@article {31305, title = {Translating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in California}, journal = {Applied Energy}, volume = {225}, year = {2018}, month = {09/2018}, pages = {522 - 534}, abstract = {

Climate change and increased electrification of space and water heating in buildings can significantly affect future electricity demand and hourly demand profiles, which has implications for electric grid greenhouse gas emissions and capacity requirements. We use EnergyPlus to quantify building energy demand under historical and under several climate change projections of 32 kinds of building prototypes in 16 different climate zones of California and imposed these impacts on a year 2050 electric grid configuration by simulation in the Holistic Grid Resource Integration and Deployment (HIGRID) model. We find that climate change only prompted modest increases in grid resource capacity and negligible difference in greenhouse gas emissions since the additional electric load generally occurred during times with available renewable generation. Heating electrification, however, prompted a 30{\textendash}40\% reduction in greenhouse gas emissions but required significant grid resource capacity increases, due to the higher magnitude of load increases and lack of readily available renewable generation during the times when electrified heating loads occurred. Overall, this study translates climate change and electrification impacts to system-wide endpoint impacts on future electric grid configurations and highlights the complexities associated with translating building-level impacts to electric system-wide impacts.

}, keywords = {Building Energy Demand, Climate Change Impacts, electric grid, Heating Electrification Effects}, issn = {03062619}, doi = {10.1016/j.apenergy.2018.05.003}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261918306962https://api.elsevier.com/content/article/PII:S0306261918306962?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0306261918306962?httpAccept=text/plain}, author = {Brian Tarroja and Felicia Chiang and Amir AghaKouchak and Scott Samuelsen and Shuba V. Raghavan and Max Wei and Kaiyu Sun and Tianzhen Hong} } @article {30728, title = {Dynamic equation-based thermo-hydraulic pipe model for district heating and cooling systems}, journal = {Energy Conversion and Management}, volume = {151}, year = {2017}, abstract = {

Simulation and optimisation of district heating and cooling networks requires efficient and realistic models of the individual network elements in order to correctly represent heat losses or gains, temperature propagation and pressure drops. Due to more recent thermal networks incorporating meshing decentralised heat and cold sources, the system often has to deal with variable temperatures and mass flow rates, with flow reversal occurring more frequently. This paper presents the mathematical derivation and software implementation in Modelica of a thermo-hydraulic model for thermal networks that meets the above requirements and compares it to both experimental data and a commonly used model. Good correspondence between experimental data from a controlled test set-up and simulations using the presented model was found. Compared to measurement data from a real district heating network, the simulation results led to a larger error than in the controlled test set-up, but the general trend is still approximated closely and the model yields results similar to a pipe model from the Modelica Standard Library. However, the presented model simulates 1.7 (for low number of volumes) to 68 (for highly discretized pipes) times faster than a conventional model for a realistic test case. A working implementation of the presented model is made openly available within the IBPSA Modelica Library. The model is robust in the sense that grid size and time step do not need to be adapted to the flow rate, as is the case in finite volume models.

}, doi = {10.1016/j.enconman.2017.08.072}, author = {Brahm van der Heijde and Marcus Fuchs and Carles Ribas Tugores and Gerald Schweiger and Kevin Sartor and Daniele Basciotti and Dirk Muller and Christoph Nytsch-Geusen and Michael Wetter and Lieve Helsen} } @article {29854, title = {Small and Medium Building Efficiency Toolkit and Community Demonstration Program}, year = {2017}, month = {03/2017}, abstract = {

Small commercial buildings in the United States consume 47 percent of all primary energy consumed in the building sector. Retrofitting small and medium commercial buildings may pose a steep challenge for owners, as many lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. To address this problem, this project developed the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit that calculates the energy use of a building, identifies and evaluates retrofit measures based on energy savings, energy cost savings, and payback. The CBES Toolkit includes a web app for end users and the CBES Application Programming Interface for integrating CBES with other energy software tools. The toolkit provides a rich feature set, including the following:

  1. Energy Benchmarking providing an Energy Star score
  2. Load Shape Analysis to identify potential building operation improvements
  3. Preliminary Retrofit Analysis which uses a custom developed pre-simulated database
  4. Detailed Retrofit Analysis which utilizes real time EnergyPlus simulations

In a parallel effort the project team developed technologies to measure outdoor airflow rate; commercialization and use would avoid both excess energy use from over ventilation and poor indoor air quality resulting from under ventilation.

