Mining building metadata by data stream comparison

Improving at scale the energy performance of buildings requires that applications are portable among buildings (i.e. the same application in two different buildings). One challenge in enabling portable applications is metadata about building instrumentation. The problem is that there are multiple ways to annotate sensor and actuation points. This makes it difficult to create intuitive queries for retrieving data streams from points. Another problem is the amount of insufficient or missing metadata. We introduce Metafier, a tool for extracting metadata from comparing data streams. Metafier enables a semi-automatic labeling of metadata to building instrumentation. Metafier annotates points with metadata by comparing the data from a set of validated points with unvalidated points. Metafier has three different algorithms to compare points with based on their data. The three algorithms are Dynamic Time Warping (DTW), Empirical Mode Decomposition (EMD), and the differential coefficient. Two of the algorithms compare the slope of the data stream in the values. EMD finds similarities based on the frequency bands among the data stream. By using several algorithms the system is robust enough to handle data streams with only slightly similar patterns. We have evaluated Metafier with points and data from one building located in Denmark. We have evaluated Metafier with 903 points, and the overall accuracy, with only 3 known examples, was 94.71%. Furthermore we found that using DTW for mining points with the point type of room temperature achieved an accuracy as high as 98.13%.