High Performance Buildings

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��Ƶ, 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��Ƶ

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

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

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

Off

Traditional

White

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.

Off

Traditional

White

Improving Data Center Energy Efficiency through End-to-End Cooling Modeling and Optimization

Anonymous — Sat, 01 Oct 2016 06:00:00 +0000

Improving Data Center Energy Efficiency through End-to-End Cooling Modeling and Optimization Anonymous (not verified) Sat, 10/01/2016 - 00:00

Led by Dr. Zuo, this is a joint research project with Lawrence Berkeley National Laboratory and Schneider Electric. We have developed an open-source, free software package (see the following links) which provides practical, end-to-end (from the IT equipment to heat rejection to the ambient) modeling and optimization for data center cooling. It can be used as a stand-alone tool by data center designers, service consultants and facility managers, or be integrated into existing data center management software for autonomous optimal operation. It is the first practical tool that couples the modeling of airflow-management and cooling systems to enable a global data center cooling optimization. Its self-learning regression model enabled by an in situ adaptive tabulation algorithm and a fast fluid dynamics model can predict the critical airflow information under various operational conditions within a few milliseconds. The equation-based modeling language allows a fast and flexible modeling of various cooling system configurations. We have demonstrated the usage of our tool and identified 53% energy saving potentials in a Florida data center and 74% in the Massachusetts data center.

Open Source Models for Data Center Cooling

This project has resulted in open source Modelica models for the data center cooling system. The models haven been released as a part of the DOE's open source Modelica Buildings library: http://simulationresearch.lbl.gov/modelica/releases/latest/help/Buildings_Applications.html#Buildings.Applications.

The model development branch is at https://github.com/lbl-srg/modelica-buildings/tree/master/Buildings/Applications.

This project has also developed a reduced order model “ISAT-FFD” trained by simulations in Fast Fluid Dynamics to predict airflow in data centers: https://github.com/doetools/isat_ffd.

See this page for more information on FFD.

The development site of the ISAT module in Modelica Buildings library is at: https://bitbucket.org/sbslab-zuo/datacenter-mblisat.

The ISAT data center case can be found at: https://bitbucket.org/sbslab-zuo/datacenter-isat-examples/src/master/.

Please see this page for more information on tools related to this project.

Commerical Tool

Based on our open source FFD models, Schneider Electric developed a commerial software "EcoStruxure IT Advisor CFD", which is a cloud-based data center CFD design software.

Technical Advisory Group

Name	Institution
Amistadi, Henry R.	MITRE Corporation
Cleaver, Donald	Keystone Critical Systems & Advisors
Doppelhammer, F. James	University of Miami
Geraghty, Edward P.	CEC Group, LLC
Groenewold, John	JPMorgan Chase & Co.
Herrlin, Magnus	Lawrence Berkeley National Laboratory
Kaiser, Raymond	Amzur Technologies
Meneghan, Brian W.	Carrier Corporation
Plamondon, David	University of Massachusetts Medical School
Sartor, Dale	Lawrence Berkeley National Laboratory
Sorell, Vali	Syska Hennessy Group, Inc.

Collaborators

Press Release

Publications

Project Report

W. Zuo, M. Wetter, J. VanGilder, X. Han, Y. Fu, C. Faulkner, J. Hu, W. Tian, M. Condor 2021. “Improving Data Center Energy Efficiency through End-to-End Cooling Modeling and Optimization.” pp. 1-109, Report for US Department of Energy, DOE-CUBoulder-07688.

Recorded Presentations

W. Zuo 2020 "Open Source Modelica Models for Data Center Cooling", American Modelica Conference 2020, September.

Ph.D. Theses

Y. Fu 2020. "Modeling and Control for Grid-interactive Efficient Data Centers." Ph.D. Thesis, Department of Civil, Environmental, and Architectural Engineering, �鶹��Ƶ.
X. Han 2020. "Holistic Optimization of Data Center Cooling Systems and Airflow Management." Ph.D. Thesis, Department of Civil, Environmental, and Architectural Engineering, �鶹��Ƶ.

