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.
|Seiten (von - bis)||12776-12794|
|Publikationsstatus||Veröffentlicht - 2015|
- Ehemaliges Research Field - Energy
- z-score normalization; adsorption chillers; first principle
- data sanitation workflow; machine learning; k-means clustering; outlier detection;