TY - GEN
T1 - Application of the Reconfigurable Environmental Intelligence Platform for HVAC Control
AU - Piadyk, Yurii
AU - Rulff, Joao
AU - Brewer, Ethan
AU - Ergan, Semiha
AU - Silva, Claudio
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/11/15
Y1 - 2023/11/15
N2 - Researchers of varying experience levels need to be able to quickly prototype and deploy sensor networks so that they can collect data and analyze their phenomena of interest as soon as possible. However, custom sensor deployments are subject to several constraints including computing resources available and types of data that are being acquired. We show how the use of the Reconfigurable Environmental Intelligence Platform (REIP) streamlines the process of multimodal environmental sensing. We offered the original sensors to a group of student researchers to monitor indoor occupancy alongside temperature and humidity in a modern office building. We demonstrate how the modular design of REIP made such a study feasible for young researchers in the context of a course project. The study resulted in findings leading to practical solutions on how air conditioning and ventilation systems can be operated more efficiently to minimize the building's energy use without affecting the comfort of its residents. It also demonstrates the potential of REIP for the rapid prototyping of multimodal sensor networks that can, in turn, enable a more data-driven approach to decision-making.
AB - Researchers of varying experience levels need to be able to quickly prototype and deploy sensor networks so that they can collect data and analyze their phenomena of interest as soon as possible. However, custom sensor deployments are subject to several constraints including computing resources available and types of data that are being acquired. We show how the use of the Reconfigurable Environmental Intelligence Platform (REIP) streamlines the process of multimodal environmental sensing. We offered the original sensors to a group of student researchers to monitor indoor occupancy alongside temperature and humidity in a modern office building. We demonstrate how the modular design of REIP made such a study feasible for young researchers in the context of a course project. The study resulted in findings leading to practical solutions on how air conditioning and ventilation systems can be operated more efficiently to minimize the building's energy use without affecting the comfort of its residents. It also demonstrates the potential of REIP for the rapid prototyping of multimodal sensor networks that can, in turn, enable a more data-driven approach to decision-making.
KW - Control systems
KW - environmental monitoring
KW - sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85179522372&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179522372&partnerID=8YFLogxK
U2 - 10.1145/3600100.3626264
DO - 10.1145/3600100.3626264
M3 - Conference contribution
AN - SCOPUS:85179522372
T3 - BuildSys 2023 - Proceedings of the10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
SP - 288
EP - 289
BT - BuildSys 2023 - Proceedings of the10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
PB - Association for Computing Machinery, Inc
T2 - 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2023
Y2 - 15 November 2023 through 16 November 2023
ER -