Sensor impact evaluation in commercial buildings: The case of occupancy-centric controls

Abstract

Occupancy-Centric Controls (OCC) are specialized control sequences which modulate building operation as a function of the sensed occupancy status, thereby leading to increased energy savings, while maintaining thermal comfort. However, occupancy sensor nonidealities, such as bias, latency, random noise, or misdetection, can degrade the performance of OCCs. In this paper, we perform a systematic simulation-based investigation to understand the impacts of nonidealities from both occupancy counting and presence sensors on OCCs. We use a prototype medium office EnergyPlus-based building model, with a detailed zoning layout, as our evaluation testbed. Subsequently, we evaluate the effectiveness of rule-based, occupancy-centric HVAC, lighting and equipment control strategies on an annualized basis. Furthermore, we systematically characterize occupancy sensor nonidealities. Our results indicate that for counting sensors, although the effects of random noise on energy consumption and thermal comfort are minimal, sensor bias and latency can increase HVAC and whole building energy consumption by up to 3.3 % and 4.9 %, respectively. Additionally, noting that an enumeration approach to understand the sensor impact evaluation map for all considered nonidealities may be resource- and time-intensive, we propose a Bayesian Optimization (BO)-based smart sampling approach to efficiently identify the most impactful sensor nonideality sets. Our results indicate that the proposed BO-based approach is able to increase the computational efficiency by 60-fold, when compared to an exhaustive grid-search (enumeration) method. For presence sensors, we observed that higher false positive rates could have a direct impact on HVAC and whole building energy consumption. A low-grade presence sensor could lead to the increase of HVAC and whole building energy consumption by 12.5 % and 8.1 % on average.