If CBES is adopted by California{\textquoteright}s statewide small office and retail buildings, by 2030 the state can anticipate 1,587 gigawatt hours of electricity savings, 356 megawatts of non-coincident peak demand savings, 30.2 megatherms of natural gas savings, $227 million of energy-related cost savings, and reduction of emissions by 757,866 metric tons of carbon dioxide equivalent. In addition, consultant costs will be reduced in the retrofit analysis process.

CBES contributes to the energy savings retrofit field by enabling a straightforward and uncomplicated decision-making process for small and medium business owners and leveraging different levels of assessment to match user background, preference, and data availability.

}, keywords = {CBES, commercial buildings, energy efficiency, energy modeling, energy savings, indoor air quality, indoor environmental quality, outdoor air measurement technology, outdoor airflow intake rate, retrofit, ventilation rate}, doi = {10.7941/S93P70}, author = {Mary Ann Piette and Tianzhen Hong and William J. Fisk and Norman Bourassa and Wanyu R. Chan and Yixing Chen and H.Y. Iris Cheung and Toshifumi Hotchi and Margarita Kloss and Sang Hoon Lee and Phillip N. Price and Oren Schetrit and Kaiyu Sun and Sarah C. Taylor-Lange and Rongpeng Zhang} } @article {30312, title = {Spatial Distribution of Internal Heat Gains: A Probabilistic Representation and Evaluation of Its Influence on Cooling Equipment Sizing in Large Office Buildings}, journal = {Energy and Buildings}, year = {2017}, abstract = {

Internal heat gains from occupants, lighting, and plug loads are significant components of the space cooling load in an office building. Internal heat gains vary with time and space. The spatial diversity is significant, even for spaces with the same function in the same building. The stochastic nature of internal heat gains makes determining the peak cooling load to size air-conditioning systems a challenge. The traditional conservative practice of considering the largest internal heat gain among spaces and applying safety factors overestimates the space cooling load, which leads to oversized air-conditioning equipment and chiller plants. In this study, a field investigation of several large office buildings in China led to the development of a new probabilistic approach that represents the spatial diversity of the design internal heat gain of each tenant as a probability distribution function. In a large office building, a central chiller plant serves all air handling units (AHUs), with each AHU serving one or more floors of the building. Therefore, the spatial diversity should be considered differently when the peak cooling loads to size the AHUs and chillers are calculated. The proposed approach considers two different levels of internal heat gains to calculate the peak cooling loads and size the AHUs and chillers in order to avoid oversizing, improve the overall operating efficiency, and thus reduce energy use.

}, keywords = {air handling unit, chiller plant, equipment sizing, internal heat gain, spatial distribution, spatial diversity, stochastic}, doi = {10.1016/j.enbuild.2017.01.044}, author = {Qi Zhang and Da Yan and Jingjing An and Tianzhen Hong and Wei Tian and Kaiyu Sun} } @article {60962, title = {Advances in research and applications of energy-related occupant behavior in buildings}, journal = {Energy and Buildings}, volume = {116}, year = {2016}, month = {03/2016}, pages = {694-702}, abstract = {

Occupant behavior is one of the major factors influencing building energy consumption and contributing to uncertainty in building energy use prediction and simulation. Currently the understanding of occupant behavior is insufficient both in building design, operation and retrofit, leading to incorrect simplifications in modeling and analysis. This paper introduced the most recent advances and current obstacles in modeling occupant behavior and quantifying its impact on building energy use. The major themes include advancements in data collection techniques, analytical and modeling methods, and simulation applications which provide insights into behavior energy savings potential and impact. There has been growing research and applications in this field, but significant challenges and opportunities still lie ahead.