Journal Articles

X. Han, W. Tian, J. VanGilder, W. Zuo, C. Faulkner 2021. "An Open Source Fast Fluid Dynamics Model for Data Center Thermal Management." Energy and Buildings, 230, pp 110599.
Y. Fu, X. Han, K. Baker, W. Zuo 2020. “Assessments of Data Centers for Provision of Frequency Regulation.” Applied Energy, 277, pp 115621.
Y. Fu, W. Zuo, M. Wetter, J. W. VanGilder, P. Yang 2019. "Equation-Based Object-Oriented Modeling and Simulation of Data Center Cooling Systems." Energy and Buildings, 198, pp. 503-519.
W. Tian, J.W. VanGilder, X. Han, C.M. Healey, M.B. Condor, W. Zuo 2019. “A New Fast Fluid Dynamics Model for Data-Center Floor Plenums.” ASHRAE Transactions, 125, pp. 141-148.
Y. Fu, W. Zuo, M. Wetter, J. W. VanGilder, X. Han, D. Plamondon 2019. “Equation-Based Object-Oriented Modeling and Simulation for Data Center Cooling: A Case Study.” Energy and Buildings,186, pp. 108-125.
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, D. Li, W. Zuo, M. Wetter 2018. "Fast and Self-Learning Indoor Airflow Simulation Based on In Situ Adaptive Tabulation." Journal of Building Performance Simulation, 11(1), pp. 99-112.
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.

Conference Proceedings

X. Han, W. Tian, W. Zuo, J.W. VanGilder 2019. “Optimization of Workload Distribution of Data Centers Based on a Self-Learning In Situ Adaptive Tabulation Model.” Proceeding of the 16th Conference of International Building Performance Simulation Association (Building Simulation 2019), September 2-4, Rome, Italy.
W. Tian, J.W. VanGilder, M.B. Condor, X. Han, W. Zuo 2019. “An Accurate Fast Fluid Dynamics Model for Data Center Applications.” The Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm 2019), May 28-31, Las Vegas, NV.
Y. Fu, M. Wetter, W. Zuo 2018. “Modelica Models for Data Center Cooling Systems.” 2018 ASHRAE Building Performance Analysis Conference and SimBuild (BPACS 2018), pp. 438-445, September 26-28, Chicago, IL.
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.

Off

Traditional

White

Virtual Reality for Building Design

Anonymous — Tue, 01 Sep 2015 06:00:00 +0000

Virtual Reality for Building Design Anonymous (not verified) Tue, 09/01/2015 - 00:00

Previously, Virtual Reality seemed to be an application only possible in science fiction movies. However, with recent advances this technology has now become available to the general public. Pioneers in this field such as Oculus Rift, Google Cardboard, and PlayStation VR have provided a path for consumers to create applications which can be used in everyday tasks. Likewise, we have taken the initiative to create our own Virtual reality application for Fast Fluid Dynamics simulation visualizations based around the iPhone and iOS ecosystem. By using this platform, we can easily develop a portable technology which can enhance the marketability and usability of the indoor airflow simulations for customers and service providers. Figure shows that SBS Lab researcher is testing the VR device.

Off

Traditional

White

Design of an Energy and Water Efficient Hotel

Anonymous — Thu, 01 Jan 2015 07:00:00 +0000

Design of an Energy and Water Efficient Hotel Anonymous (not verified) Thu, 01/01/2015 - 00:00

Located in Tropical climate, buildings in Miami requires cooling around the year. Space conditioning and domestic hot water systems account for 41% of all energy usage in a hotel. To save the energy for space cooling and domestic hot water heating, we proposed a heat recovery system which pre-heats the city water using the waste heat from space cooling. We also proposed to collect, store and process the rain water for non-portable usage. The system is implemented at the Grand Beach Hotel in Miami Beach, FL. This is a joint project with UCI Engineering.

Press Release

UM COE News "Sustainability Research"

Publications

R. J. Miranda, S. Huang, G. A. Barrios, D. Li, W. Zuo 2015. “Energy Efficient Design for Hotels in the Tropical Climate Using Modelica.” Proceedings of the 11th International Modelica Conference, pp. 71-78, September 21-23, Versailles, France.

