Data-Driven Co-optimization of Energy Efficiency and Indoor Environmental Quality in Commercial Buildings

被引：1
|
作者：
Naqvi, S. [1 ]
Bhattacharya, S. [1 ]
Radhakrishnan, N. [1 ]
Sivaraman, C. [1 ]
Chandan, V. [2 ]
Luo, N. [3 ]
Kar, K. [4 ]
机构：
[1] Pacific Northwest Natl Lab, Richland, WA 99354 USA
[2] CrossnoKaye, Santa Barbara, CA USA
[3] Lawrence Berkeley Natl Lab, Berkeley, CA USA
[4] Rensselaer Polytech Inst Troy, Troy, NY USA
来源：
E-ENERGY '23 COMPANION-PROCEEDINGS OF THE 2023 THE 14TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS | 2023年
关键词：
data-driven learning; indoor air quality; data-sets;
D O I：
10.1145/3599733.3600262
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, we use publicly available data of a highly instrumented office building to estimate how zonal temperature and carbon dioxide (CO2) concentration are related to some key operational and environmental measurements. Subsequently, we have developed, simulated, and evaluated an optimization framework for minimizing the energy consumption of the central heating, ventilation and air conditioning (HVAC) unit while meeting zonal temperature and indoor air quality (IAQ) standards. Finally, we have evaluated the achievable energy savings for our proposed approach as compared to a baseline approach and reported significant savings potential.
引用
收藏
页码：140 / 144
页数：5
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