A Distributed Generic Data Structure for Urban Level Building Data Monitoring
Building a generic data structure that handles building realated data at an urban scale offers certain challenges. Real world entities must be captured in an environment that allows for the communication of relevent data. The associated software component
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Abstract. Building a generic data structure that handles building realated data at an urban scale offers certain challenges. Real world entities must be captured in an environment that allows for the communication of relevent data. The associated software components must be maintainable and reliable. The present contribution describes efforts to enhance a well tested building monitoring framework to handle building data at an urban scale. This requires the development of a distributed, generic and enhancable data store, as well as the conceptualization of a modular and scalable application architecture. The scalable data store is introduced, as well as the modularization process of the application logic, including data handling and communication routines. Furthermore, the concept of Virtual Datapoints and Virtual Datapoint Collections enables urban entities (for instance buildings) to communicate their status to the system in an effective way. Keywords: Urban Monitoring, Generic Data Structure, Distributed Systems.
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Introduction
Urbanization and sustainability strategies (e.g. Europe 2020) raise the need to address ecologic and economic issues on an urban level. A building is not to be recognized as a discrete entity but as part of a dynamic system, that interacts with it’s surroundings [1]. The urban environment produces massive amounts of data, not all of them related to buildings, but nevertheless influential. For instance, it is common practice to integrate weather forecasts and historical weather data into a building’s Energy Management and Control System (EMCS) as well as to utilize it as base data for light and thermal simulation [2][3]. Pang et al. introduced a framework to compare the building performance with predictive values of an EnergyPlus simulation in real time [4]. The EMCS transmits data to the simulation via a BACnet interface. The presented approach offers a promising solution for accessing EnergyPlus models in real time, but the resulting data collection is very specific and task oriented. Linawati et al. (Eds.): ICT-EurAsia 2014, LNCS 8407, pp. 86–95, 2014. c IFIP International Federation for Information Processing 2014
A Scaling Urban Data Processing Framework
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Previous research by the authors concentrated on the development of a vendor and platform independent building monitoring system [5][6]. The motivation was to develop a storage concept that handles building related data in an unified way, regardless of the initial data format or producer. Nevertheless, a majority of research projects focused on various stages of a building’s life cycle and considered buildings as singular entities. For instance, the SEMERGY project developed a concept to help remove data-related disincentives and to facilitate the integration of building performance evaluation in building design and retrofit [7]. On a lower application level, monitoring related projects elaborated a generic data structure that is used in a number of simulation applications and building data representations. 1.1
Monitoring System To
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