TY - GEN
T1 - Identification of Principal Factors in Determining Building Peak Energy Shaving Capacities during Demand Response Events
AU - Yu, Xinran
AU - Ergan, Semiha
N1 - Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2019
Y1 - 2019
N2 - In the U.S., the building sector consumes 70% of electricity and lays massive pressure on national grids. To avoid electricity blackouts, demand response programs incentivize end-consumers for reducing their electricity demand during peak hours. Therefore, it is essential for grid operators to understand the electricity shaving capacity of buildings. However, previous studies either simplify buildings as black-boxes - resulting in low accuracy in estimations, or represent buildings with detailed information - resulting in over-parameterized models. In this study, the authors provided a computational framework to identify principal factors that dictate peak shaving capacities of buildings. In total, fifteen buildings during twelve DR events were used as testbeds as validation. The results showed that the day-in-the-week and the quantity of relevant equipment are part of the principal factors behind peak capacity determination. With this framework, practitioners can represent buildings beyond black-boxes with less complexity and promising accuracy of peak shaving capacity determination.
AB - In the U.S., the building sector consumes 70% of electricity and lays massive pressure on national grids. To avoid electricity blackouts, demand response programs incentivize end-consumers for reducing their electricity demand during peak hours. Therefore, it is essential for grid operators to understand the electricity shaving capacity of buildings. However, previous studies either simplify buildings as black-boxes - resulting in low accuracy in estimations, or represent buildings with detailed information - resulting in over-parameterized models. In this study, the authors provided a computational framework to identify principal factors that dictate peak shaving capacities of buildings. In total, fifteen buildings during twelve DR events were used as testbeds as validation. The results showed that the day-in-the-week and the quantity of relevant equipment are part of the principal factors behind peak capacity determination. With this framework, practitioners can represent buildings beyond black-boxes with less complexity and promising accuracy of peak shaving capacity determination.
UR - http://www.scopus.com/inward/record.url?scp=85068767236&partnerID=8YFLogxK
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U2 - 10.1061/9780784482445.070
DO - 10.1061/9780784482445.070
M3 - Conference contribution
AN - SCOPUS:85068767236
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 - 547
EP - 554
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 -