Publications and deliverables
Deliverable 2.1
Title: | Data preparation and data description report |
Due date: | 2017-03-31 |
Executive summary:
The aim of this document is to describe the entire process from data import, over descriptive data analytics to candidate action generation. In order to give an overview, the main results are summarized in this deliverable.
Based on the requirements and the system specification specified in work package 1, the interfaces to relevant external data sources were defined. These are an OPC UA and SQL-based monitoring data interface, a Web service-based weather data interface, and the semi-automatic semantic data interface. Subsequently, the infrastructure to store the data within the system was developed. This includes an OWL ontology for semantic information about the building and its automation systems, and an SQL database for monitoring and weather data. Monitoring data from the building automation system requires some pre-processing before it can be used for data analytics. Therefore, methods to remove data gaps, misalignments, and out of range values were implemented.
The descriptive data analytics module was developed to detect faults in the building automation system and to identify optimization potential via performance evaluation. Concrete use cases were defined for both tasks in order to support the specification of workflows and the implementation. Fault detection and performance evaluation rely on the same algorithms. An XML-based input format was specified for the description of how a specific fault or optimization potential can be detected. Together with monitoring data and semantic information from the internal databases, this information is used as input for the descriptive data analytics algorithms. An important goal for the implementation was that fault and optimization potential descriptions are not necessarily building-specific. They can be defined in a way such that they can also be used for other similar buildings with small or no adaptations. The results of the algorithms are then written to the semantic database.
In the following, the results of the performance evaluation were used for the generation of candidate actions. A candidate action is the adaptation of a single value within the configuration of a building automation system. For each identified optimization potential, all possibly suitable measures to use the potential, and therefore to increase the efficiency of the building, are fetched from the semantic database. Finally, all actions are combined to sets of candidate actions.
Furthermore, weather parameters that can be used for data analysis and simulation were defined. Also the forecasting models for sun radiation and cloud cover were improved as part of the work package.