U.S.-Ireland R&D Partnership: Intelligent Data Harvesting for
Multi-Scale Building Stock Classification and Energy
Performance Prediction
Sponsored by U.S. National Science Foundation, Science Foundation of Ireland, and Department for the Economy in Northern Ireland (2021-2025)
Project Description
This is a 5-year $1.2M international collaborative research project among Pennsylvania State University, 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
Pennsylvania State University
Wangda Zuo, Ph.D.
Department of Architectural Engineering, Pennsylvania State University, United States
Yingli Lou, Ph.D.
Department of Architectural Engineering, Pennsylvania State University, United States
Yizhi Yang
Department of Architectural Engineering, Pennsylvania State University, United States
Rosina Adhikari
Department of Architectural Engineering, Pennsylvania State University, United States
Jiyuan Sui
Department of Architectural Engineering, Pennsylvania State University, United States
Almila Meng
Department of Architectural Engineering, Pennsylvania State University, United States
Genevieve Gandara
Department of Mechanical Engineering, California Institute of Technology, United States
Alex Pan
Department of Computer Science, Pennsylvania State University, United States
Carter Lowell
Department of Architectural Engineering, Pennsylvania State University, United States
Ulster University
Neil Hewitt, Ph.D.
Belfast School of Architecture and the Built Environment, Ulster University, Northern Ireland
Stephanie Ogunrin, Ph.D.
Belfast School of Architecture and the Built Environment, Ulster University, Northern Ireland
University College Dublin
James O'Donnell, Ph.D.
School of Mechanical and Materials Engineering and UCD Energy Institute, University College Dublin, Ireland
Cathal Hoare
School of Mechanical and Materials Engineering and UCD Energy Institute, University College Dublin, Ireland
Usman Ali, Ph.D.
School of Mechanical and Materials Engineering and UCD Energy Institute, University College Dublin, Ireland
Collaborators
- United States
- Ireland
- Northern Ireland
Press Release
- National Science Foundation Award Announcement
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College of Engineering at the University of Colorado Boulder “International research partnership aims to reduce residential energy consumption”
Reports
- U.S.-Ireland Workshop on Multi-Scale Building Energy Modeling: Challenges and Opportunities, Dublin, Ireland, May 27-28, 2024. Report.
Ph.D. Thesis
- Y. Lou 2022. ”Interconnections among Energy Consumption, Carbon Emissions, and Economic Impacts for Sustainable Buildings.” Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder.
Journal Articles
- U. Ali, S. Bano, M.H. Shamsi, D. Sood, C. Hoare, W. Zuo, N. Hewitt, J. O’Donnell 2024. “Urban Residential Building Stock Synthetic Datasets for Building Energy Performance Analysis.” Data in Brief. 53. pp. 110241.
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Y. Lou, Y. Ye, Y. Yang, W. Zuo, G. Wang. 2024. “Energy Modeling of Typical Commercial Buildings in Support of ASHRAE Building Energy Quotient Energy Rating Program (ASHRAE RP-1771).” Science and Technology for the Built Environment.
- U. Ali, S. Bano, M. H. Shamsi, D. Sood, C. Hoare, W. Zuo, N. Hewitt, J. O’Donnell. 2023. “Urban Building Energy Performance Prediction and Retrofit Analysis Using Data-Driven Machine Learning Approach.” Energy and Buildings. 303 (2024), 113768.
- C. A. Faulkner, D. S. Jankowski, J. E. Castellini Jr., W. Zuo, P. Epple, M. D. Sohn, A. T. Z. Kasgari, W. Saad 2023. “Fast Prediction of Indoor Airflow Distribution Inspired by Synthetic Image Generation Artificial Intelligence.” Building Simulation, pp.1-20.
- Y. Ye, C. A. Faulkner, R. Xu, S. Huang, Y. Liu, D. L. Vrabie, J. Zhang, W. Zuo 2023. “System modeling for grid-interactive efficient building applications.” Journal of Building Engineering. 69. pp. 101614.
- Y. Yang, Y. Lou, C. Payne, Y. Ye, W. Zuo. 2023. “Long-term carbon intensity reduction potential of K-12 school buildings in the United States.” Energy and Building. 282, pp. 112802.
- Y. Lou, Y. Ye, Y. Yang, W. Zuo, G. Wang, M. Strong, S. Upadhyaya, C. Payne. 2023. “Individualized Empirical Baselines for Evaluating the Energy Performance of Existing Buildings,” Science and Technology for the Built Environment, 29 (1), pp. 19-33.
- Y. Lou, Y. Yang, Y. Ye, C. He, W. Zuo. 2022. “The Economic Impacts of Carbon Emission Trading Scheme on Building Retrofits: A Case Study with US Medium Office Buildings,” Building and Environment, 221, pp. 109311.
