Beiträge in Tagungsbänden:

U. Habib, G. Zucker, M. Blöchle, F. Judex, J. Haase:
"Outliers detection method using clustering in buildings data";
in: "IECON 2015 - 41st Annual Conference of the IEEE 2015", herausgegeben von: IEEE; Eigenverlag, Yokohama, Japan, 2015, ISBN: 978-1-4799-1762-4, Paper-Nr. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7378180, 7 S.

To achieve energy efficiency in buildings, a lot of
raw data is recorded, during the operation of buildings. This
recorded raw data is further used for the analysis of the
performance of buildings and its different components e.g.
Heating, Ventilation and Air-Conditioning (HVAC). To save time
and energy it is required to ensure resilience of the data by
detecting and replacing outliers (i.e. data samples that are not
plausible) in the data before detailed analysis. This paper
discusses the steps involved for detecting outliers in the data
obtained from absorption chiller using their On/Off state
information. It also proposes a method for automatic detection of
On/Off and/or Missing Data status of the chiller. The technique
uses two layer K-Means clustering for detecting On/Off as well as
Missing Data state of the chiller. After automatic detection of the
chiller On/Off cycle, a method for outlier detection is proposed
using Z-Score normalization based on the On/Off cycle state of
chillers and clustering outliers by Expectation Maximization
clustering algorithm. Moreover, the results of filling the missing
values with regression and linear interpolation for short and long
periods are elaborated. All proposed methods are applied to real
building data and the results are discussed.

adsorption chillers; physical rules; K-Means Clustering Algorithm; Outliers; Z-Score Normalization; Expectation Maximization Clustering Algorithm (EM); Heating, Ventilation and Air-Conditioning(HVAC); Fault detection and diagnosis (FDD);

Erstellt aus der Publikationsdatenbank des AIT Austrian Institute of Technology.