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Energies 2015, 8(11), 12776-12794; doi:10.3390/en81112337

Sanitation and Analysis of Operation Data in Energy Systems

AIT Austrian Institute of Technology, Giefinggasse 2, Vienna 1210, Austria
Omnetric GmbH, Ruthnergasse 3, Vienna 1210, Austria
Author to whom correspondence should be addressed.
Academic Editor: Chi-Ming Lai
Received: 3 September 2015 / Revised: 24 October 2015 / Accepted: 3 November 2015 / Published: 11 November 2015
(This article belongs to the Special Issue PCM Applications in Building Energy)
View Full-Text   |   Download PDF [6248 KB, uploaded 11 November 2015]   |  


We present a workflow for data sanitation and analysis of operation data with the goal of increasing energy efficiency and reliability in the operation of building-related energy systems. The workflow makes use of machine learning algorithms and innovative visualizations. The environment, in which monitoring data for energy systems are created, requires low configuration effort for data analysis. Therefore the focus lies on methods that operate automatically and require little or no configuration. As a result a generic workflow is created that is applicable to various energy-related time series data; it starts with data accessibility, followed by automated detection of duty cycles where applicable. The detection of outliers in the data and the sanitation of gaps ensure that the data quality is sufficient for an analysis by domain experts, in our case the analysis of system energy efficiency. To prove the feasibility of the approach, the sanitation and analysis workflow is implemented and applied to the recorded data of a solar driven adsorption chiller. View Full-Text
Keywords: data sanitation workflow; machine learning; k-means clustering; outlier detection; z‑score normalization; adsorption chillers; first principle data sanitation workflow; machine learning; k-means clustering; outlier detection; z‑score normalization; adsorption chillers; first principle

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zucker, G.; Habib, U.; Blöchle, M.; Judex, F.; Leber, T. Sanitation and Analysis of Operation Data in Energy Systems. Energies 2015, 8, 12776-12794.

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