Research

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 �鶹ӰԺ, 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

�鶹ӰԺ

Wangda Zuo, Ph.D.
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ, United States
wangda.zuo@colorado.edu

Yingli Lou
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ, United States
yingli.lou@colorado.edu

Yizhi Yang
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ, 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 �鶹ӰԺ
"US-Ireland Partnership to deliver data driven approach to address residential energy use" by UCD Energy Institute

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Optimal Co-Design of Integrated Thermal-Electrical Networks and Control Systems for Grid-interactive Efficient District (GED) Energy Systems

Anonymous — Wed, 14 Oct 2020 20:44:01 +0000

Optimal Co-Design of Integrated Thermal-Electrical Networks and Control Systems for Grid-interactive Efficient District (GED) Energy Systems Anonymous (not verified) Wed, 10/14/2020 - 14:44

Cary Faulkner

Sponsored by U.S. Department of Energy (DOE)

The coordination of thermal-electrical-control networks is essential for the optimal design of grid-interactive efficient district (GED) energy systems, particularly when renewable energy sources are integrated. Led by Dr. Zuo, this $4.16M joint research is to create a holistic open-source modeling and optimization platform for the optimal design and retrofit of GED energy systems. Based on National Renewable Energy Laboratory's URBANopt and Lawrence Berkeley National Laboratory's Modelica Buildings library, this platform can be used to increase system efficiency and resilience of our communities.

Technical Advisory Group

Name	Institution
Ben Ealey	Electric Power Research Institute
Caitlin Holley	ENGIE
Henry Johnstone	GLHN
John Camilleri	PSC
Peter Lilienthal	HOMER Energy by UL
Shalom Flank	Pareto Energy

Team Members:

�鶹ӰԺ:

Wangda Zuo, Associate Professor, Ph.D.
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
wangda.zuo@colorado.edu

Kyri Baker, Assistant Professor, Ph.D.
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
Kyri.Baker@colorado.edu

Bryan Birosak, Director
Utility and Energy Services, �鶹ӰԺ
bryan.birosak@colorado.edu

Ellen Edwards, Energy Manager
Utility and Energy Services, �鶹ӰԺ
ellen.edwards@colorado.edu

Katy Hinkelman, M.S., EIT
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
kathryn.hinkelman@colorado.edu

Cary Faulkner
Department of Mechanical Engineering, �鶹ӰԺ
cary.faulkner@colorado.edu

Aisling Pigott
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
Aisling.Pigott@colorado.edu

Saranya Anbarasu, M.S.
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
saranya.anbarasu@colorado.edu

Mingzhe Liu, Ph.D.
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
mingzhe.liu@colorado.edu

Chengnan Shi, M.S.
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
chengnan.shi@colorado.edu

Rensselaer Polytechnic Institute:

Luigi Vanfretti, Associate Professor, Ph.D.
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute
luigi.vanfretti@gmail.com

Tetiana Bogodorova, Research Scientist, Ph.D.
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute
tetiana.bogodorova@gmail.com

Fernando Fachini
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute
emaildofachini@gmail.com

University of Texas Austin:

Atila Novoselac, Professor, Ph.D.
Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin
atila@mail.utexas.edu

Ardeshir Moftakhari
Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin
ardeshir.moftakhari@gmail.com

Mengjia Tang
Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin
m.tang@utexas.edu

Lawrence Berkeley National Laboratory:

Michael Wetter, Staff Scientist, Ph.D.
Building Technology & Urban Systems Division, Lawrence Berkeley National Laboratory
mwetter@lbl.gov

Jianjun Hu, Principal Scientific Engineering Associate, Ph.D.
Building Technology & Urban Systems Division, Lawrence Berkeley National Laboratory
jianjunhu@lbl.gov

National Renewable Energy Laboratory:

Kyle Benne, Researcher, M.S.
National Renewable Energy Laboratory
Kyle.Benne@nrel.gov

Nicholas Long, Senior Engineer, P.E., M.S.
Commercial Buildings Research Group, National Renewable Energy Laboratory
nicholas.long@nrel.gov

Amy Allen, Researcher, M.S.
National Renewable Energy Laboratory
Amy.Allen@nrel.gov

Lauren Klun, Project Manager
National Renewable Energy Laboratory
Lauren.Klun@nrel.gov

Amzur technologies:

Raymond Kaiser, Director of Energy Management Systems
Amzur Technologies
raymond.kaiser@amzur.com

Media Reports

Open source tool aims to improve electric grid efficiency, resiliency

Resulted Open Source Libraries

Journal Publications

K. Hinkelman, S. Anbarasu, M. Wetter, A. Gautier, W. Zuo. 2022. "A Fast and Accurate Modeling Approach for Water and Steam Thermodynamics with Practical Applications in District Heating System Simulation," Energy, 254(4), pp. 124227.