}, keywords = {Behavior Modeling, Building design and operation, building performance simulation, energy use, occupant behavior}, doi = {10.1016/j.enbuild.2015.11.052}, author = {Tianzhen Hong and Sarah C. Taylor-Lange and Simona D{\textquoteright}Oca and Da Yan and Stefano P. Corgnati} } @article {60965, title = {Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance}, journal = {Energy}, year = {2015}, month = {07/2015}, chapter = {738}, abstract = {

Small and medium-sized commercial buildings can be retrofitted to significantly reduce their energy use, however it is a huge challenge as owners usually lack of the expertise and resources to conduct detailed on-site energy audit to identify and evaluate cost-effective energy technologies. This study presents a DEEP (database of energy efficiency performance) that provides a direct resource for quick retrofit analysis of commercial buildings. DEEP, compiled from the results of about ten million EnergyPlus simulations, enables an easy screening of ECMs (energy conservation measures) and retrofit analysis. The simulations utilize prototype models representative of small and mid-size offices and retails in California climates. In the formulation of DEEP, large scale EnergyPlus simulations were conducted on high performance computing clusters to evaluate hundreds of individual and packaged ECMs covering envelope, lighting, heating, ventilation, air-conditioning, plug-loads, and service hot water. The architecture and simulation environment to create DEEP is flexible and can expand to cover additional building types, additional climates, and new ECMs. In this study DEEP is integrated into a web-based retrofit toolkit, the Commercial Building Energy Saver, which provides a platform for energy retrofit decision making by querying DEEP and unearthing recommended ECMs, their estimated energy savings and financial payback.

}, keywords = {building simulation, Energy conservation measure, energy modeling, energyplus, High Performance computing, retrofit}, doi = {10.1016/j.energy.2015.07.107}, author = {Sang Hoon Lee and Tianzhen Hong and Mary Ann Piette and Geof Sawaya and Yixing Chen and Sarah C. Taylor-Lange} } @article {60092, title = {Assessment of the Potential to Achieve Very Low Energy Use in Public Buildings in China with Advanced Window and Shading Systems}, journal = {Buildings}, volume = {5}, year = {2015}, month = {05/2015}, pages = {668-699}, chapter = {668}, abstract = {

As rapid growth in the construction industry continues to occur in China, the increased demand for a higher standard living is driving significant growth in energy use and demand across the country. Building codes and standards have been implemented to head off this trend, tightening prescriptive requirements for fenestration component measures using methods similar to the US model energy code American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 90.1. The objective of this study is to (a) provide an overview of applicable code requirements and current efforts within China to enable characterization and comparison of window and shading products, and (b) quantify the load reduction and energy savings potential of several key advanced window and shading systems, given the divergent views on how space conditioning requirements will be met in the future.

System-level heating and cooling loads and energy use performance were evaluated for a code-compliant large office building using the EnergyPlus building energy simulation program. Commercially-available, highly-insulating, low-emittance windows were found to produce 24-66\% lower perimeter zone HVAC electricity use compared to the mandated energy-efficiency standard in force (GB 50189-2005) in cold climates like Beijing. Low-e windows with operable exterior shading produced up to 30-80\% reductions in perimeter zone HVAC electricity use in Beijing and 18-38\% reductions in Shanghai compared to the standard. The economic context of China is unique since the cost of labor and materials for the building industry is so low. Broad deployment of these commercially available technologies with the proper supporting infrastructure for design, specification, and verification in the field would enable significant reductions in energy use and greenhouse gas emissions in the near term.

