Tools

Building Energy Models for Commercial Buildings Based on CBECS Data

Anonymous — Thu, 11 Mar 2021 19:11:52 +0000

Building Energy Models for Commercial Buildings Based on CBECS Data Anonymous (not verified) Thu, 03/11/2021 - 12:11

As an outcome of ASHRAE RP-1771, we have developed building energy models based on Commercial Buildings Energy Consumption Survey (CBECS) data to support the development of the ASHRAE building energy quotient (bEQ) standard. These building energy models can be used as a starting point 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.

These building energy models are developed based on the 2003 CBECS data, which include 18 commercial building types in two vintages (pre-1980 and post-1980) in 15 climate locations in the U.S. This results in an overall set of 540 total building energy models (EnergyPlus™ Version 9.0 for Supermarket; EnergyPlus™ Version 8.7 for the other building types).

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Current version (V2.0) was released on 3/11/2022. See release history for previous releases.

Table 1. Building Energy Models
No.	Building Type	V2.0
1	Large Office*	ZIP
2	Medium Office*	ZIP
3	Small Office*	ZIP
4	Non-refrigerated Warehouse*	ZIP
5	Standalone Retail*	ZIP
6	Strip Mall*	ZIP
7	Primary School*	ZIP
8	Secondary School*	ZIP
9	College/University [1]	ZIP
10	Quick Service Restaurant*	ZIP
11	Full Service Restaurant*	ZIP
12	Hospital*	ZIP
13	Outpatient Health Care*	ZIP
14	Motel or Inn*	ZIP
15	Hotel*	ZIP
16	Religious Worship [2]	ZIP
17	Supermarket**	ZIP
18	Auto Repair Service [3]	ZIP
19	All Building Types	ZIP

* This model is developed by referring to the U.S. Department of Energy Commercial Reference Building Models. https://github.com/NREL/openstudio-standards

** This model is developed by referring to the model developed by Oak Ridge National Laboratory. https://www.ornl.gov/publication/development-supermarket-prototype-building-model

Table 2. 2003 Weather Data*
Thermal Zone	Climate	Representative City
1A	Very Hot Humid	Miami, Florida
2A	Hot Humid	Houston, Texas
2B	Hot Dry	Phoenix, Arizona
3A	Warm Humid	Atlanta, Georgia
3B	Warm Dry	El Paso, Texas
3C	Warm Marine	San Francisco, California
4A	Mixed Humid	Baltimore, Maryland
4B	Mixed Dry	Albuquerque, New Mexico
4C	Mixed Marine	Seattle, Washington
5A	Cool Humid	Chicago, Illinois
5B	Cool Dry	Denver, Colorado
6A	Cold Humid	Burlington, Vermont
6B	Cold Dry	Helena, Montana
7	Very Cold	Duluth, Minnesota
8	Subarctic/Arctic	Fairbanks, Alaska

*Download weather file ZIP, which is provided by Climate.OneBuilding, https://climate.onebuilding.org

Acknowledgment

These building energy models are the outcome of the project "RP-1771, Energy Modeling of Typical Commercial Buildings in Support of ASHRAE Building Energy Quotient Energy Rating Program" sponsored by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). The project website is here.

We appreciate the RP-1771 Project Monitoring Subcommittee members for their comments and suggestions during the model development:

Chris Balbach, Performance Systems Development, New York, USA
Dru Crawley, Bentley Systems, District of Columbia, USA
Michael Deru, National Renewable Energy Laboratory, Colorado, USA
Charles Eley, USA
Daniel Nall, Syska Hennessy Group, New Jersey, USA
Terry Sharp, Oak Ridge National Laboratory, Tennessee, USA

The following people have directly contributed to the model creation:

Yingli Lou, ��ƷSM��ӰƬ, Colorado, USA
Matthew Strong, ��ƷSM��ӰƬ, Colorado, USA
Satish Upadhyaya, ��ƷSM��ӰƬ, Colorado, USA
Gang Wang, University of Miami, Florida, USA
Yunyang Ye, ��ƷSM��ӰƬ, Colorado, USA
Yizhi Yang, ��ƷSM��ӰƬ, Colorado, USA
Wangda Zuo, ��ƷSM��ӰƬ, Colorado, USA

Disclaimer

This material is based upon work supported by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) under the Research Project RP-1771. Neither ASHRAE, nor any of its employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by ASHRAE thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of ASHRAE thereof.

