Abstract
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.
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 12776-12794 |
| Seitenumfang | 19 |
| Fachzeitschrift | Energies |
| Volume | 8 |
| Publikationsstatus | Veröffentlicht - 2015 |
Research Field
- Ehemaliges Research Field - Energy
Schlagwörter
- z-score normalization; adsorption chillers; first principle
- data sanitation workflow; machine learning; k-means clustering; outlier detection;
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
