Abstract
Distributed sensor networks are at the heart of smart buildings, providing greater detail and valuable insights into their energy consumption patterns. The problem is particularly complex for older buildings retrofitted with Building Energy Management Systems (BEMS) where extracting useful knowledge from large sensor data streams without full understanding of the underlying system variables is challenging. This paper presents an application of Q-Analysis, a computationally simple topological approach for summarizing large sensor data sets and revealing useful relationships between different variables. Q-Analysis can be used to extract novel structural features called Q-vectors. The Q-vector magnitude visualizations are shown to be very effective in providing insights on macro behaviors, i.e., building floor behaviors in the present case, which are not evident from the use of unsupervised learning algorithms applied on individual terminal units. It has been shown that the building floors exhibited distinct behaviors that are dependent on the set-point distribution, but independent of the time and season of the year.
Highlights
The rise of Internet of Things (IoT) has promoted a new class of applications that can help improve process control as well as enable intelligent decision making
The methodology for application of Q-Analysis to Building Energy Management Systems (BEMS) data is divided into the following steps: Step 1
Q-Analysis provided a means for summarizing global behavior—extracting data from a set of sensors and describing the data in a simpler manner
Summary
The rise of Internet of Things (IoT) has promoted a new class of applications that can help improve process control as well as enable intelligent decision making. The ability to add sensors capable of monitoring activity in complex systems with real-time reporting via the Internet presents many opportunities for effective automated control without the need for human involvement. In many situations, such monitoring adds a timeliness and finer data granularity allowing improvements in well-understood situations [1]. Energy Management Systems (BEMS) are a good example of this class of large scale, distributed sensor based IoT applications. This paper presents the first application of a topological approach called
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