Advanced Modeling Techniques

U.S.-Ireland R&D Partnership: Intelligent Data Harvesting for Multi-Scale Building Stock Classification and Energy Performance Prediction

Anonymous — Mon, 28 Jun 2021 04:16:34 +0000

U.S.-Ireland R&D Partnership: Intelligent Data Harvesting for Multi-Scale Building Stock Classification and Energy Performance Prediction Anonymous (not verified) Sun, 06/27/2021 - 22:16

Sponsored by National Science Foundation

This is a 3-year international collaborative research project among University of Colorado Boulder, University College Dublin, Ireland and Ulster University in United Kingdom. Residential buildings account for 14%-27% of greenhouse gas (GHG) emissions in the three jurisdictions and cause significant negative impact on the environment. Supported by National Science Foundation in the United States, the Science Foundation Ireland in the Republic of Ireland (RoI), and the Department for the Economy in Northern Ireland (NI), this joint research aims to reduce residential building energy consumption and related GHG emissions and environmental impacts across the three jurisdictions. The research will create decision support tools to inform policy makers, planners, and other stakeholders about the most beneficial residential retrofitting solutions at multiple scales (local to national). The methodology employed will lie at the confluence of various expertise, including green engineering of the NI team, building energy modeling and machine learning of the U.S. team, and information theory of the RoI team. The aim is to transform diverse public datasets in the three jurisdictions into actionable information. Empowered by this information, the anticipation is that better decisions can guide modern societies towards transformative green solutions for the built environment that leverage sustainable engineering systems and enable the creation of energy-efficient, healthy, and comfortable buildings for a nation's citizens. The approach is cognizant of society's need to provide ecological protection while maintaining favorable economic conditions.

This joint research seeks to provide the foundational science needed to design, optimize, and deploy green engineering approaches that reduce residential building energy consumption and related GHG emissions. The interdisciplinary research targets to yield three results: 1) A methodology for data ingestion and an ontology and associated server that provides both a means of accessing and subsequently homogenizing data for both the data enrichment and the modeling processes. The intent is to enable previously unused data sources to be utilized as a whole to significantly improve the accuracy of modeling processes; 2) An advanced automated building energy model generation method powered by physics-informed machine learning, which can improve the efficiency of model generation, significantly reduce computing demand for large scale building energy prediction and protect building users' privacy. Algorithms will also be created to enable robust prediction with incomplete datasets; 3) A new complementary solution for predicting the GHG emissions reduction potential for stakeholders will be created to analyze near/zero GHG buildings in terms of energy performance. It is anticipated that these results will be beneficial both in terms of making buildings greener by reducing GHG emissions and energy consumption as well as decreasing operational costs. The plan is to seek the U.S. Department of Energy's Pacific Northwest National Laboratory to adopt the research results in their national building energy policy analysis for 139 million homes. The Northern Ireland Housing Executive will utilize this work to help predict decarbonization pathways for their housing stock of nearly 86,000 homes (10% of the housing stock in NI). The research will also assist the Sustainable Energy Authority of Ireland for its retrofit plan of 500,000 homes in the Republic of Ireland.

Project Team

University of Colorado Boulder

Wangda Zuo, Ph.D.
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, United States
wangda.zuo@colorado.edu

Yingli Lou
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, United States
yingli.lou@colorado.edu

Yizhi Yang
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, United States
yizhi.yang@colorado.edu

Ulster University

Neil Hewitt, Ph.D.
Belfast School of Architecture and the Built Environment, Ulster University, Northern Ireland
nj.hewitt@ulster.ac.uk

University College Dublin

James O'Donnell, Ph.D.
School of Mechanical and Materials Engineering and UCD Energy Institute, University College Dublin, Ireland
james.odonnell@ucd.ie

Cathal Hoare
School of Mechanical and Materials Engineering and UCD Energy Institute, University College Dublin, Ireland
cathal.hoare@ucd.ie

