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Title: Leveraging Fine-Grained Occupancy Estimation Patterns for Effective HVAC Control

Conference · · 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)

As occupancy sensing technologies become mature, various occupancy sensors are increasingly deployed in commercial buildings for pervasive occupancy monitoring. These sensors provide occupant-count data, which contains rich spatiotemporal information about occupancy patterns. With long-term occupant-count data collected from a commercial building, we design three different predictive models that capture the occupancy dynamics and examine how a model predictive control of the HVAC system benefits from actual occupancy count prediction. Our analysis reveals that mispredictions of occupancy states, especially false positives and false negatives, may introduce inefficient control that leads to energy waste or user discomfort. To address this issue, we take a step further to design an adaptive model predictive controller that minimizes inefficient control actions according to misprediction types and distributions. A comprehensive evaluation is performed in OpenBuild and EnergyPlus simulators to study the effectiveness of the proposed end-to-end control strategy. The evaluation shows that the proposed solution reduces energy consumption by 29.5% while improving the average weighted occupants comfort by 86.7% in Predicted Mean Vote (PMV) over the fixed schedule strategy.

Research Organization:
Stony Brook Univ., NY (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
DOE Contract Number:
EE0007682
OSTI ID:
1811647
Journal Information:
2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI), Conference: In 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)
Country of Publication:
United States
Language:
English

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