Figure SBS lab researcher Rey Miranda is giving a technical tour at the Grand Beach Hotel

Off

Traditional

White

Model Predictive Control for Chilled Water Plants

Anonymous — Thu, 01 Jan 2015 07:00:00 +0000

Model Predictive Control for Chilled Water Plants Anonymous (not verified) Thu, 01/01/2015 - 00:00

Improving the energy efficiency of chilled water plants is important since they account for about 35% the energy consumed by commercial building cooling equipment in the U.S. Model predictive control is a feasible way to enhance the operational efficiency of the chilled water plants. Our research is to develop a Python-based dynamic optimization platform that can perform continuous optimization for various control settings of chilled water plants. By using the Modelica modeling language, we can quickly model a chilled water plant with any system configurations and dynamically simulate its control behavior.

Collaborators

Lawrence Berkeley National Laboratory

Publications:

Journal articles:

S. Huang, A. Malara, W. Zuo, M. Sohn 2018. "A Bayesian Network Model for the Optimization of a Chiller Plant’s Condenser Water Set Point."Journal of Building Performance Simulation, 11(1), pp. 36-47.
S. Huang, W. Zuo, M. Sohn 2018. "A Bayesian Network Model for Predicting Cooling Load of Commercial Buildings." Building Simulation, 11(1), pp. 87-101.
S. Huang, W. Zuo, M. Sohn 2017. "Improved Cooling Tower Control of Legacy Chiller Plants by Optimizing the Condenser Water Set Point." Building and Environment, 111, pp. 33-46.
S. Huang, W. Zuo, M. Sohn 2016. "Amelioration of the Cooling Load Based Chiller Sequencing Control." Applied Energy, 168, pp. 204-215.

Conference proceedings:

S. Huang, W. Zuo, M. D. Sohn 2016. “A Bayesian Network Model for Predicting the Cooling Load of Educational Facilities.” Proceedings of the ASHRAE and IBPSA-USA SimBuild 2016: Building Performance Modeling Conference, pp. 1-8, August 8-12, Salt Lake City, UT.
S. Huang, M.V. Cavey, R. Sterling, W. Zuo, L. Helsen, A. Giretti, M. Bonvini, Z. O’Neill, M. Wetter, A. Costa, G. Boehme, R. Klein, B. Dong, M. M. Keane 2016. “IEA Annex 60 Activity 2.3: Model Use During Operation, Approach and Case Studies.” Proceedings of the 12th REHVA World Congress (CLIMA2016), May 22-25, Aalborg, Denmark.
S. Huang, W. Zuo, M. D. Sohn 2015. “A New Method for the Optimal Chiller Sequencing Control.” Proceedings of the 14th Conference of International Building Performance Simulation Association (Building Simulation 2015), pp. 316-323, December 7-9, Hyderabad, India.
A. C. L. Malara, S. Huang, W. Zuo, M. D. Sohn, N. Celik 2015. “Optimal Control of Chiller Plants Using Bayesian Network.” Proceedings of the 14th Conference of International Building Performance Simulation Association (Building Simulation 2015), pp. 449-455, December 7-9, Hyderabad, India.
T. Li, G. Shao, W. Zuo, S. Huang 2015. “Genetic Algorithm for Building Optimization - State-Of-The-Art Survey.” Proceedings of the 9th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC) and the 3rd International Conference on Building Energy and Environment (COBEE), pp. 205-210, July 12-15, Tianjin, China.
S. Huang, W. Zuo 2014. “Optimization of the Water-Cooled Chiller Plant System Operation.” Proceedings of the 2014 ASHRAE/IBPSA-USA Building Simulation Conference, pp. 300-307, September 10-12, Atlanta, GA.

Off

Traditional

White

Image Enhancement Techniques for Sonar Based Time-of-Flight Camera

Anonymous — Wed, 01 Jan 2014 07:00:00 +0000

Image Enhancement Techniques for Sonar Based Time-of-Flight Camera Anonymous (not verified) Wed, 01/01/2014 - 00:00

Sponsored by University of Miami

[video:https://www.youtube.com/watch?v=qIyFPsKVd1E]

This project aims to develop image enhancement techniques for sonar based time-of-flight camera. The objectives of this project are:

Obtain a 2D range Image of an indoor environment/object.
Process raw data in order to filter out inconsistencies.
Interpolate missing values due to the sensors inability to obtain the values.
Provide a final image of the range measurements.

Off

Traditional

White