Despite tremendous advances in computing and control technologies, most buildings are still relatively inefficient in their operation. Modern building management systems provide tools to manage the energy consumption of the building. However, with the increase of the amount of data and rate at which this data is collected, it becomes impossible to manually analyze the data and make appropriate decisions. Thus, an automated system that supports decision making based on data analytics algorithms can help facility managers improve building performance in a more dynamic and optimal way. Such system could be used not only to visualize energy consumption and generation data from the past, present and future by monitoring and forecasting, but also to provide feedback on how certain measures can improve the overall energy efficiency of the building. In this work the focus is on the analysis of the technical feasibility and market potential of this prescriptive analytics approach for improving the energy efficiency of buildings. For this, the technical requirements, needed inputs and effort are discussed. The market potential is analyzed based on a rough estimation of the additional investment and the potential effects of the improvements. The deployment itself requires additional investment costs (e.g. for additional equipment or modeling of the building and its systems) and only seen through the savings the return of investment period based on the assumptions is somewhere between 5 and 10 years. However, the indirect effect of the occupants´ comfort and company´s image improvement, which are difficult to measure, is another major benefit and should be a driving factor.
|Title of host publication
|Science.Research.Pannonia, Gebäuder der Zukunft?
|Number of pages
|Published - 2018
|e-nova 2018 Gebäude der Zukunft? -
Duration: 22 Nov 2018 → 23 Nov 2018
|e-nova 2018 Gebäude der Zukunft?
|22/11/18 → 23/11/18
- Digitalisation and HVAC Technologies in Buildings