ECPPM 2014 - Sep, 2014

Title of the Conference/Workshop: 10th European Conference on Product and Process Modelling

Location: Vienna, Austria

Dates: 17th-19th September, 2014



Brief description of the conference:

For more information of the conference, please visit the organised workshops (here)


Campus21 publications

New business models for holistic building management



Building Management is broken down into multiple categories of services. Classifications depend on the jurisdiction and industrial sectors. One commonly known categorization is the GEFMA-based model which distinguishes into technical, infrastructural and commercial building management. However, the challenges for companies working in the Facilities and Building Management market have dramatically changed with legislation such as the European Energy Performance Directive and many sub sequentially released standards. Emerging technologies for energy co-generation, advanced building control and for performance monitoring have further contributed to the state of flux in building management.  This paper explains, how novel IT-based methods and tools can be further exploited as ‘enablers’ for new Business Models which focus on offering building and energy management services holistically. The authors use a Business Modelling Framework developed by Osterwalder to allow a structured discussion of the different ‘pillars’ required to describe Business Models holistically.


Performance indicators to evaluate buildings' systems' performance



Given the increasing complexity and variety of modern building services systems, alternative methodologies are required to benchmark and compare these systems beyond the traditional energy or CO2 consumed per building area. This paper describes the methodology developed and used in the CAMPUS21 project to determine buildings’ and systems’ performance and communicate advanced building benchmarks from continuous monitoring on a room/zone level, building level and campus/neighbourhood level.


Why and how to assess the quality of building performance data



Over the past decade buildings have become much more complex technical artefacts due to the fact that numerous energy co-generation and storage capabilities can be integrated into buildings. The integrated, complementing operation of generation, storage, and building services systems is not a trivial task. Numerous data streams need to be analysed to ensure that the operation of the aforementioned systems contributes to overall energy savings and CO2-reductions without undermining the tenants’ comfort and wellbeing in those buildings. One could assume that through the emergence of wireless monitoring systems an improved knowledge base can be quickly and efficiently build-up to support the decisions which must be made by building automation systems or systems’ operators. However, the pure availability of additional data compiled from sensors, meters, access control systems and local weather stations can worsen the situation in case the compiled data is incomplete or of insufficient quality. Therefore, this paper discusses the question of why and how to assess the quality of Building Performance Data. 


Generation and evaluation of embedded probabilistic occupancy models for predictive building systems control



Knowledge of occupants' presence and behavior in buildings and associated predictive models are of central importance to the implementation efforts concerning predictive building systems control strategies. Specifically, prediction of occupants' presence in office buildings represents a necessary condition for predicting their interactions with building systems. Implementation of occupancy prediction models in existing buildings can benefit from available occupancy monitoring data. Actual occupancy data can be compared with model predictions on an ongoing basis, thus improving model reliability. In the present contribution, we examine various options to process occupancy monitoring data toward developing probabilistic occupancy models. These options can be described in terms of a number of related questions: What temporal horizon of past occupancy information (days, weeks, months) shall be considered for model development? Would it be advantageous to differentially treat individual week days? Shall model identification occur in fixed intervals or in terms of dynamically receding horizons? To explore these questions on an empirical basis, we selected a university campus office area, which is equipped with a monitoring infrastructure and includes a number of open and closed offices. For this case study, the above mentioned options for occupancy presence prediction were implemented and compared with actual occupancy information. The results facilitate a discussion of the potential and limitations of probabilistic occupancy models intended for incorporation in the control logic of existing buildings. 


Development and evaluation of models for the computation of sky radiance and luminance distribution



There exist a number of sky radiance and luminance distribution models. Some of these models have been integrated in both weather data repositories and libraries of simulation applications. However, our past research implies that the existing models still fall short of meeting expectations. A comparison of a number of existing models for prevailing sky conditions in Vienna, Austria did not produce convincing evidence of the accuracy of these models. In the present study, we used a rich data set of high-resolution measurements of sky radiance (using the sky scanner data for 2011 obtained from monitoring station of the Building Physics and Building Ecology Department of the Vienna University of Technology, Austria) to explore the potential for models that would support the generation of detailed sky radiance and luminance models based on standard weather station data. Thereby, we focused on clear sky conditions. The derived empirically-based model could be shown to perform reasonably well. However, sky radiance prediction errors increasing patch altitudes. The paper concludes with discussion of the results and potential directions of future research.