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Title: A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses

Abstract

Within the residential building sector, the air-conditioning (AC) load is the main target for peak load shifting and reduction since it is the largest contributor to peak demand. By leveraging its power flexibility, residential AC is a good candidate to provide building demand response and peak load shifting. For realization of accurate and reliable control of AC loads, a building thermal model, which characterizes the properties of a building’s envelope and its thermal mass, is an essential component for accurate indoor temperature or cooling/heating demand prediction. Building thermal models include two types: “Forward” and “Data-Driven”. Due to time-saving and cost-effective characteristics, different data-driven models have been developed in a number of research studies. However, few developed models can predict temperatures in respective zones of a multiple-zone building with an open air path between zones e.g., an open stairwell connecting two floors of a home. In this research, a novel hybrid modeling approach is proposed to predict the average indoor air temperatures of both the upstairs and downstairs. This “hybrid” solution integrates both gray-box, i.e. RC model and black-box models. A developed RC model is used to predict the building mean temperature, and black-box model, in which the supervised machine learningmore » algorithms are leveraged, is used to predict the temperature difference between the downstairs and upstairs. Compared with the measured data from a real house, the results obtained have acceptable/satisfactory accuracy. The method proposed in this study integrates the advantages of black-box and gray-box modeling. As a result, it can be used as a reliable alternative to predict the average temperatures in respective floors of typical detached two-story houses.« less

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1];  [3]; ORCiD logo [4]; ORCiD logo [1]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Shenzhen Univ., Shenzhen (China)
  3. China Univ. of Petroleum (East China), Qingdao (China)
  4. The Hong Kong Polytechnic Univ., Kowloon (Hong Kong)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1484127
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 236; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Building demand management; Data-driven model; Supervised machine learning; Particle swarm optimization

Citation Formats

Cui, Borui, Fan, Cheng, Munk, Jeffrey D., Mao, Ning, Xiao, Fu, Dong, Jin, and Kuruganti, Teja. A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses. United States: N. p., 2018. Web. doi:10.1016/j.apenergy.2018.11.077.
Cui, Borui, Fan, Cheng, Munk, Jeffrey D., Mao, Ning, Xiao, Fu, Dong, Jin, & Kuruganti, Teja. A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses. United States. doi:10.1016/j.apenergy.2018.11.077.
Cui, Borui, Fan, Cheng, Munk, Jeffrey D., Mao, Ning, Xiao, Fu, Dong, Jin, and Kuruganti, Teja. Thu . "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses". United States. doi:10.1016/j.apenergy.2018.11.077.
@article{osti_1484127,
title = {A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses},
author = {Cui, Borui and Fan, Cheng and Munk, Jeffrey D. and Mao, Ning and Xiao, Fu and Dong, Jin and Kuruganti, Teja},
abstractNote = {Within the residential building sector, the air-conditioning (AC) load is the main target for peak load shifting and reduction since it is the largest contributor to peak demand. By leveraging its power flexibility, residential AC is a good candidate to provide building demand response and peak load shifting. For realization of accurate and reliable control of AC loads, a building thermal model, which characterizes the properties of a building’s envelope and its thermal mass, is an essential component for accurate indoor temperature or cooling/heating demand prediction. Building thermal models include two types: “Forward” and “Data-Driven”. Due to time-saving and cost-effective characteristics, different data-driven models have been developed in a number of research studies. However, few developed models can predict temperatures in respective zones of a multiple-zone building with an open air path between zones e.g., an open stairwell connecting two floors of a home. In this research, a novel hybrid modeling approach is proposed to predict the average indoor air temperatures of both the upstairs and downstairs. This “hybrid” solution integrates both gray-box, i.e. RC model and black-box models. A developed RC model is used to predict the building mean temperature, and black-box model, in which the supervised machine learning algorithms are leveraged, is used to predict the temperature difference between the downstairs and upstairs. Compared with the measured data from a real house, the results obtained have acceptable/satisfactory accuracy. The method proposed in this study integrates the advantages of black-box and gray-box modeling. As a result, it can be used as a reliable alternative to predict the average temperatures in respective floors of typical detached two-story houses.},
doi = {10.1016/j.apenergy.2018.11.077},
journal = {Applied Energy},
number = C,
volume = 236,
place = {United States},
year = {Thu Nov 29 00:00:00 EST 2018},
month = {Thu Nov 29 00:00:00 EST 2018}
}

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