concept of the building as a service ecosystem (BaaS) where any new tool can be plugged in the system and can benefit from already existing components. In the present work, the high level architecture of the BaaS platform is presented and the ability of BaaS system to act as a platform enabling the building contextual data as well as dynamic data (sensor readings) to software modules is demonstrated through a use-case example on a simple one zone office building.

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