Related Publications:

[1] 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.

[2] 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.

[3] 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.

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Fast Fluid Dynamics Model

Anonymous — Thu, 12 Nov 2020 01:02:15 +0000

Fast Fluid Dynamics Model Anonymous (not verified) Wed, 11/11/2020 - 18:02

Cary Faulkner

Originating from the computer game industry, Fast Fluid Dynamics (FFD) is a faster alternative to Computational Fluid Dynamics (CFD) used for fluid flow simulation. Dr. Zuo developed the FFD model for indoor environment modeling in his Ph.D. thesis "Advanced Simulations of Air Distributions in Buildings" at Purdue University. Since then, he and many other researchers have extended the FFD model for both indoor and outdoor airflow simulation. This page summarizes the development of FFD model by the SBS Lab over the years.

Data Center Airflow Management:

The FFD model is tailored for data center computer room airflow management with new modules added for data center applications. The figure on the right shows temperature contours of an example data center with varying supply flow air ratio produced by FFD simulations. The air ratio is defined as the ratio of the total supply flowrate to the total IT flowrate. It can be seen a high air ratio overcools the data center, but too low of an air ratio can lead to unwanted hot spots. The source code is publicly available here. It is also publicaly released in the Modelica Buildings library to support the coupled simulation of indoor airflow and HVAC systems. The FFD model is already adopted by Schneider Electric in their commerical data center tools. The research is described in this paper: 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.

Coupled Simulation of Indoor Environment and Building HVAC and Control System:

A detailed room model was implemented in the open source Modelica Buildings library to enable the coupling of FFD with Modelica. This coupled model enables the co-simulation of airflow and HVAC system to study the optimal design and control of indoor environment. The coupled models are available at the Modelica Buildings library website. The research is described in this paper: 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.

Simulation of Outdoor Airflow Around Buildings:

FFD was compared to CFD for its ability to simulate natural ventilation in buildings, an important application to saving energy in buildings. The results showed that FFD could predict wind-driven and buoyancy-driven ventilation with reasonable accuracy. The figure on the right shows the velocity contours predicted by CFD (left) and FFD (right) for wind-driven ventilation around a group of buildings, where the prevailing wind is from the southwest direction (lower-left in the figure). The flow upstream is similar between the two, however discrepancies are present for the wake region behind the buildings. This research is described in this paper: M. Jin, W. Zuo, Q. Chen 2013. "Simulating Natural Ventilation in and Around Buildings by Fast Fluid Dynamics." Numerical Heat Transfer, Part A: Applications, 64(4), pp. 273-289.

Cross-platform Parallel Computing for Indoor Airflow Simulation:

Dr. Zuo implemented our first parallel verion of FFD in CUDA on a NVIDA GPU in 2009 and achieved 30 times speedup. Our latest parallel version of FFD code was implemented in OpenCL, which is a cross-platform parallel computing language. This code can perform parallel computing on multiple different CPUs or GPUs and achieved up to 1,000 times speedup as described in this paper: 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.

Game Style Demo of FFD Code

A game style demo of mixed convection flow in an empty room was created. Like playing a computer game, this interactive demo allows users to change supply airflow rate and floor temperature, add contaminants into the space and observe the transmission of the contaminants. This demo can be downloaded here. The results have been validated in our paper: W. Zuo, Q. Chen 2009. "Real Time or Faster-than-Real-Time Simulation of Airflow in Buildings." Indoor Air, 19 (1), pp. 33-44.

Related Journal Papers:

