Demo Abstract: BEAD - Building Energy Asset Discovery Tool for Automating Smart Building Analytics Joern Ploennigs, Bernard Gorman, Niall Brady, Anika Schumann IBM Research Smarter Cities Technology Centre Dublin, Ireland
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Abstract The demo presents the IBM Building Energy Asset Discovery Tool (BEAD). The tool semi-automates the discovery of relevant assets and sensors for energy management in large sets of building data. Application to real-world examples demonstrate how easy it is to extract the semantic information for the configuration of an energy management system as well as for generating building specific diagnostic models.
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Introduction
Buildings nowadays can provide large amounts of data. Unfortunately the data sets are often badly structured and labelled as they are commissioned by different people without any specific labelling standard. Identification of relevant assets and their data points for energy management is therefore often a manual process that is not supported by tools. This increases not only the installation costs of energy management systems. The additional integration effort also keeps potential customers with legacy systems from installing energy management systems. In result, industry sees the labelling problem as a major stepping stone for novel analytic approaches in buildings [2, 3]. The demo showcases IBM’s Building Energy Asset Discovery Tool (BEAD). It semi-automates the labelling process and allows for automatically solving smart building analytic problems such as energy management and diagnosis. We show on a real-world example how a user can quickly label an unstructured data set in a few minutes to retrieve a configuration for an energy management software. In extension, we demonstrate how we use reasoning to compute from the extracted semantic labels a detailed model of the cause-effect relationships between sensors that can be used to diagnose anomalies in buildings.
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2 Tool Demo 2.1 Labelling of Data Points We demo our tool for IBM’s Technology Campus Dublin that consists of 6 buildings. We have a tabular data point list given that contains all textual labels extracted from the Building Management System (BMS) with a total of 9,820 data points. We determine possible matches between those data points and relevant Energy Management System (EMS) marker sets as described in [6]. Based on this rating we ask the user a series of questions to confirm or reject the automatically computed potential matches.
Figure 1. Label Mapping Fig. 1 shows the wizard user interface for this step. The window depicts on the top the overall process in labelling the data set. For the Dublin dataset it starts at 94 %, which represents the percentage of data points that have been identified with a very high confidence. Below this progress bar, a list of BMS data points and their potential EMS label matches is shown. It uses colour coding to support the user in distinguishing already assigned labels in green from unassigned labels in red. Below this the individual questions are presented to the user with the computed match. In this case ”Ch 01 CHWT” is correctly matched to a Chilled Water Leaving Temperature Sensor. According to the answer of the user, we either accept the semantic match or reject it and compute the next question. The Dublin campus dataset with 9,820 data points is fully labelled after 40 questions. It takes about 5
minutes for an experienced user to answer them. This is a huge difference in comparison to a former manual labelling process that took an building expert 3 days. The user can proceed to the next step whenever he is satisfied with the overall labelling progress.
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Location and Asset Discovery
The tool extracts in the next step the location and asset hierarchy from the data set to structure the semantic labels. Experiments with different datasets have shown that locations and assets are often labelled using dataset specific identifiers such as the individual building names. As these site specific identifiers cannot be automatically identified, we allow the user to add these identifiers manually to the set of predefined ones.
erage of detectable and diagnosable behaviour than the classic rule-based energy management software [4]. The diagnosis graph computed for the given Dublin campus covers 2,411 sensors, with 1,446 diagnosable anomalies resulting from 47,284 potential causes. The demo shows how the diagnosis graph is created and how easy relationships can be added using semantic web technologies.
Figure 3. Diagnosis result for a high room temperature created from the semantic model Fig. 3 shows one diagnosis result for a high room temperature that traces back the causes to an inactive cooling, a high number of occupants, and a high outside temperature, while the latter one has the highest probability. The individual causes occur in different combinations and increase the internal energy in the room with the result of the high temperature.
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Figure 2. Location and Asset Discovery Fig. 2 shows the wizard window for this step. It lists on the left hand side the site and asset types relevant for the data set. The location types range for this example from the site to the zone level of each building. The identified assets are air handling units (AHU), fan coil units, boilers, chillers, lights, and blinds. The middle column in Fig. 2 allows to define additional identifiers for each type. The extracted site and asset structure is shown on the right. The assets and their data points are assigned to the corresponding site. Fig. 2 shows the heat exchanger and AHUs for the example data set.
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BEAD as Enabler for Analytics
The discovered assets and data points can be directly used to configure the IBM TRIRIGA Environmental and Energy Management Software [1]. It is a rule-based energy management software that allows to detect common energy waste scenarios in buildings. For example, from the dataset of 9,820 sensors a subset of 194 relevant sensors is identified primarily around the main building assets such as the AHUs, boilers, and chillers. The tool configures 300 energy management rules for this sensor set. The extracted semantic model is an extension of the Semantic Sensor Network Ontology [5] and can be used for more than just configuring energy management software. We use reasoning to automatically extract cause-effect relationships between sensors which in turn allow for the automated diagnosis of smart building problems. It leads to higher cov-
Summary
The demo presents the BEAD tool that semi-automates the discovery of relevant assets and sensors in data sets to simplify the deployment of energy management software in buildings. It allows labelling even large datasets with several thousand data points within a short time. From the resulting EMS labels with their location and asset structure we are able to automatically configure the IBM TRIRIGA Environmental and Energy Management Software. Beyond that, the semantic representation of labels allows to use semantic web technologies for automating the creation of diagnosis models within buildings that detect and diagnose significantly more anomalies in buildings [4]. The demo demonstrates the usage of the tool for the IBM Technology Campus, Dublin.
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References
[1] N. Brady, F. Lecue, A. Schumann, and O. Verscheure. Configuring building energy management systems using knowledge encoded in building management system points lists, 2012. US20140163750 A1. [2] J. F. Butler and R. Veelenturf. Point naming standards. ASHRAE Journal, B16-B20, Nov. 2010. [3] W. Livingood, J. Stein, T. Considine, and C. Sloup. Review of current data exchange practices: Providing descriptive data to assist with building operations decisions. Technical Report NREL/TP-5500-50073, National Renewable Energy Laboratory, 2011. [4] J. Ploennigs, A. Schumann, and F. Lecue. Adapting semantic sensor networks for smart building diagnosis. In ISWC - Int. Semantic Web Conf., 2014. [5] J. Ploennigs, A. Schumann, and F. Lecue. Extending semantic sensor networks for automatically tackling smart building problems. In ECAI/PAIS - Eu. Conf. on Artificial Intelligence - Prestigious Applications of Intelligent Systems, 2014. [6] A. Schumann, J. Ploennigs, and B. Gorman. Towards automating the deployment of energy saving approaches in buildings. In BuildSys, 2014.