}, keywords = {building, China, energy efficiency, shading, windows}, doi = {10.3390/buildings5020668}, author = {Eleanor S. Lee and Xiufeng Pang and Andrew McNeil and Sabine Hoffmann and Anothai Thanachareonkit and Zhengrong Li and Yong Ding} } @article {61107, title = {Commercial Building Energy Saver: An energy retrofit analysis toolkit}, journal = {Applied Energy}, volume = {159}, year = {2015}, month = {9/2015}, chapter = {298}, abstract = {

Small commercial buildings in the United States consume 47\% of the total primary energy of the buildings sector. Retrofitting small and medium commercial buildings poses a huge challenge for owners because they usually lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper presents the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit, which calculates the energy use of a building, identifies and evaluates retrofit measures in terms of energy savings, energy cost savings and payback. The CBES Toolkit includes a web app (APP) for end users and the CBES Application Programming Interface (API) for integrating CBES with other energy software tools. The toolkit provides a rich set of features including: (1) Energy Benchmarking providing an Energy Star score, (2) Load Shape Analysis to identify potential building operation improvements, (3) Preliminary Retrofit Analysis which uses a custom developed pre-simulated database and, (4) Detailed Retrofit Analysis which utilizes real-time EnergyPlus simulations. CBES includes 100 configurable energy conservation measures (ECMs) that encompass IAQ, technical performance and cost data, for assessing 7 different prototype buildings in 16 climate zones in California and 6 vintages. A case study of a small office building demonstrates the use of the toolkit for retrofit analysis. The development of CBES provides a new contribution to the field by providing a straightforward and uncomplicated decision making process for small and medium business owners, leveraging different levels of assessment dependent upon user background, preference and data availability.

}, keywords = {Building Technologies Department, Building Technology and Urban Systems Division, buildings, buildings energy efficiency, Commercial Building Systems, conservation measures, energy efficiency, energy use, energyplus, External, Retrofit Energy, simulation research}, doi = {10.1016/j.apenergy.2015.09.002}, author = {Tianzhen Hong and Mary Ann Piette and Yixing Chen and Sang Hoon Lee and Sarah C. Taylor-Lange and Rongpeng Zhang and Kaiyu Sun and Phillip N. Price} } @article {60955, title = {Energy retrofit analysis toolkit for commercial buildings: A review}, volume = {89}, year = {2015}, month = {09/2015}, pages = {1087-1100}, publisher = {Elsevier Ltd.}, chapter = {1087}, abstract = {

Retrofit analysis toolkits can be used to optimize energy or cost savings from retrofit strategies, accelerating the adoption of ECMs (energy conservation measures) in buildings. This paper provides an up-todate review of the features and capabilities of 18 energy retrofit toolkits, including ECMs and the calculation engines. The fidelity of the calculation techniques, a driving component of retrofit toolkits, were evaluated. An evaluation of the issues that hinder effective retrofit analysis in terms of accessibility, usability, data requirement, and the application of efficiency measures, provides valuable insights into advancing the field forward. Following this review the general concepts were determined: (1) toolkits developed primarily in the private sector use empirically data-driven methods or benchmarking to provide ease of use, (2) almost all of the toolkits which used EnergyPlus or DOE-2 were freely accessible, but suffered from complexity, longer data input and simulation run time, (3) in general, there appeared to be a fine line between having too much detail resulting in a long analysis time or too little detail which sacrificed modeling fidelity. These insights provide an opportunity to enhance the design and development of existing and new retrofit toolkits in the future.

}, keywords = {Building energy retrofit, Energy conservation measures, Energy efficiency, Energy simulation, Retrofit analysis tools, Web-based applications}, doi = {10.1016/j.energy.2015.06.112}, author = {Sang Hoon Lee and Tianzhen Hong and Mary Ann Piette and Sarah C. Taylor-Lange} } @article {60948, title = {Occupant Behavior Modeling for Building Performance Simulation: Current State and Future Challenges}, journal = {Energy and Buildings}, volume = {107}, year = {2015}, month = {11/2015}, pages = {264-278}, abstract = {

Occupant behavior is now widely recognized as a major contributing factor to uncertainty of building performance. While a surge of research on the topic has occurred over the past four decades, and particularly the past few years, there are many gaps in knowledge and limitations to current methodologies. This paper outlines the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools. Major themes include the need for greater rigor in experimental methodologies; detailed, honest, and candid reporting of methods and results; and development of an efficient means to implement occupant behavior models and integrate them into building energy modeling programs.