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.
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, 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.
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.
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.
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.
M. Jin, W. Zuo, Q. Chen 2013. "Simulating Natural Ventilation in and Around Buildings by Fast Fluid Dynamics." Numerical Heat Transfer, Part A: Applications, 64(4), pp. 273-289.
M. Jin, W. Zuo, Q. Chen 2012. "Improvement of Fast Fluid Dynamics for Simulating Airflow in Buildings." Numerical Heat Transfer, Part B Fundamentals, 62(6), pp. 419-438.
W. Zuo, M. Jin, Q. Chen 2012. "Reduction of Numerical Diffusion in FFD Model." Engineering Applications of Computational Fluid Mechanics, 6(2), pp. 234-247, 2012
W. Zuo, Q. Chen 2010. "Fast and Informative Flow Simulation in a Building by Using Fast Fluid Dynamics Model on Graphics Processing Unit." Building and Environment, 45(3), pp. 747-757.
W. Zuo, J. Hu, Q. Chen 2010. "Improvements on FFD Modeling by Using Different Numerical Schemes." Numerical Heat Transfer, Part B: Fundamentals, 58(1), pp. 1-16.
W. Zuo, Q. Chen 2010. "Simulations of Air Distribution in Buildings by FFD on GPU." HVAC&R Research, 16(6), pp. 785-798.
W. Zuo, Q. Chen 2009. "Real Time or Faster-than-Real-Time Simulation of Airflow in Buildings." Indoor Air, 19 (1), pp. 33-44.

Related 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.
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.
D. Li, W. Tian, W. Zuo, M. Wetter 2016. “Simulation Using In Situ Adaptive Tabulation and Fast Fluid Dynamics.” Proceedings of the ASHRAE and IBPSA-USA SimBuild 2016: Building Performance Modeling Conference, pp. 65-71, August 8-12, Salt Lake City, UT.
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.
M. Jin, Q. Chen, and W. Zuo 2013. “Validation of a Fast Fluid Dynamics Program for Simulating Natural Ventilation in Buildings .” Proceedings of CLIMA 2013, the 11th REHVA World Congress and the 8th Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, June 16-19, Prague, Czech.
M. Jin, W. Zuo, Q. Chen 2012. “Validation of Three Dimensional Fast Fluid Dynamics for Indoor Airflow Simulations .” Proceedings of the 2nd International Conference on Building Energy and Environment (COBEE2012), pp. 1055-1062, August 1-4, Boulder, CO.
W. Zuo, Q. Chen 2011. “Validation of a Fast-Fluid-Dynamics Model for Predicting Distribution of Particles with Low Stokes Number .” Proceedings of the 12th International Conference on Indoor Air Quality and Climate (Indoor Air 2011), June 5-10, Austin, TX.
W. Zuo, Q. Chen 2010. “Fast Simulations of Smoke Transport in Buildings .” Proceedings of the 41st International HVAC&R congress, pp. 340-348, December 1-3, Beograd, Serbian.
J. Hu, W. Zuo, Q. Chen 2010. “Impact of Time-Splitting Schemes on the Accuracy of FFD Simulations .” Proceedings of the 7th International Indoor Air Quality, Ventilation and Energy Conservation in Buildings Conference (IAQVEC 2010), August 15-18, pp. 55- 62, Syracuse, NY.
W. Zuo, Q. Chen 2010. “Improvements on the Fast Fluid Dynamic Model for Indoor Airflow Simulation.” Proceedings of the 4th National Conference of International Building Performance Simulation Association -USA (SimBuild2010), pp. 539-546, August 11-13, New York, NY.
W. Zuo, Q. Chen 2009. “High-Performance and Low-Cost Computing for Indoor Airflow .” Proceedings of the 11th Conference of International Building Performance Simulation Association (Building Simulation 2009), pp. 244-249, July 27-30, Glasgow, U.K.
W. Zuo, Q. Chen 2009. “Fast Parallelized Flow Simulations on Graphic Processing Units .” Proceedings of the 11th International Conference on Air Distribution in Rooms (RoomVent 2009), May 24-27, Busan, Korea.
W. Zuo, Q. Chen 2007. “Real Time Airflow Simulation in Buildings .” Proceedings of the 6th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings (IAQVEC 2007), October 28-31, Sendai, Japan.
W. Zuo, Q. Chen 2007. “Validation of Fast Fluid Dynamics for Room Airflow .” Proceedings of the 10th Conference of International Building Performance Simulation Association (Building Simulation 2007), pp. 980-983, September 3-6, Beijing, China.

