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Beiträge in Tagungsbänden:

G. Zucker, J. Malinao, U. Habib, T. Leber, A. Preisler, F. Judex:
"Improving energy efficiency of buildings using data mining technologies";
in: "Proceedings of the 23rd IEEE International Symposium on Industrial Electronics (ISIE 2014)", herausgegeben von: IEEE Conference Publications; Eigenverlag, Germany, 2014, S. 2664 - 2669.



Kurzfassung:
Building automation systems record operation data
including physical values, system states and operation conditions.
This data is stored, but commonly not automatically evaluated.
This historic data is the key to efficient operation and to quick
recognition of errors and inefficiencies, a potential that is not
exploited today. Instead, today the evaluation during operation
delivers only alarming in case of system failures. Analysis is
commonly done by the facility manager, who uses his experience
to interpret data. Methods from data mining and data analysis
can contribute to a better understanding of building operation
and provide the necessary information to optimize operation,
especially in the area of Heating, Ventilation and Air Conditioning
(HVAC) systems. Increases in energy efficiency and can be
achieved by automated data analysis and by presenting the user
energy performance indicators of all relevant HVAC components.
The authors take a first step to examine operation data of
adsorption chillers using the X-Means algorithm to automate the
detection of system states.

Erstellt aus der Publikationsdatenbank des AIT Austrian Institute of Technology.