Automatic Classification of Sensors in Buildings: Learning from Time Series Data

Abstract

Smart buildings are generally equipped with thousands of heterogeneous sensors and control devices that impact the operation of their electrical systems. Analytical tools that aim to optimise the energy efficiency within such complex systems requires prior mapping or (classification) of diverse set of sensors according to a standard. Prior research primarily focuses on exploiting the similarities in sensor names (text metadata) to categorise them into identical classes (or groups). However, the sensors within and across buildings often follow distinct naming conventions by different vendors. In addition the definition of the classes or groups also varies significantly amongst researchers. This limits the usability and portability of prior techniques when applied across buildings. There are standard ontologies (Brick, Haystack etc.) that provide a set of standardized classes for the sensors in the buildings. The work herein follows a new avenue to address this challenging classification problem by (i) utilizing only time-series data of sensors and not text metadata, (ii) developing a simple, effective and hitherto unexplored Machine Learning (ML) model to classify the sensors into a set of standard Brick classes, and (iii) evaluating the model on a large proprietary dataset comprising of 129 buildings. Experimental results demonstrate promising performance of the presented data driven model, with average classification accuracy in terms of weighted F-score at 0.78 (±0.14), and statistically significant improvements over prior methods.