Usman Ali, Ph.D.
School of Mechanical and Materials Engineering and UCD Energy Institute, University College Dublin, Ireland
usman.ali@ucd.ie

Collaborators

United States
- Pacific Northwest National Laboratory
Ireland
- Sustainable Energy Authority of Ireland
- Electricity Supply Board (ESB) Networks
Northern Ireland
- Northern Ireland Housing Executive

Publications

Journal Article

Y. Lou, Y. Ye, Y. Yang, W. Zuo 2022. “Long-term Carbon Emission Reduction Potential of Building Retrofits with Dynamically Changing Electricity Emission Factors.” Building and Environment, 210, pp. 108683.

Y. Lou, Y. Yang, Y. Ye, W. Zuo, J. Wang 2021. “The Effect of Building Retrofit Measures on CO2 Emissions Reduction – A Case Study with U.S. Medium Office Buildings.” Energy and Buildings, 253, pp. 111514.

J. Neale, M. H. Shamsi, E. Mangina, D. Finn, J. O’Donnell 2022. "Accurate Identification of Influential Building Parameters Through an Integration of Global Sensitivity and Feature Selection Techniques." Applied Energy, 315, pp. 118956.

Press Release

National Science Foundation Award Announcement (01/22-08/24)
National Science Foundation Award Announcement (09/21-12/2021)
"International research partnership aims to reduce residential energy consumption" by College of Engineering at the University of Colorado Boulder
"US-Ireland Partnership to deliver data driven approach to address residential energy use" by UCD Energy Institute

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Contaminant Spread Modeling

Anonymous — Wed, 09 Sep 2020 17:31:22 +0000

Contaminant Spread Modeling Anonymous (not verified) Wed, 09/09/2020 - 11:31

Cary Faulkner

Sponsored by Defense Threat Reduction Agency

This five-year project aims to model the transport of harmful biological and chemical compounds in urban environment using different modeling techniques, such as computational fluid dynamics and multizone modeling for indoor airflow, Modelica-based modeling for HVAC system and control. The project is led by Lawrence Berkeley National Laboratory and in collaboration with many federal research laboratories and industry partners. In response to the COVID pandemic, our team has investigated various mitigation strategies, including 1) placement of portable air cleaners in a conference room; 2) HVAC operation strategies for office buildings during COVID and their energy consumptions.

Comparison of the streamlines, colored by the local velocity magnitude, coming from the portable air cleaner (PAC) inlet in (a) the fully occupied (F) and (b) the socially distanced (D) room setups for the PAC setting 1BL. (Castellini et al. 2021)

Project Team

Wangda Zuo, Ph.D.
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder
wangda.zuo@colorado.edu

Jake Castellini, M.S.
Department of Mechanical Engineering, University of Colorado Boulder
jaca6283@colorado.edu

Cary Faulkner
Department of Mechanical Engineering, University of Colorado Boulder
cary.faulkner@colorado.edu

Publications

Journal Articles

C. A. Faulkner, J. E. Castellini, W. Zuo, D. M. Lorenzetti, M. D. Sohn 2022. “Investigation of HVAC Operation Strategies for Office Buildings During COVID-19 Pandemic.” Building and Environment, 207 (B), pp. 108519.
J. E. Castellini, C. A. Faulkner, W. Zuo, D. M. Lorenzetti, M. D. Sohn 2022. “Assessing the Use of Portable Air Cleaners for Reducing Exposure to Airborne Diseases in a Conference Room with Thermal Stratification.” Building and Environment, 207 (B), pp. 108441.

Conference Proceedings

C. A. Faulkner, J. E. Castellini Jr., W. Zuo, D. M. Lorenzetti, M. D. Sohn 2021. "MERV 13 Filtration for Office Buildings During COVID-19 Pandemic." Proceedings of the 12th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2021), Seoul, South Korea (Virtual Conference), November 24-26, 2021.
C. A. Faulkner, D. S. Jankowski, W. Zuo, P. Epple, M. D. Sohn 2021. "A Novel Physics-informed Algorithm for Training AI Models to Predict Indoor Airflow." Proceedings of the 12th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2021), Seoul, South Korea (Virtual Conference), November 24-26, 2021.

