Automated data labeling of building automation systems using time series data and conditional probabilities

As energy efficiency demands increase, buildings get smarter, and the amount of data to analyze grows, where each building device may generate multiple data streams. These extensive quantities of monitoring data serve as a great opportunity for detecting anomalies in building automation systems and for optimizing their control. However, each building usually uses a custom format for data labels, therefore requiring an individual data label analysis per building. This makes the conceptually manageable task of detecting energy systems from the raw data increasingly complex and error-prone, which is a further hurdle that any building operation optimizer must resolve. This paper presents a methodology for automatically categorizing and labeling raw monitoring data from building automation systems. Using statistical features of the data, the method checks which data streams follow which known building operation rules and patterns. Therefore, an initial labeling of the data streams takes place. Furthermore, examining the correlation between the data streams indicates possible related system components using the concept of conditional probability. As a use case for the methodology, unlabeled data from a real building automation system are examined. The results show that, using unlabeled time series, data types from certain sensors and actuators can be reliably identified. The proposed methodology could therefore simplify the implementation of energy applications such as operation optimization and fault detection of building automation systems