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Modelica Buildings Library

Anonymous — Mon, 10 Aug 2020 15:51:28 +0000

Modelica Buildings Library Anonymous (not verified) Mon, 08/10/2020 - 09:51

Cary Faulkner

The Modelica Buildings library is a free open-source library with dynamic simulation models for building and district energy and control systems. The development of Modelica Buildings library is led by Lawrence Berkeley National Laboratory (LBNL). The Modelica Buildings library website is https://simulationresearch.lbl.gov/modelic a/

Developing library quality models requires rigorous training in physical systems, numerical algorithms, and software engineering. Dr. Zuo was one of the first developers of the Buildings library while he worked as a Research Scientist at LBNL. When Dr. Zuo moved to academia in 2013, he has continued the collaboration with LBNL for the development of Buildings library. Besides developing models by himself, he has invested a lot of time in training new library developers. As a result, many SBS lab members have been officially acknowledged as the contributors of the Buildings library. This page shows some of our research related to the Buildings library development.

Data Center Cooling Models:

Detailed cooling system models for data centers were developed and released open source in the Buildings library here. These models were developed as part of the DOE project Improving Data Center Energy Efficiency through End-to-End Cooling Modeling and Optimization to improve data center energy efficiency through optimization of cooling systems and airflow management, either separately or coupled. An example of one of the chiller cooled system models is shown on the right. More about this research can be found in this paper: 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.

Coupled Indoor Airflow, HVAC, Control, and Building Envelope Heat Transfer Models:

These models couple our FFD code for indoor airflow simulation with HVAC, control, and building envelope heat transfer models from the Buildings library. This allows for the dynamic simulation of complex, stratified airflow in buildings that can be used for the design and control of ventilation systems. These models have been released open source in the Buildings library here and an example model that simulates feedback control of space cooling is shown on the right. More about this research can be seen in this paper: 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.

Library Developers from the SBS Lab:

Yangyang Fu Katy Hinkelman Dan Li Thomas Alonso Sevilla

Wei Tian Wangda Zuo

Related Journal Publications:

X. Han, W. Tian, J. VanGilder, W. Zuo, C. Faulkner 2020. "An Open Source Fast Fluid Dynamics Model for Data Center Thermal Management ." Energy and Buildings, 230, pp 110599.
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.
Y. Fu, X. Han, K. Baker, W. Zuo 2020. “Assessments of Data Centers for Provision of Frequency Regulation.” Applied Energy, 277, pp 115621.
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.
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.
X. Lu, K. Hinkelman, Y. Fu, J. Wang, W. Zuo, Q. Zhang, W. Saad 2019. “An Open Source Modeling Framework for Interdependent Energy-Transportation-Communication Infrastructure in Smart and Connected Communities.” IEEE Access, 7, pp. 55458-55476.
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.
Q. Wang, Y. Pan, Y. Fu, Z. Huang, W. Zuo, P. Xu 2019. "A Simulation-based Method for Air Loop Balancing and Fan Sizing Using Uncertainty and Sensitivity Analysis." Building Simulation, 12 (2), pp. 247-258.
G. Zhou, Y. Ye, W. Zuo, X. Zhou, X. Xu 2018. “Fast and Efficient Prediction of Finned-Tube Heat Exchanger Performance Using Wet-Dry Transformation Method with Nominal Data.” Applied Thermal Engineering, 145, pp. 133-146.
S. Huang, W. Wang, M. Brambley, S. Goyal, W. Zuo 2018. “An Agent-based Hardware-in-the-Loop Simulation Framework for Building Controls.” Energy and Buildings, 181, pp. 26-37.
G. Zhou, Y. Ye, J. Wang, W. Zuo, Y. Fu, X. Zhou 2018. “Modeling Air-to-Air Plate-Fin Heat Exchanger Without Dehumidification.” Applied Thermal Engineering, 143, pp. 137-148.
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.
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.
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.
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.
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.
T. S. Nouidui, M. Wetter, W. Zuo 2014. "Functional Mock-up Unit for Co-Simulation Import in EnergyPlus." Journal of Building Performance Simulation, 7(3), pp. 192-202.
M. Wetter, W. Zuo, T. S. Nouidui, X. Pang 2014. "Modelica Buildings Library." Journal of Building Performance Simulation, 7(4), pp. 253-270.

