Talks and Poster Presentations (with Proceedings-Entry):
D. Schachinger, S. Gaida, W. Kastner, F. Petrushevski, C. Reinthaler, M. Sipetic, G. Zucker:
"An Advanced Data Analytics Framework for Energy Efficiency in Buildings";
Poster: 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2016),
- 2016-09-09; in: "Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation",
Today's buildings provide continuously growing amounts of data monitored from diverse sensors. With regard to the similarly increasing energy needs of buildings, accurate evaluation and analysis of these data can be used in order to improve energy efficiency and reduce overall energy consumption of buildings. Hence, this work introduces a comprehensive framework based on well-known data analytics methods to process the bulk of data and extract exploitable knowledge for further usage. The approach includes the descriptive evaluation and interpretation of sensed data, the prediction of future energy needs based on a set of potential actions, and the prescription of energy efficiency measures in the form of user instructions and configuration files for building automation systems. Additionally, identified use case scenarios are described, which will be used for evaluation of the presented data analytics framework. Furthermore, current results as well as ongoing work and expected future results are discussed in this work in progress.
Buildings, building automation, data processing, data analysis, energy efficiency, information management
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Project Head Wolfgang Kastner:
Advanced Data Analytics for Energy Efficiency
Created from the Publication Database of the Vienna University of Technology.