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.
Highlights
Data acquisition in buildings and building-related energy systems is primarily implemented for control purposes and for subsequent manual examination of faulty operations
Data sanitation is the process of correcting the monitoring data in order to increase data quality. This includes sanitation tests to check whether the data are physically plausible and in acceptable process range as well as reconstruction of missing or implausible data. It has been shown in [7] that conventional analysis using the calculations found in norms and standards, e.g., EN 15316 yields misleading results if data quality is low
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Summary
Data acquisition in buildings and building-related energy systems is primarily implemented for control purposes and for subsequent manual examination of faulty operations. Data sanitation is the process of correcting the monitoring data in order to increase data quality This includes sanitation tests to check whether the data are physically plausible and in acceptable process range as well as reconstruction of missing or implausible data. It has been shown in [7] that conventional analysis using the calculations found in norms and standards, e.g., EN 15316 yields misleading results if data quality is low. During the use of such automated processes, it is prudent to preserve the original data: this allows one to determine the actual problems in the data which have been corrected It may be required for auditing purposes. A batch of data is the block of data rows that is imported at once and to which the process described in this paper is applied to
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