Related Conference Proceedings:

J. Wang, K.N. Garifi, K.A. Baker, W. Zuo, Y. Zhang 2020. “Optimal Operation for Resilient Communities Through A Hierarchical Load Scheduling Framework .” 2020 Building Performance Modeling Conference and SimBuild, Virtual Conference, September 29-October 1.
Y. Fu, W. Zuo, K. Baker 2020. “Multi-market Optimization of a Data Center without Storage.” American Modelica Conference 2020, Virtual Conference, September 22-24.
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.
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.
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.
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.
Q. Wang, Y. Pan, Z. Huang, W. Zuo, P. Xu 2018. “A Case Study of Simulation-Based PID Tuning Method for a Room Temperature Controller Using Uncertainty Analysis.” The 4^th Asia Conference of International Building Performance Simulation Association - ASim2018, December 3-5, Hong Kong.
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.
X. Lu, Y. Fu, W. Zuo 2018. “Modeling of Smart Community Infrastructure Accounting for the Interdependencies Among Energy, Transportation and Communication Networks.” 2018 ASHRAE Building Performance Analysis Conference and SimBuild (BPACS 2018), pp. 250-257, September 26-28, Chicago, IL.
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.
G. Zhou, Y. Ye, W. Zuo, X. Zhou 2018. “Modelling Air-To-Air Plate-Fin Heat Exchanger Without Condensation.” Proceedings of the 4th International Conference on Building Energy and Environment (COBEE2018), pp. 597-602, February 5-9, Melbourne, Australia.
G. Zhou, Y. Ye, W. Zuo, X. Zhou 2017. “Modelling Water-Cooled Air Coolers Under Wet-Cooing Conditions. (in Chinese)” Chinese National Conference on HVAC Modeling, Guangzhou, China, December 6-8, 2017.
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.
D. He, S. Huang, W. Zuo, R. Kaiser 2016. “Demo Abstract: a Virtual Testbed for Net Zero Energy Communities .” Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys 2016), pp. 211-212, November 16-17, Palo Alto, CA.
R. Sterling, T. Mueller, A. Andriamamonjy, A. Giretti, M. Bonvini, Z. O’Neill, M. Wetter, M. V. Cavey, A. Costa1, G. Boehme, W. Zuo, 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 3rd IBPSA-England Conference BSO 2016, September 12-14, Newcastle, UK.
D. He, S. Huang, W. Zuo, R. Kaiser 2016. “Towards to the Development of Virtual Testbed for Net Zero Energy Communities.” Proceedings of the ASHRAE and IBPSA-USA SimBuild 2016: Building Performance Modeling Conference, pp. 125-132, 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.
R. Sterling, A. Giretti, M. Bonvini, Z. O’Neill, M. Wetter, M. V. Cavey, A. Costa1, G. Boehme, W. Zuo, R. Klein, B. Dong, M. M. Keane 2015. “IEA Annex 60 Activity 2.3: Model Use During Operation, Approach and Case Studies .” Proceedings of the 14th Conference of International Building Performance Simulation Association (Building Simulation 2015), pp. 355-362, December 7-9, Hyderabad, India.
F. Niu, Z. O'Neill, W. Zuo, F. Li 2015. “Assessment of Different Data-Driven Algorithms for AHU Energy Consumption Predictions .” Proceedings of the 14th Conference of International Building Performance Simulation Association (Building Simulation 2015), pp. 1539-1546, December 7-9, Hyderabad, India.
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.
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.
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.
E. Widl, B. Delinchan, S. Kuebler, D. Li, W. Mueller, V. Norrefeldt, T. S. Nouidui, S. Stratbuecker, M. Wetter, F. Wurtz, W. Zuo 2014. “Novel Simulation Concepts for Buildings and Community Energy Systems Based on the Functional Mock-up Interface Specification .” Proceedings of the 2014 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES), April 14, Berlin, Germany.
T. S. Nouidui, M. Wetter, W. Zuo 2013. “Functional Mock-Up Unit Import in EnergyPlus for Co-Simulation .” Proceedings of the 13th Conference of International Building Performance Simulation Association (Building Simulation 2013), pp. 3275-3282, August 26-28, Chambery, France.
D. Kim, W. Zuo, J. E. Braun, M. Wetter 2013. “Comparisons of Building System Modeling Approaches for Control System Design .” Proceedings of the 13th Conference of International Building Performance Simulation Association (Building Simulation 2013), pp. 3267-3274, August 26-28, Chambery, France.
T. S. Nouidui, K. Phalak, W. Zuo, M. Wetter 2012. “Validation and Application of the Room Model of the Modelica Buildings Library .” Proceedings of the 9th International Modelica Conference, pp. 727-736, September 3-5, Munich, Germany.
T. S. Nouidui, M. Wetter, W. Zuo 2012. “Validation of the Window Model of the Modelica Buildings Library.” Proceedings of the 5th National Conference of International Building Performance Simulation Association-USA (SimBuild2012), pp. 529-536, August 1-3, Madison, WI.
M. Wetter, W. Zuo, T. S. Nouidui 2011. “Modeling of Heat Transfer in Rooms in the Modelica “Buildings” Library.” Proceedings of the 12th Conference of International Building Performance Simulation Association (Building Simulation 2011), pp. 1096-1103, November 14-16, Sydney, Australia.
W. Zuo, M. Wetter 2011. “Advanced Simulations of Building Energy and Control Systems with an Example of Chilled Water Plant Modeling .” Proceedings of the 8th International Forum and Workshop on Combined Heat, Air, Moisture and Pollutant Simulations (CHAMPS 2011), March 20-22, Nanjing, China.
M. Wetter, W. Zuo, T. S. Nouidui 2011. “Recent Developments of the Modelica “Buildings” Library for Building Energy and Control Systems .” Proceedings of the 8th International Modelica Conference, pp. 266-275, March 20-22, Dresden, Germany.

