Scalable Causal Learning for Predicting Adverse Events in Smart Buildings

Emerging smart buildings, such as the NASA Sustainability Base (SB), have a broad range of energyrelated systems, including systems for heating and cooling. While the innovative technologies found in SB and similar smart buildings have the potential to increase the usage of renewable energy, they also add substantial technical complexity. Consequently, managing a smart building can be a challenge compared to managing a traditional building, sometimes leading to adverse events including unintended thermal discomfort of occupants (too hot or too cold). Fortunately, todays smart buildings are typically equipped with thousands of sensors, controlled by Building Automation Systems (BASs). However, manually monitoring a BAS time series data stream with thousands of values may lead to information overload for the people managing a smart building. We present here a novel technique, Scalable Causal Learning (SCL), that integrates dimensionality reduction and Bayesian network structure learning techniques. SCL solves two problems associated with the naive application of dimensionality reduction and causal machine learning techniques to BAS time series data: (i) using autoregressive methods for causal learning can lead to induction of spurious causes and (ii) inducing a causal graph from BAS sensor data using existing graph structure learning algorithms may not scale to large data sets. Our novel SCL method addresses both of these problems. We test SCL using time series data from the SB BAS, comparing it with a causal graph learning technique, the PC algorithm. The causal variables identified by SCL are effective in predicting adverse events, namely abnormally low room temperatures, in a conference room in SB. Specifically, the SCL method performs better than the PC algorithm in terms of false alarm rate, missed detection rate and detection time. Introduction NASA Ames Sustainability Base1 (SB) is a green building that provides a research testbed for different sustainable technologies and concepts. The SB is designed with a Net Zero Energy objective. Detailed monitoring of the BAS is Copyright c © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. http://www.nasa. gov/ames/facilities/sustainabilitybase required at regular intervals. SB is instrumented with 2636 sensors, which perform physical or logical measurements. One major area of consumption is the building heating and cooling system. From Jan 2014 to May 2014 many alarms were acknowledged from sensors specific to the heating system. An alarm is initiated whenever a sensor value goes beyond the desired range, indicating an anomalous behavior of the heating system. Some alarms, e.g room temperature going outside the predefined range, cause occupants’ discomfort and others can lead to short-term or long-term damage to the system. In order to eliminate these alarms, it is crucial to identify the causes. An attempt via human inspection is time consuming and can take several weeks or months. As the sensor data captures enough information about the system, an alternative for cause-detection is a data driven approach which does not require intervening with the BAS of SB building. Causality is mainly described by two methods: counterfactuals or causal graphs (Pearl 2000). Here we primarily consider the second one. A causal graph is based on the principle that each variable is independent of its non-effects, conditional on its direct causes. PC, FCI, RFCI and GES are commonly used algorithms to learn causal graphs from data (Spirtes, Glymour, and Scheines 2000; Chickering 2003; Colombo et al. 2012). Bayesian networks have also been shown to convey causal interpretation (Pearl, Verma, and others 1991). A Bayesian network (BN) is a directed acyclic graph (DAG) which represents a family of distributions over its nodes. The structure of a BN represents the conditional independence relations among the nodes. Hence learning the independence relations by conditional independence tests is a main approach in learning the structure of a Bayesian network from data (Margaritis and Thrun 1999). Another structure learning approach is to perform a search over directed graphs based on a score function (Heckerman, Geiger, and Chickering 1995). Hybrid techniques have also been developed to learn BN structure (Tsamardinos, Brown, and Aliferis 2006). However identifying the causal Bayesian network structure only from observations is challenging because it is possible to model one distribution by two distinct directed graphs which have different causal meaning of the variables (Ellis and Wong 2008). In this work we propose a novel amalgamation (Figure  x x . x  Dimension reduction using autoregressive model Bayesian network structure learning Isolating candidate causes Adverse event prediction Missed detection rate False alarm rate