}, keywords = {building simulation, energy efficiency, energy modeling, energy use, occupant behavior}, doi = {10.1016/j.enbuild.2015.08.032}, author = {Da Yan and William O{\textquoteright}Brien and Tianzhen Hong and Xiaohang Feng and H. Burak Gunay and Farhang Tahmasebi and Ardeshir Mahdavi} } @article {60959, title = {An Ontology to Represent Energy-Related Occupant Behavior in Buildings. Part II: Implementation of the DNAS framework using an XML schema}, journal = {Building and Environment}, volume = {94}, year = {2015}, month = {08/2015}, pages = {196-205}, abstract = {

Energy-related occupant behavior in buildings is difficult to define and quantify, yet critical to our understanding of total building energy consumption. Part I of this two-part paper introduced the DNAS (Drivers, Needs, Actions and Systems) framework, to standardize the description of energy-related occupant behavior in buildings. Part II of this paper implements the DNAS framework into an XML (eXtensible Markup Language) schema, titled {\textquoteleft}occupant behavior XML{\textquoteright} (obXML). The obXML schema is used for the practical implementation of the DNAS framework into building simulation tools. The topology of the DNAS framework implemented in the obXML schema has a main root element OccupantBehavior, linking three main elements representing Buildings, Occupants and Behaviors. Using the schema structure, the actions of turning on an air conditioner and closing blinds provide two examples of how the schema standardizes these actions using XML. The obXML schema has inherent flexibility to represent numerous, diverse and complex types of occupant behaviors in buildings, and it can also be expanded to encompass new types of behaviors. The implementation of the DNAS framework into the obXML schema will facilitate the development of occupant information modeling (OIM) by providing interoperability between occupant behavior models and building energy modeling programs.

}, keywords = {building energy consumption, building simulation, energy modeling, obXML, occupant behavior, XML schema}, doi = {10.1016/j.buildenv.2015.08.006}, author = {Tianzhen Hong and Simona D{\textquoteright}Oca and Sarah C. Taylor-Lange and William J. N. Turner and Yixing Chen and Stefano P. Corgnati} } @article {59959, title = {An Ontology to Represent Energy-related Occupant Behavior in Buildings Part I: Introduction to the DNAs Framework}, journal = {Building and Environment}, volume = {92}, year = {2015}, month = {10/2015}, pages = {764-777}, abstract = {

Reducing energy consumption in the buildings sector requires significant changes, but technology alone may fail to guarantee efficient energy performance. Human behavior plays a pivotal role in building design, operation, management and retrofit, and is a crucial positive factor for improving the indoor environment, while reducing energy use at low cost. Over the past 40 years, a substantial body of literature has explored the impacts of human behavior on building technologies and operation. Often, need-action-event cognitive theoretical frameworks were used to represent human-machine interactions. In Part I of this paper a review of more than 130 published behavioral studies and frameworks was conducted. A large variety of data-driven behavioral models have been developed based on field monitoring of the human-building-system interaction. Studies have emerged scattered geographically around the world that lack in standardization and consistency, thus leading to difficulties when comparing one with another. To address this problem, an ontology to represent energy-related occupant behavior in buildings is presented. Accordingly, the technical DNAs framework is developed based on four key components: i) the Drivers of behavior, ii) the Needs of the occupants, iii) the Actions carried out by the occupants, and iv) the building systems acted upon by the occupants. This DNAs framework is envisioned to support the international research community to standardize a systematic representation of energy-related occupant behavior in buildings. Part II of this paper further develops the DNAs framework as an XML (eXtensible Markup Language) schema, obXML, for exchange of occupant information modeling and integration with building simulation tools.

