Sensor deployment configurations for building energy consumption prediction

Abstract

Sensor-based data-driven building energy consumption prediction could play an important role in designing and operating energy-efficient buildings. However, existing approaches rely on rigid sensor networks to collect indoor physical parameter data, and the impact of sensor locations on the prediction performance is often poorly understood. To address this gap, this paper aims to study the impact of sensor deployment configurations on building energy consumption prediction, where a configuration is defined in terms of the number and locations of sensors and their flexibility (i.e., fixed or can change over time). Forty-eight (48) configurations were defined and tested. In identifying the sensor locations of the configurations, three clustering approaches were tested. Indoor physical parameter data were collected from an office building using sensors placed at different locations to test these configurations. For each configuration, four feature combinations were also defined to test how well the collected data from the sensor configurations could predict the hourly building energy consumption of three end uses (total, HVAC, and both lighting and receptacles) – alone and in combination with other data (e.g., outdoor weather data). The results showed that the sensor configurations could impact the prediction performance [measured by coefficient of variation (CV)] by 35–76%. The findings from this work could help define sensor deployment configurations for enhanced building energy consumption prediction.