K. Hinkelman, J. Wang, W. Zuo, A. Gautier, M. Wetter, C. Fan, N. Long. 2022. "Modelica-Based Modeling and Simulation of District Cooling Systems: A Case Study." Applied Energy, 311, pp.118654.

Conference Proceedings

F. Fachini, M. d. Castro, M. Liu, T. Bogodorova, L. Vanfretti, W. Zuo. 2022. “Multi-Domain Power and Thermo-Fluid System Stability Modeling using OpenIPSL Library”, 2022 IEEE Power & Energy Society General Meeting (PESGM), July 17-21, Denver, CO.

K. Hinkelman, S. Anbarasu, M. Wetter, A. Gautier, B. Ravache, W. Zuo 2022. "Towards Open-Source Modelica Models For Steam-Based District Heating Systems." Proceedings of the 1st International Workshop On Open Source Modelling And Simulation Of Energy Systems (OSMSES 2022), Aachen, German, April 4-5, 2022.

F. Fachini, L. Vanfretti, M. C. Fernandes, T. Bogodorova, G. Laera 2021. “Modeling and Validation of Renewable Energy Sources in the OpenIPSL Modelica Library.” Proceeding of the 47th Annual Conference of the IEEE Industrial Electronics Society (IECON 2021). October 13-16, Toronto, Canada.

A. Pigott, K. Baker, S. A. Dorado-Rojas, L. Vanfretti 2022. “Dymola-Enabled Reinforcement Learning for Real-time Generator Set-point Optimization.” Proceeding of the 13th Conference on Innovative Smart Grid Technologies (ISGT 2022). February 21-24, Washington, D.C., USA.

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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, �鶹ӰԺ
wangda.zuo@colorado.edu

Jake Castellini, M.S.
Department of Mechanical Engineering, �鶹ӰԺ
jaca6283@colorado.edu

Cary Faulkner
Department of Mechanical Engineering, �鶹ӰԺ
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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Modernizing Cities via Smart Garden Alleys with Application in Makassar City

Anonymous — Fri, 17 Jul 2020 17:34:44 +0000

Modernizing Cities via Smart Garden Alleys with Application in Makassar City Anonymous (not verified) Fri, 07/17/2020 - 11:34

Cary Faulkner

Sponsored by National Science Foundation

This project is sponsored by the National Science Foundation and is in collaboration with the US Department of State. This project will work to integrate innovations in smart and connected communities to improve garden alleys within the City of Makassar, Indonesia via a synergistic collaboration between US and Indonesian teams and a close partnership with Makassar City. Makassar is striving to become a livable world-class city for a fast-growing, diverse population of 1.7 million people. The ongoing Garden Alley project in the city aims to improve the livability of the city, measured by factors including air-quality, heat index, food security, and social interactions. There are 7520 alleys in Makassar City. To date, Makassar has implemented 40 gardens within 15 of the city's sub-districts, covering a sizable portion of the city's alleys. The goal of this research is to catalyze the transformation of Makassar City's garden alleys into smart environments by deploying a sensor network at representative green allies and conventional allies to collect data related to air quality, microclimates, and other factors, to analyze the heterogeneous data using machine learning techniques, and to then share the data and its insights with city representatives and specific communities within the city.

Project Team

�鶹ӰԺ

Wangda Zuo, Ph.D.
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
wangda.zuo@colorado.edu

John Zhai, Ph.D.
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
john.zhai@colorado.edu

Katy Hinkelman, M.S., EIT
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
kathryn.hinkelman@colorado.edu

Yizhi Yang , M.S.
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
yizhi.yang@colorado.edu

Yingli Lou , M.S.
Department of Civil, Environmental and Architectural Engineering, �鶹ӰԺ
yingli.lou@colorado.edu

Chris Payne
Fairview High School
ninjakiwi1224@gmail.com

Virginia Polytechnic Institute and State University

Walid Saad, Ph.D.
Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University
walids@vt.edu

Alexander DeRieux
Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University
acd1797@vt.edu