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Smart and Connected Community (SCC) Library

Anonymous — Sat, 23 Mar 2019 19:06:29 +0000

Smart and Connected Community (SCC) Library Anonymous (not verified) Sat, 03/23/2019 - 13:06

This open source Modelica library contains an integrated modeling framework and component models for designing coupled energy, transportation, and communication systems. The framework features a multi-level, multi-layer, multi-agent (3M) approach in order to enable flexible modeling of the interconnected systems. Various component and system-level models are included as the testbed of future SCCs in order to assess the impact of infrastructure interdependencies during typical operation. This modeling framework can be further extended for various modeling purposes and use cases, such as dynamic modeling and optimization, resilience analysis, and integrated decision making in future connected communities.

Software Download

The development site of this software is at: https://bitbucket.org/sbslab-zuo/scc-smart-city.

Release Notes

07/28/2020: SCC-Smart-City library V1.1 released.
- Updates the case study models and includes the tutorial models.
03/27/2019: SCC-Smart-City library V1.0 released.
- First release of the library. It contains component and system models supporting city-scale interdependent energy, transportation, and communication system modeling.

Acknowledgement

The SCC library is the outome of the project "BIGDATA: Collaborative Research: IA: Big Data Analytics for Optimized Planning of Smart, Sustainable, and Connected Communities" sponsored by the National Science Foundation (Awad No. IIS-1802017). The project website is here.

Related Publications

X. Lu, K. Hinkelman, Y. Fu, J. Wang, W. Zuo, Q. Zhang, W. Saad 2019. “An Open Source Modeling Framework for Interdependent Energy-Transportation-Communication Infrastructure in Smart and Connected Communities.” IEEE Access, 7, pp. 55458-55476.

X. Lu, Y. Fu, W. Zuo 2018. “Modeling of Smart Community Infrastructure Accounting for the Interdependencies among Energy, Transportation and Communication Networks.” 2018 ASHRAE Building Performance Analysis Conference and SimBuild (BPACS 2018), pp. 250-257, September 26-28, Chicago, IL.

Presentations

K. Hinkelman 2020 "A Modeling Framework to Evaluate Energy, Transportation, and Communication Interdependence in Smart and Connected Communities", American Modelica Conference 2020, September.

Workshop

J. Wang, S. Huang, W. Zuo 2020 "Cyber-Physical System Modeling using Modelica for Smart and Sustainable Communities", the free workshop on "Cyber-physical System Modeling using Modelica for Smart and Sustainable Communities" on September 18.

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Net Zero Energy Community (NZEC) Library

Anonymous — Sat, 23 Mar 2019 19:05:33 +0000

Net Zero Energy Community (NZEC) Library Anonymous (not verified) Sat, 03/23/2019 - 13:05

A open source Modelica library for the NZEC is built to facilitate the design and operation of a real NZEC. Using this library, a virtual testbed is built based on Historic Green Village, a real-world NZEC on Anna Maria Island, Florida. The testbed consists of a framework and system models for different subsystems, including solar photovoltaic (PV) systems, ground-coupled source heat pumps, buildings, the electric grid, and so on. The framework streamlines the process for simulation and optimization with Python; the models include both physics-based ones and data-driven ones, designed for different data availability and application contexts. The models are validated against the measurement data.

