TY - GEN
T1 - Planning and Monitoring of Building Energy Demands under Uncertainties by Using IoT Data
AU - Chang, Soowon
AU - Castro-Lacouture, Daniel
AU - Matsui, Kanae
AU - Yamagata, Yoshiki
N1 - Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2019
Y1 - 2019
N2 - Building energy models have under predicted actual electricity consumption by about 30%. These models are typically based on physical attributes of an actual building and simulate its operation at static indoor conditions. The attributes of building designs and systems may not represent accurate properties of the building because the data becomes available only as architectural and engineering design progresses. The lack of building data details creates uncertainties in the model. Internet of things (IoT) data enable us to obtain reliable building operational data. This research addresses a major challenge for using IoT data during a building energy prediction and operation. IoT data currently pose two major challenges: 1) IoT data has been generated in existing buildings, but it is often unused, and 2) uncertainties in building energy predictions have resulted in unreliability of the simulation results. The research objective is to present a methodology of IoT-initiated building energy modeling and monitoring to increase the reliability of energy predictions and energy management. The uncertainty-integrated energy modeling enables for reliable energy planning prior to building construction, and for resilient monitoring during post construction and facility operation.
AB - Building energy models have under predicted actual electricity consumption by about 30%. These models are typically based on physical attributes of an actual building and simulate its operation at static indoor conditions. The attributes of building designs and systems may not represent accurate properties of the building because the data becomes available only as architectural and engineering design progresses. The lack of building data details creates uncertainties in the model. Internet of things (IoT) data enable us to obtain reliable building operational data. This research addresses a major challenge for using IoT data during a building energy prediction and operation. IoT data currently pose two major challenges: 1) IoT data has been generated in existing buildings, but it is often unused, and 2) uncertainties in building energy predictions have resulted in unreliability of the simulation results. The research objective is to present a methodology of IoT-initiated building energy modeling and monitoring to increase the reliability of energy predictions and energy management. The uncertainty-integrated energy modeling enables for reliable energy planning prior to building construction, and for resilient monitoring during post construction and facility operation.
UR - http://www.scopus.com/inward/record.url?scp=85068763152&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068763152&partnerID=8YFLogxK
U2 - 10.1061/9780784482445.027
DO - 10.1061/9780784482445.027
M3 - Conference contribution
AN - SCOPUS:85068763152
T3 - Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
SP - 211
EP - 218
BT - Computing in Civil Engineering 2019
A2 - Cho, Yong K.
A2 - Leite, Fernanda
A2 - Behzadan, Amir
A2 - Wang, Chao
PB - American Society of Civil Engineers (ASCE)
T2 - ASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019
Y2 - 17 June 2019 through 19 June 2019
ER -