Universitas Gadjah Mada

Rachmawan Budiarto, Ph.D., G.P.
Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Indonesia
Centre for Development of Sustainable Region (CDSR)
rachmawan@ugm.ac.id

Irawan Eko Prabowo, M.Eng.
Center for Energy Studies, Universitas Gadjah Mada, Indonesia
irawanekop@ugm.ac.id

Dwi Novitasari, M.Sc.
Department of Electrical and Information Engineering, Universitas Gadjah Mada,
dwi.novitasari@mail.ugm.ac.id

Institut Teknologi Bandung

M Donny Koerniawan, Dr. Eng.
Department of Architecture, Institut Teknologi Bandung, Indonesia
donny@ar.itb.ac.id

Lily Tambunan, Dr.Ir.
Department of Architecture, Institut Teknologi Bandung, Indonesia
lilyrosalina@yahoo.com

Nissa Aulia Ardiani S.T., M.Sc
Department of Architecture, Institut Teknologi Bandung, Indonesia
nissaardiani@gmail.com

Makassar City

Ferdi Mochtar SPt, M.Sc, PhD
Food Security Service, Makassar Government, Indonesia
Ferdy_mochtar@yahoo.com

Universitas Hasanuddin

Edward Syarif, Dr. Ir.
Department of Architecture, Universitas Hasanuddin, Indonesia
edosyarif@yahoo.com

Press Release

Project Updates

Publication

Z. Mahrez, E. Sabir, E. Badidi, W Saad, M Sadik 2021. "Smart Urban Mobility: When Mobility Systems Meet Smart Data." IEEE transactions on intelligent transportation systems.

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Support for District Energy Simulation with Modelica

Anonymous — Wed, 12 Jun 2019 22:21:23 +0000

Support for District Energy Simulation with Modelica Anonymous (not verified) Wed, 06/12/2019 - 16:21

Sponsored by U.S. Department of Energy (DOE)

CU East District Energy Plant

District heating and cooling (DHC) systems commonly involve a central plant that distributes steam, hot water, or chilled water to buildings by means of insulated pipes. This is a promising and long-established solution for community sustainability, yet it remains underutilized, particularly in the United States. By sharing thermal resources, DHC can reduce the carbon intensity of heating and cooling in buildings, reduce energy costs, improve air quality, allow high penetration of renewable energy sources, recycle waste heat from industrial and commercial activities, and improve the resiliency of communities. In collaboration with the National Renewable Energy Laboratory (NREL) and Lawrence Berkeley National Laboratory (LBNL), the goal of this project is to create a new software analysis platform that leverages the Modelica language in order to enable developers of community-scale construction projects to effectively evaluate and optimize DHC systems. The models will be publicly released in LBNL's Modelica Buildings Library and used by NREL's URBANopt.

Collaborators:

Journal Publications

K. Hinkelman, J. Wang, W. Zuo, A. Gautier, M. Wetter, C. Fan, N. Long. 2022. "Modelica-Based Modeling and Simulation of District Cooling Systems: A Case Study." Applied Energy, 311, pp.118654.

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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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Comprehensive Pliant Permissive Priority Optimization

Anonymous — Mon, 14 Jan 2019 01:10:12 +0000

Comprehensive Pliant Permissive Priority Optimization Anonymous (not verified) Sun, 01/13/2019 - 18:10

This project is funded by Department of Energy and is a joint project with Pacific Northwest National Laboratory and Oak Ridge National Laboratory. The project goal is to develop a framework to prioritize behind-the-meter end-use resources, and a prototype tool that offers assessment of end-use load-utilization patterns and provides real-time dynamic prioritization of the energy-consuming loads based on end-use functional utility to satisfy occupant activity requirements.

Collaborators

Journal publications

S. Huang, J. Wang, Y. Fu, W. Zuo, K. Hinkelman, R. M. Kaiser, D. He, D. Vrabie 2021. “An Open-source Virtual Testbed for a Real Net-Zero Energy Community”. Sustainable Cities and Society, 75, pp. 103255.
J. Wang, S. Huang, W. Zuo, D. Vrabie 2021. “Occupant Preference-Aware Load Scheduling for Resilient Communities.” Energy and Buildings, 252, pp. 111399.
J. Wang, K. Garifi, K. Baker, W. Zuo, Y. Zhang, S. Huang, D. Vrabie 2020. "Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities." Energies, 13, pp. 5683.