}, keywords = {Building energy, human-building-system interaction, modeling, occupant behavior, ontology, simulation}, doi = {10.1016/j.buildenv.2015.02.019}, author = {Tianzhen Hong and Simona D{\textquoteright}Oca and William J. N. Turner and Sarah C. Taylor-Lange} } @article {60964, title = {A pattern-based automated approach to building energy model calibration}, year = {2015}, abstract = {

Building model calibration is critical in bringing simulated energy use closer to the actual consumption. This paper presents a novel, automated model calibration approach that uses logic linking parameter tuning with bias pattern recognition to overcome some of the disadvantages associated with traditional calibration processes. The pattern-based process contains four key steps: (1) running the original precalibrated energy model to obtain monthly simulated electricity and gas use; (2) establishing a pattern bias, either Universal or Seasonal Bias, by comparing load shape patterns of simulated and actual monthly energy use; (3) using programmed logic to select which parameter to tune first based on bias pattern, weather and input parameter interactions; and (4) automatically tuning the calibration parameters and checking the progress using pattern-fit criteria. The automated calibration algorithm was implemented in the Commercial Building Energy Saver, a web-based building energy retrofit analysis toolkit. The proof of success of the methodology was demonstrated using a case study of an office building located in San Francisco. The case study inputs included the monthly electricity bill, monthly gas bill, original building model and weather data with outputs resulting in a calibrated model that more closely matched that of the actual building energy use profile. The novelty of the developed calibration methodology lies in linking parameter tuning with the underlying logic associated with bias pattern identification. Although there are some limitations to this approach, the pattern-based automated calibration methodology can be universally adopted as an alternative to manual or hierarchical calibration approaches.

}, author = {Kaiyu Sun and Tianzhen Hong and Sarah C. Taylor-Lange and Mary Ann Piette} } @article {59963, title = {A Technical Framework to Describe Occupant Behavior for Building Energy Simulations}, year = {2014}, abstract = {

Green buildings that fail to meet expected design performance criteria indicate that technology alone does not guarantee high performance. Human influences are quite often simplified and ignored in the design, construction, and operation of buildings. Energy-conscious human behavior has been demonstrated to be a significant positive factor for improving the indoor environment while reducing the energy use of buildings. In our study we developed a new technical framework to describe energy-related human behavior in buildings. The energy-related behavior includes accounting for individuals and groups of occupants and their interactions with building energy services systems, appliances and facilities. The technical framework consists of four key components:

  1. the drivers behind energy-related occupant behavior, which are biological, societal, environmental, physical, and economical in nature
  2. the needs of the occupants are based on satisfying criteria that are either physical (e.g. thermal, visual and acoustic comfort) or non-physical (e.g. entertainment, privacy, and social reward)
  3. the actions that building occupants perform when their needs are not fulfilled
  4. the systems with which an occupant can interact to satisfy their needs

The technical framework aims to provide a standardized description of a complete set of human energy-related behaviors in the form of an XML schema. For each type of behavior (e.g., occupants opening/closing windows, switching on/off lights etc.) we identify a set of common behaviors based on a literature review, survey data, and our own field study and analysis. Stochastic models are adopted or developed for each type of behavior to enable the evaluation of the impact of human behavior on energy use in buildings, during either the design or operation phase. We will also demonstrate the use of the technical framework in assessing the impact of occupancy behavior on energy saving technologies. The technical framework presented is part of our human behavior research, a 5-year program under the U.S. - China Clean Energy Research Center for Building Energy Efficiency.