Software Download

The development site of this software is at: https://bitbucket.org/sbslab-zuo/scc-nzec.

Training Workshop

J. Wang, S. Huang, W. Zuo 2020 "Cyber-Physical System Modeling using Modelica for Smart and Sustainable Communities", the free workshop on "Cyber-physical System Modeling using Modelica for Smart and Sustainable Communities" on September 18.

Release Notes

07/28/2020: SCC-NZEC library V1.1 released.
- Added new package OccupantBehavior which enables occupancy-dependent schedule generation for thermostat setpoints, etc.
03/27/2019: SCC-NZEC library V1.0 released.
- First release of the library. It contains component and system models supporting community-scale building load, domestic hot water, heat pump system and power source modeling.

Acknowledgment

The NZEC library was the outcome of the project "BIGDATA: Collaborative Research: IA: Big Data Analytics for Optimized Planning of Smart, Sustainable, and Connected Communities" sponsored by the National Science Foundation (Award No. IIS-1802017).

The development of the library was also supported by the U.S. Department of Energy, Energy Efficiency and Renewable Energy, Building Technologies Office, under contract no. DE-AC05-76RL01830.

Related 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. "Occupant Preference-Aware Load Scheduling for Resilient Communities" Energies, 13, pp 5683.
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.
D. He, S. Huang, W. Zuo, R. Kaiser 2016. “Demo Abstract: a Virtual Testbed for Net Zero Energy Communities.” Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys 2016), pp. 211-212, November 16-17, Palo Alto, CA.
D. He, S. Huang, W. Zuo, R. Kaiser 2016. “Towards to the Development of Virtual Testbed for Net Zero Energy Communities.” Proceedings of the ASHRAE and IBPSA-USA SimBuild 2016: Building Performance Modeling Conference, pp. 125-132, August 8-12, Salt Lake City, UT.

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End-to-End Modeling and Optimization Package for Data Center Cooling

Anonymous — Sat, 23 Mar 2019 18:54:23 +0000

End-to-End Modeling and Optimization Package for Data Center Cooling Anonymous (not verified) Sat, 03/23/2019 - 12:54

This package is an outcome of our data center project sponsored by the DOE. See this research page for more information.

Modelica models package for data center cooling systems

4 system models and 30+ component models for data center cooling systems have been released in the DOE’s Modelica Buildings library (V5.0.0): Buildings.Applications.DataCenters.

ISAT-FFD model for data center airflow management

ISAT-FFD is a reduced order model trained by FFD simulations that can be used for fast predictions of airflow in the building.

Integrated model of data center airflow management and cooling systems

The integrated model that couples Modelica cooling system model with ISAT-FFD for airflow mangement, which can be regarded as an alternated detailed room model in Modelica Building library.

Software Download

The development site of the Modelica models package is at: https://github.com/lbl-srg/modelica-buildings/tree/master/Buildings/Applications .

The development site of the ISAT-FFD model is at: https://github.com/doetools/isat_ffd.

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

The mesh tool “SCI” program for Fast Fluid Dynamics in Modelica Buildings Library: https://github.com/FastFluidDynamics/Mesh.

Tutorial

This guide describes how to compile and run ISAT cases in Linux.

This guide shows how to compile the ISAT libraries using free software in Windows.

This guide demonstrates how to compile the FFD CPU and GPU codes in Windows.

A guide for pre-processing with SCI can be found here.

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Tools

Building Energy Models for Commercial Buildings Based on CBECS Data

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Acknowledgment

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Related Publications:

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Fast Fluid Dynamics Model

Game Style Demo of FFD Code

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Modelica Buildings Library

Library Developers from the SBS Lab:

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Smart and Connected Community (SCC) Library

Software Download

Release Notes

07/28/2020: SCC-Smart-City library V1.1 released.

03/27/2019: SCC-Smart-City library V1.0 released.

Acknowledgement

Related Publications

Presentations

Workshop

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Net Zero Energy Community (NZEC) Library

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End-to-End Modeling and Optimization Package for Data Center Cooling

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