Occupant behavior determines a large share of the energy consumption of buildings. Software applications driven by information about occupant behavior provide a mean to optimize this share. However, existing systems for sensing occupancy behavior provide technology-specific APIs statically coupled to the type of computed occupancy information. Software platforms for developing applications for buildings do also not provide abstractions for occupancy behavior. Therefore, technology lock in and lack of proper abstractions wreck the development of occupancy-driven applications. In this paper we present the design, implementation and evaluation of OccuRE, a stream-based Occupancy REasoning platform. OccuRE provides a technology agnostic API for accessing occupancy information to significantly improve portability. The platform uses a component-based computation model with dynamic composition to calculate and reason about occupancy behavior. Together these elements avoid that developers need to deal with technology-specific processing of sensor data to ease application development. Through microbenchmarks we show that OccuRE successfully and efficiently computes occupancy information for technology-heterogeneous building instrumentations. We use the development of three prototype applications to demonstrate that the API of OccuRE (i) enables several types of occupancy-driven applications, (ii) that the applications-by using the interface-achieve portability in regards to occupancy information computation and (iii) that the application code avoids handling sensor data processing.
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%.
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