Conference Proceedings

J. Wang, W. Zuo, S. Huang, D. Vrabie 2020. “Data-driven Prediction of Occupant Presence and Lighting Power: A Case Study for Small Commercial Buildings.” American Modelica Conference 2020, Virtual Conference, September 22-24.
Y. Fu, S. Huang, Y. Liu, T. McDermott, D. Vrabie, W. Zuo 2019. “A Multidisciplinary Model to Couple Power System Dynamics and Building Dynamics to Enable Building-to-Grid Integration.” Proceeding of the 16th Conference of International Building Performance Simulation Association (Building Simulation 2019), September 2-4, Rome, Italy.
Y. Fu, S. Huang, D. Vrabie, W. Zuo 2019. “Coupling Power System Dynamics and Building Dynamics to Enabling Building-to-Grid Integration.” Proceedings of the 13th International Modelica Conference, pp. 561-566, March 4-6, Regensburg, Germany.

Open Source Model Release

A open source Modelica library for the net zero energy community (NZEC) is built to facilitate the design and operation of a real NZEC. The models and related training materials are available at Net Zero Energy Community (NZEC) Library.

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An Innovative Computational Platform for Robust and Optimal Operation of Renewable Energy Cities

Anonymous — Sat, 24 Nov 2018 18:04:00 +0000

An Innovative Computational Platform for Robust and Optimal Operation of Renewable Energy Cities Anonymous (not verified) Sat, 11/24/2018 - 11:04

As international sustainability goals and U.S. sustainability initiatives increase, sustainable energy applications are evolving from the conventional single building to the entire city. A 100% renewable energy city (REC) will undoubtedly play a pivotal role in this progression. The energy system of a REC will integrate many subsystems including renewable energy generation, distribution networks, building energy consumption, electric and thermal storage, and interaction with the power grid.

This proposed project is to create the scientific foundation of an innovative computational platform that will enable robust and optimal operation of future renewable energy cities.

Collaborator: National Renewable Energy Laboratory, �鶹ӰԺ Housing Partners, Amzur Technologies

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1661-TRP, Development of Near-Optimal Control Sequence for Chiller Plants with Water Side Economizer using Dynamic Models

Anonymous — Wed, 01 Feb 2017 07:00:00 +0000

1661-TRP, Development of Near-Optimal Control Sequence for Chiller Plants with Water Side Economizer using Dynamic Models Anonymous (not verified) Wed, 02/01/2017 - 00:00

A water-side economizer (WSE) is a cooling system by which the chilled water is cooled by the cooling tower directly/indirectly without the use of mechanical cooling. Employing WSEs can decrease the building energy consumption by reducing the chiller operating time or increasing the chiller efficiency. WSEs, however, may pose challenges to the operation of chiller plants. For example, using WSE may result in chiller short cycling and temporary loss of chilled water supply temperature control. Moreover, having cold condenser water may cause the chillers trip on low head pressure, i.e., a lower pressure difference between the condenser and the evaporator. The above challenges can be addressed by adopting certain control sequences and there have been several control sequences successfully demonstrated in real-world applications. However, they are not intensively evaluated against all plant configurations, under all possible weather conditions. It is necessary to consider the effects for all plant configurations and all weather conditions when developing and evaluating a near-optimal WSE control sequence for large-scale applications.

Therefore, the objectives of the proposed research is to:

evaluate state-of-art control sequences for water side economizer (WSE) and develop a near-optimal sequence based on the comparison;
develop open-source dynamic models for controls evaluation of the chilled water plants with WSEs;
demonstrate the value of using a multidisciplinary modeling and analysis environment for integrating and sharing research among different TCs.

To achieve the above objectives, researchers in our lab will

conduct a comprehensive review on the state-of-the-art control sequences for WSEs.
develop Dynamic Models for Different Configurations of Chiller Plants with WSEs.
evaluate Strategies for WSE Sequencing.
develop a Near-Optimal Control Sequence for WSEs.

Publications

C. Fan, K. Hinkelman, Y. Fu, W. Zuo, S. Huang, C. Shi, N. Mamaghani, C. Faulkner, X. Zhou 2021. "Open-source Modelica Models for the Control Performance Simulation of Chiller Plants with Water-side Economizer." Applied Energy. 299, pp. 117337.
Y. Fu, X. Lu, W. Zuo 2019. “Modelica Models for the Control Evaluations of Chilled Water System with Waterside Economizer.” Proceedings of the 13th International Modelica Conference, pp. 811-818, March 4-6, Regensburg, Germany.

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