Presentations

C. A. Faulkner 2021 "A Novel Physics-informed Algorithm for Training AI Models to Predict Indoor Airflow", ISHVAC 2021 Conference, November.
C. A. Faulkner 2021 "MERV 13 Filtration for Office Buildings During COVID-19 Pandemic", ISHVAC 2021 Conference, November.

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Virtual Electric Power Board (EPB)

Anonymous — Tue, 11 Jun 2019 23:36:28 +0000

Virtual Electric Power Board (EPB) Anonymous (not verified) Tue, 06/11/2019 - 17:36

The goal of this collaborative research, with ORNL (who is the overall project lead) and the Electric Power Board (EPB) of Chattanooga, is to create calibrated virtual models of buildings within a utility’s service area to enable advanced grid modeling research (AGMR) integration with utility business systems. This will be achieved by making use of DOE’s suite of prototypical buildings, integrating available data sources for buildings with properties extracted from image processing of satellite and aerial imagery, developing leadership-class high performance computing capabilities for large-scale building simulations, and extracting relevant 15-minute utility data from advanced metering infrastructure (AMI) deployed for 176,579 buildings in EPB’s utility district.

Collaborator: Oak Ridge National Laboratory

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Building Energy Modeling - OpenStudio SDK Development and Management

Anonymous — Thu, 14 Feb 2019 18:47:50 +0000

Building Energy Modeling - OpenStudio SDK Development and Management Anonymous (not verified) Thu, 02/14/2019 - 11:47

Angelique Fathy

In support of DOE’s Building Energy Codes Program, Pacific Northwest National Laboratory (PNNL) developed a suite of prototype building models representing newly constructed commercial buildings that comply with ASHRAE Standard 90.1 and IECC requirements. In this project, we will collaborate with PNNL on developing and improving the OpenStudio versions of the prototype building models.

Collaborator

Pacific Northwest National Laboratory

Publications

Conference Proceedings

1. Y. Lou, Y. Ye, W. Zuo, J. Zhang 2021. "Energy Prediction Impact of the Space Level Occupancy Schedule for a Primary School." Proceeding of the 17th Conference of International Building Performance Simulation Association (Building Simulation 2021), September 1-3, Bruges, Belgium.

Presentation

Y. Lou 2021 "Energy Prediction Impact of the Space Level Occupancy Schedule for a Primary School", Building Simulation 2021 Conference, September.

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Energy Modeling of Typical Commercial Buildings in Support of ASHRAE Building Energy Quotient Energy Rating Program

Anonymous — Fri, 01 Apr 2016 06:00:00 +0000

Energy Modeling of Typical Commercial Buildings in Support of ASHRAE Building Energy Quotient Energy Rating Program Anonymous (not verified) Fri, 04/01/2016 - 00:00

The objective of this research is to reconcile the differences between the empirical and modeled baselines for energy performance comparison for new and existing commercial buildings, allowing seamless translation of building energy performance metrics among LEED, Standard 90.1, Standard 189.1, Standard 100, and the bEQ As Designed and In Operation ratings. This research is to:

Evaluate and characterize the variability of EUI with building characteristics for standard building types using both statistical analysis of CBECS data and parametric variation of prototype energy models.
Develop a set of modeling assumptions and correction procedures that provide consistent baselines for energy performance metrics for new and existing commercial buildings based on measured EUI of existing buildings.
Validate the modeling assumptions and correction procedures by demonstrating that energy models of buildings with average construction characteristics predict median energy performance.
Using the methods developed above, develop a procedure to relate the EUI of an ASHRAE Standard 90.1-2004 compliant building to the median EUI of existing buildings of the same type and in the same climate.