- C. A. Faulkner, J. E. Castellini, Y. Lou, W. Zuo, D. M. Lorenzetti, M. D. Sohn. 2022. “Tradeoffs Among Indoor Air Quality, Financial Costs, and CO2 Emissions for HVAC Operation Strategies to Mitigate Indoor Virus in U.S. Office Buildings,” Building and Environment, 221, pp. 109282.
- Y. Ye, M. Strong, Y. Lou, C. A. Faulkner, W. Zuo, S. Upadhyaya. 2022. “Evaluating Performance of Different Generative Adversarial Networks for Large-Scale Building Power Demand Prediction,” Energy and Buildings, 269, pp. 112247.
- C. Hoare, R. Aghamolaei, M. Lynch, A. Gaur, J. O’Donnell, 2022. “A linked data approach to multi-scale energy modelling.” Advanced Engineering Informatics, 54, pp. 101719.
- 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.
- 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.
- J. Wang, P. Munankarmi, J. Maguire, C. Shi, W. Zuo, D. Roberts, X. Jin 2022.“Carbon Emission Responsive Building Control : A Case Study With an All-Electric Residential Community in a Cold Climate,” Applied Energy, 314, pp. 118910.
- 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.
- C. A. Faulkner, R. Lutes, S. Huang, W. Zuo, D. Vrabie 2023. “Simulation-based assessment of ASHRAE Guideline 36, considering energy performance, indoor air quality, and control stability.” Building and Environment, 240, pp. 110371.
Conference Papers
- Y. Yang, J. Sui, Y. Ye, W. Zuo, Y. J. Jung, X. Lei 2024 “Long-Term Assessment of Commercial Building Energy and Carbon Emissions in the Northwestern Region Under Future Weather Trend.” SimBuild 2024. May 21-23, Denver, CO.
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Y. Yang, Y. Lou, Y. Ye, W. Zuo 2023. “Regional Carbon Emission Reduction Prediction via Retrofits of Commercial Buildings with a Case Study of US School Buildings in Hot Climates.” The 18th Conference of International Building Performance Simulation Association (Building Simulation 2023). September 4-6, Shanghai, China.
- U. Ali, S. Bano, N. Molinard, M. Shamsi, D. Sood, C. Hoare, J. O’Donnell 2023. “Data-driven Prediction of Residential Building Energy Performance at an Urban Scale through End-use Demand Segregation.” The 18th Conference of International Building Performance Simulation Association (Building Simulation 2023). September 4-6, Shanghai, China.
- A. Usman, S. Bano, M. Shamsi, D. Sood, C. Hoare, J. O’Donnell 2023. “Residential building energy performance prediction at an urban scale using ensemble machine learning algorithms.” Proceedings of the 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference, July 10-12, Crete, Greece.
- H. Cathal, T. AlQazzaz, U. Ali, S. Hu, and J. O’Donnell 2023. “Development of a National Scale Digital Twin for Domestic Building Stock.” Proceedings of the 11th Linked Data in Architecture and Construction Workshop, June 15-16, Matera, Italy.
- C. A. Faulkner, J. E. Castellini Jr., Y. Lou, W. Zuo, D. M. Lorenzetti, M. D. Sohn 2022. “Tradeoffs Between Indoor Air Quality and Sustainability for Indoor Virus Mitigation Strategies in Office Buildings.” American Modelica Conference 2022. October 26-28, Dallas, TX.
- Y. Lou, Y. Yang, Y. Ye, W. Zuo 2023. “Pathways To Decarbonize the U.S. Medium-Sized Office Buildings in Cold Climates.” 2023 HVAC Cold Climate Conference. March 6-8, Anchorage, AK.
- Y. Lou, Y. Ye, W. Zuo, Y. Yang 2022. “Realistic Estimation of CO2 Emission Reductions Due to Building Retrofit.” 2022 Building Performance Analysis Conference and SimBuild. September 14-16, Chicago, IL.
Project News
SBS Lab Team Attends US-Ireland Workshop on Multi-Scale Building Energy Performance Prediction
Nearly 30 experts from the US, Republic of Ireland, and Northern Ireland attended the U.S.-Ireland Workshop on Multi-Scale Building Energy Performance Prediction at the University College Dublin. The participants were from a wide variety of backgrounds including...
Research Paper Published As Part Of Our Joint U.S.-Ireland R&D Project
We are delighted to announce that another research paper from our joint U.S.-Ireland R&D Project was published in the Data in Brief Journal. The paper titled “Urban Residential Building Stock Synthetic Datasets for Building Energy Performance Analysis”...
SBS Lab US-Ireland R&D project team at Ulster University
Last week, Prof. Zuo and Ph.D. student Yizhi Yang attended the joint project meeting at Ulster University. They were able to meet and discuss their joint US-NI-Rol project “Intelligent Data Harvesting for Multi-Scale Building Stock Classification and Energy...