TY - GEN
T1 - Understanding the Impact of Sensing Flexibility and Strategies on HVAC Energy Consumption Modeling
AU - Bucarelli, Nidia
AU - El-Gohary, Nora
N1 - The authors would like to thank the Institute for Sustainability, Energy and Environment (iSEE) at the University of Illinois at Urbana-Champaign (UIUC). This paper is based on work supported by the Campus as a Living Laboratory program of iSEE. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of iSEE. The authors would also like to thank (1) Dr. Mohamed Attala, Dr. Ehab Kamarah, Morgan White, Karl Helmink, and Paul Foote from the Facilities and Services at UIUC for supporting the building selection process and energy sub-metering funding and installation, as well as Jerrold Buchanan for helping with the network power supply, (2) Dan Mann and Cheryl Gerber for providing access to the facility and building occupants for participating in the study, and (3) Uros Marjanovic, from Technology Services at UIUC, and Alejandro Gomez for helping with the Wi-Fi configuration and database connection of the network.
PY - 2024
Y1 - 2024
N2 - Data-driven HVAC energy consumption modeling could play an important role in operating energy-efficient buildings. However, (1) existing data-driven approaches rely on rigid sensor networks to collect indoor physical parameter data, often deployed in an ad-hoc manner or based on engineering judgment; and (2) the impact of this practice on model performance is often poorly understood. To address this gap, this paper aims to assess the impact of sensor deployment configurations on HVAC energy consumption modeling, 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). Indoor temperature and humidity data were collected from an office building. Several configurations were defined and evaluated in predicting the HVAC consumption, using an XGBoost-based model. The results showed that sensor configurations could significantly impact the performance of HVAC consumption modeling, and that periodic changes in sensor locations could improve the performance of traditional rigid methods. The findings from this work could help define improved sensor configurations for enhanced HVAC management and operation.
AB - Data-driven HVAC energy consumption modeling could play an important role in operating energy-efficient buildings. However, (1) existing data-driven approaches rely on rigid sensor networks to collect indoor physical parameter data, often deployed in an ad-hoc manner or based on engineering judgment; and (2) the impact of this practice on model performance is often poorly understood. To address this gap, this paper aims to assess the impact of sensor deployment configurations on HVAC energy consumption modeling, 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). Indoor temperature and humidity data were collected from an office building. Several configurations were defined and evaluated in predicting the HVAC consumption, using an XGBoost-based model. The results showed that sensor configurations could significantly impact the performance of HVAC consumption modeling, and that periodic changes in sensor locations could improve the performance of traditional rigid methods. The findings from this work could help define improved sensor configurations for enhanced HVAC management and operation.
UR - http://www.scopus.com/inward/record.url?scp=85184082322&partnerID=8YFLogxK
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U2 - 10.1061/9780784485248.119
DO - 10.1061/9780784485248.119
M3 - Conference contribution
AN - SCOPUS:85184082322
T3 - Computing in Civil Engineering 2023: Resilience, Safety, and Sustainability - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023
SP - 996
EP - 1004
BT - Computing in Civil Engineering 2023
A2 - Turkan, Yelda
A2 - Louis, Joseph
A2 - Leite, Fernanda
A2 - Ergan, Semiha
PB - American Society of Civil Engineers
T2 - ASCE International Conference on Computing in Civil Engineering 2023: Resilience, Safety, and Sustainability, i3CE 2023
Y2 - 25 June 2023 through 28 June 2023
ER -