Collaborators

University of Miami

Open Source Release

During the project, we have develop new prototype building energy models based on the CBECS data. Those models have been open source released at Building Energy Models for Commercial Buildings Based on CBECS Data

Press Release

UM COE News "Dr. Zuo's proposal of ASHRAE1771-TRP fully funded"

Publications

Ph.D. Thesis:

Y. Ye 2019. "Standardized Computational Framework for Prototypical Building Energy Model Creation and Building Energy Analyses." Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder.

Journal Articles

Y., Ye, K. Hinkelman, Y. Lou, W. Zuo, G. Wang, G., J. Zhang 2021. "Evaluating the Energy Impact Potential of Energy Efficiency Measures for Retrofit Applications: A Case Study with US Medium Office Buildings." Building Simulation, pp. 1-17.
Y. Ye, Y. Lou, W. Zuo, E. Franconi, G. Wang 2020. "How Do Electricity Pricing Programs Impact the Selection of Energy Efficiency Measures? - A Case Study with U.S. Medium Office Buildings." Energy and Buildings, 224, pp. 110267.
Y. Ye, Y. Lou, M. Strong, S. Upadhyaya, W. Zuo, G. Wang 2020. "Development of New Baseline Models for U.S. Medium Office Buildings Based on Commercial Buildings Energy Consumption Survey Data." Science and Technology for the Built Environment, 26 (9), pp. 1321-1336.
Y. Ye, K. Hinkelman, J. Zhang, W. Zuo, G. Wang 2019. “A Methodology to Create Prototypical Building Energy Models for Existing Buildings: A Case Study on U.S. Religious Worship Buildings.” Energy and Buildings, 194, pp. 351-365.
Y. Ye, W. Zuo, G. Wang 2019. “A Comprehensive Review of Energy-Related Data for U.S. Commercial Buildings.” Energy and Buildings, 186, pp. 126-137.

Conference Proceedings

Y. Ye, K. Hinkelman, J. Zhang, Y. Xie, W. Zuo 2019. “A Methodology to Quantify the Impact of Building Energy Code Upgrades on Building Energy Savings: A Case Study on Small Offices.” Proceeding of the 16th Conference of International Building Performance Simulation Association (Building Simulation 2019), September 2-4, Rome, Italy.
Y. Ye, K. Hinkelman, W. Zuo, G. Wang 2019. “ASHRAE TRP-1771: Methodology to Evaluate Sensitive Levels of Inputs for US Commercial Building Models.” ASHRAE 2019 Annual Conference, June 22-26, Kansas City, MO.
Y. Ye, G. Wang, W. Zuo, P. Yang, K. Joshi 2018. “Development of a Baseline Building Model of Auto Service and Repair Shop.” 2018 ASHRAE Building Performance Analysis Conference and SimBuild (BPACS 2018), pp. 573-580, September 26-28, Chicago, IL.
Y. Ye, G. Wang, W. Zuo 2018. “Creation of a Prototype Building Model of College and University Building.” Proceedings of the 4th International Conference on Building Energy and Environment (COBEE2018), pp. 373-378, February 5-9, Melbourne, Australia.

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High Performance Building Simulation on Heterogeneous Platforms

Anonymous — Sun, 01 Feb 2015 07:00:00 +0000

High Performance Building Simulation on Heterogeneous Platforms Anonymous (not verified) Sun, 02/01/2015 - 00:00

Powerful computational capabilities have always been sought after by those working in fields that require vast amount of calculations to be done in a relatively short amount of time and a low cost. To meet this needs, we are conducting research in performing high performance building simulation using multi-core CPU and GPU (graphics processing units). By optimizing the numerical algorithm and using the multi-core CPU and GPU, we have successfully accelerated the indoor airflow simulation up to 1,500 times and daylighting simulation up to 12,000 times.

