Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and...
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Autor*in:	Yu Cui [verfasserIn] Zishang Zhu [verfasserIn] Xudong Zhao [verfasserIn] Zhaomeng Li [verfasserIn] Peng Qin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	building energy model Bayesian calibration sensitive analysis automatic calibration method

Übergeordnetes Werk:	In: Buildings - MDPI AG, 2012, 12(2022), 7, p 1052
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:7, p 1052

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/buildings12071052

Katalog-ID:	DOAJ030917891

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allfields	10.3390/buildings12071052 doi (DE-627)DOAJ030917891 (DE-599)DOAJ919cdf9d8287489d85342e2d20bcc478 DE-627 ger DE-627 rakwb eng TH1-9745 Yu Cui verfasserin aut Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability. building energy model Bayesian calibration sensitive analysis automatic calibration method Building construction Zishang Zhu verfasserin aut Xudong Zhao verfasserin aut Zhaomeng Li verfasserin aut Peng Qin verfasserin aut In Buildings MDPI AG, 2012 12(2022), 7, p 1052 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:12 year:2022 number:7, p 1052 https://doi.org/10.3390/buildings12071052 kostenfrei https://doaj.org/article/919cdf9d8287489d85342e2d20bcc478 kostenfrei https://www.mdpi.com/2075-5309/12/7/1052 kostenfrei https://doaj.org/toc/2075-5309 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2022 7, p 1052
spelling	10.3390/buildings12071052 doi (DE-627)DOAJ030917891 (DE-599)DOAJ919cdf9d8287489d85342e2d20bcc478 DE-627 ger DE-627 rakwb eng TH1-9745 Yu Cui verfasserin aut Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability. building energy model Bayesian calibration sensitive analysis automatic calibration method Building construction Zishang Zhu verfasserin aut Xudong Zhao verfasserin aut Zhaomeng Li verfasserin aut Peng Qin verfasserin aut In Buildings MDPI AG, 2012 12(2022), 7, p 1052 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:12 year:2022 number:7, p 1052 https://doi.org/10.3390/buildings12071052 kostenfrei https://doaj.org/article/919cdf9d8287489d85342e2d20bcc478 kostenfrei https://www.mdpi.com/2075-5309/12/7/1052 kostenfrei https://doaj.org/toc/2075-5309 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2022 7, p 1052
allfields_unstemmed	10.3390/buildings12071052 doi (DE-627)DOAJ030917891 (DE-599)DOAJ919cdf9d8287489d85342e2d20bcc478 DE-627 ger DE-627 rakwb eng TH1-9745 Yu Cui verfasserin aut Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability. building energy model Bayesian calibration sensitive analysis automatic calibration method Building construction Zishang Zhu verfasserin aut Xudong Zhao verfasserin aut Zhaomeng Li verfasserin aut Peng Qin verfasserin aut In Buildings MDPI AG, 2012 12(2022), 7, p 1052 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:12 year:2022 number:7, p 1052 https://doi.org/10.3390/buildings12071052 kostenfrei https://doaj.org/article/919cdf9d8287489d85342e2d20bcc478 kostenfrei https://www.mdpi.com/2075-5309/12/7/1052 kostenfrei https://doaj.org/toc/2075-5309 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2022 7, p 1052
allfieldsGer	10.3390/buildings12071052 doi (DE-627)DOAJ030917891 (DE-599)DOAJ919cdf9d8287489d85342e2d20bcc478 DE-627 ger DE-627 rakwb eng TH1-9745 Yu Cui verfasserin aut Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability. building energy model Bayesian calibration sensitive analysis automatic calibration method Building construction Zishang Zhu verfasserin aut Xudong Zhao verfasserin aut Zhaomeng Li verfasserin aut Peng Qin verfasserin aut In Buildings MDPI AG, 2012 12(2022), 7, p 1052 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:12 year:2022 number:7, p 1052 https://doi.org/10.3390/buildings12071052 kostenfrei https://doaj.org/article/919cdf9d8287489d85342e2d20bcc478 kostenfrei https://www.mdpi.com/2075-5309/12/7/1052 kostenfrei https://doaj.org/toc/2075-5309 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2022 7, p 1052
allfieldsSound	10.3390/buildings12071052 doi (DE-627)DOAJ030917891 (DE-599)DOAJ919cdf9d8287489d85342e2d20bcc478 DE-627 ger DE-627 rakwb eng TH1-9745 Yu Cui verfasserin aut Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability. building energy model Bayesian calibration sensitive analysis automatic calibration method Building construction Zishang Zhu verfasserin aut Xudong Zhao verfasserin aut Zhaomeng Li verfasserin aut Peng Qin verfasserin aut In Buildings MDPI AG, 2012 12(2022), 7, p 1052 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:12 year:2022 number:7, p 1052 https://doi.org/10.3390/buildings12071052 kostenfrei https://doaj.org/article/919cdf9d8287489d85342e2d20bcc478 kostenfrei https://www.mdpi.com/2075-5309/12/7/1052 kostenfrei https://doaj.org/toc/2075-5309 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2022 7, p 1052
language	English
source	In Buildings 12(2022), 7, p 1052 volume:12 year:2022 number:7, p 1052
sourceStr	In Buildings 12(2022), 7, p 1052 volume:12 year:2022 number:7, p 1052
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	building energy model Bayesian calibration sensitive analysis automatic calibration method Building construction
isfreeaccess_bool	true
container_title	Buildings
authorswithroles_txt_mv	Yu Cui @@aut@@ Zishang Zhu @@aut@@ Xudong Zhao @@aut@@ Zhaomeng Li @@aut@@ Peng Qin @@aut@@
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callnumber-first	T - Technology
author	Yu Cui
spellingShingle	Yu Cui misc TH1-9745 misc building energy model misc Bayesian calibration misc sensitive analysis misc automatic calibration method misc Building construction Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model
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topic_title	TH1-9745 Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model building energy model Bayesian calibration sensitive analysis automatic calibration method
topic	misc TH1-9745 misc building energy model misc Bayesian calibration misc sensitive analysis misc automatic calibration method misc Building construction
topic_unstemmed	misc TH1-9745 misc building energy model misc Bayesian calibration misc sensitive analysis misc automatic calibration method misc Building construction
topic_browse	misc TH1-9745 misc building energy model misc Bayesian calibration misc sensitive analysis misc automatic calibration method misc Building construction
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title	Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model
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title_full	Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model
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author_browse	Yu Cui Zishang Zhu Xudong Zhao Zhaomeng Li Peng Qin
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title_sort	bayesian calibration for office-building heating and cooling energy prediction model
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title_auth	Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model
abstract	Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability.
abstractGer	Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability.
abstract_unstemmed	Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability.
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title_short	Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model
url	https://doi.org/10.3390/buildings12071052 https://doaj.org/article/919cdf9d8287489d85342e2d20bcc478 https://www.mdpi.com/2075-5309/12/7/1052 https://doaj.org/toc/2075-5309
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up_date	2024-07-03T17:42:49.543Z
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Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

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