Collaborators

Lawrence Berkeley National Laboratory

Press Release

UM College of Engineering News "SBS lab's Research: Simulating Dense Gas Transport"

Publications

W. Tian, T. A. Sevilla, W. Zuo 2017. “A Systematic Evaluation of Accelerating Indoor Airflow Simulations Using Cross Platform Parallel Computing.” Journal of Building Performance Simulation, 10(3), pp. 243-255.

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Coupled Simulation of Indoor Environment, Envelope, HVAC, and Control Systems

Anonymous — Thu, 01 May 2014 06:00:00 +0000

Coupled Simulation of Indoor Environment, Envelope, HVAC, and Control Systems Anonymous (not verified) Thu, 05/01/2014 - 00:00

This research is to create a coupled simulation of indoor environment, envelope, HVAC and control systems to support the controls design and evaluation of energy efficient ventilation. The fast fluid dynamics (FFD) model is adopted to model the indoor environment with stratified air distribution. The Modelica Buildings library is used to model the building envelope, HVAC and related control system. The models have been publically released as a part of the Modelica Buildings library since 2015.

Collaborators

Lawrence Berkeley National Laboratory

Publications

Journal articles:

W. Tian, X. Han, W. Zuo, Q. Wang, Y. Fu, M. Jin 2019. “An Optimization Platform Based on Coupled Indoor Environment and HVAC Simulation and Its Application in Optimal Thermostat Placement.” Energy and Buildings, 199, pp. 342-251.
W. Tian, X. Han, W. Zuo, M. Sohn 2018. "Building Energy Simulation Coupled with CFD for Indoor Environment: A Critical Review and Recent Applications." Energy and Buildings, 165, pp.184-199.
W. Tian, T. A. Sevilla, W. Zuo, M. Sohn 2017. "Coupling Fast Fluid Dynamics and Multizone Airflow Models in Modelica Buildings Library to Simulate the Dynamics of HVAC System." Building and Environment, 122, pp. 269-286.
W. Zuo, M. Wetter, W. Tian, D. Li, M. Jin, Q. Chen 2016. "Coupling Indoor Airflow, HVAC, Control and Building Envelope Heat Transfer in the Modelica Buildings Library.” Journal of Building Performance Simulation, 9(4), pp. 366-381.

Conference proceedings:

W. Tian, Y. Fu, Q. Wang, T. A. Sevilla, W. Zuo 2018. “Optimization on Thermostat Location in an Office Room Using the Coupled Simulation Platform in Modelica Buildings Library: A Pilot Study.” Proceedings of the 4th International Conference on Building Energy and Environment (COBEE2018), pp. 569-574, February 5-9, Melbourne, Australia.
W. Tian, W. Zuo, T. A. Sevilla, M. Sohn 2017. “Coupled Simulation Between CFD and Multizone Models Based on Modelica Buildings Library to Study Indoor Environment Control.” Proceedings of the 12th International Modelica Conference, pp. 55-61, May 15-17, Prague, Czech Republic.
M. Wetter, M. Bonvini, T. S. Nouidui, W. Tian, W. Zuo 2015. “Modelica Buildings Library 2.0.” Proceedings of the 14th Conference of International Building Performance Simulation Association (Building Simulation 2015), pp. 387-394, December 7-9, Hyderabad, India.
W. Zuo, M. Wetter, D. Li, M. Jin, W. Tian, Q. Chen 2014. “Coupled Simulation of Indoor Environment, HVAC and Control System by Using Fast Fluid Dynamics and the Modelica Buildings Library.” Proceedings of the 2014 ASHRAE/IBPSA-USA Building Simulation Conference, pp. 56-63, September 10-12, Atlanta, GA.
W. Tian, W. Zuo 2013. “Literature Review and Research Needs to Couple Building Energy and Airflow Simulation.” Proceedings of the APEC Conference on Low-carbon Towns and Physical Energy Storage, pp. 492-496, May 25-26, Changsha, China.

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