}, keywords = {building simulation, energy efficiency, framework, occupant behavior, XML schema}, author = {William J. N. Turner and Tianzhen Hong} } @article {56946, title = {The Two-Day CERC-BEE Forum on Building Integrated Design and Occupant Behavior: Presentations and Summary}, year = {2013}, author = {Tianzhen Hong and William J. N. Turner and Cheng Li} } @article {2583, title = {Multi-Criteria Optimisation using Past, Real Time and Predictive Performance Benchmarks}, journal = {Simulation Modelling Practice and Theory}, volume = {19}, year = {2011}, month = {04/2011}, pages = {1258-1265}, chapter = {1258}, author = {Torrens, J. Ignacio and Marcus Keane and Andrea Costa and James O{\textquoteright}Donnell} } @article {75, title = {Co-simulation for performance prediction of integrated building and HVAC systems - An analysis of solution characteristics using a two-body system}, journal = {Simulation Modelling Practice and Theory}, volume = {18}, year = {2010}, month = {08/2010}, pages = {957-970}, chapter = {957}, abstract = {

Integrated performance simulation of buildings and heating, ventilation and air-conditioning (HVAC) systems can help in reducing energy consumption and increasing occupant comfort. However, no single building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to analyze integrated building systems and to enable rapid prototyping of innovative building and system technologies. One way to alleviate this problem is to use co-simulation to integrate different BPS tools. Co-simulation approach represents a particular case of simulation scenario where at least two simulators solve coupled differential-algebraic systems of equations and exchange data that couples these equations during the time integration.

This article analyzes how co-simulation influences consistency, stability and accuracy of the numerical approximation to the solution. Consistency and zero-stability are studied for a general class of the problem, while a detailed consistency and absolute stability analysis is given for a simple two-body problem. Since the accuracy of the numerical approximation to the solution is reduced in co-simulation, the article concludes by discussing ways for how to improve accuracy.

}, doi = {10.1016/j.simpat.2010.02.011}, author = {Marija Trcka and Jan Hensen and Michael Wetter} } @article {68, title = {Co-simulation of innovative integrated HVAC systems in buildings}, journal = {Journal of Building Performance Simulation}, volume = {2}, year = {2009}, pages = {209-230}, chapter = {209}, abstract = {

Integrated performance simulation of buildings HVAC systems can help in reducing energy consumption and increasing occupant comfort. However, no single building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to analyze integrated building systems and to enable rapid prototyping of innovative building and system technologies. One way to alleviate this problem is to use co-simulation, as an integrated approach to simulation. This article elaborates on issues important for co-simulation realization and discusses multiple possibilities to justify the particular approach implemented in the here described co-simulation prototype. The prototype is validated with the results obtained from the traditional simulation approach. It is further used in a proof-of-concept case study to demonstrate the applicability of the method and to highlight its benefits. Stability and accuracy of different coupling strategies are analyzed to give a guideline for the required coupling time step.

}, keywords = {building performance simulation, co-simulation, hvac simulation, innovative building system modelling and simulation}, doi = {10.1080/19401490903051959}, url = {http://www.informaworld.com/smpp/section?content=a913244253\&fulltext=713240928}, author = {Marija Trcka and Jan Hensen and Michael Wetter} } @proceedings {60, title = {An implementation of co-simulation for performance prediction of innovative integrated HVAC systems in buildings}, journal = {Proc. of the 11th IBPSA Conference}, year = {2009}, month = {07/2009}, pages = {724-731}, address = {Glasgow, Scotland}, abstract = {Integrated performance simulation of buildings and heating, ventilation and air-conditioning (HVAC)systems can help reducing energy consumption and increasing level of occupant comfort. However, no singe building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to accommodate the ever-increasing complexity and rapid innovations in building and system technologies. One way to alleviate this problem is to use co-simulation. The co-simulation approach represents a particular case of simulation scenario where at least two simulators solve coupled differential-algebraic systems of equations and exchange data that couples these equations during the time integration. This paper elaborates on issues important for co-simulation realization and discusses multiple possibilities to justify the particular approach implemented in a co-simulation prototype. The prototype is verified and validated against the results obtained from the traditional simulation approach. It is further used in a case study for the proof-of-concept, to demonstrate the applicability of the method and to highlight its benefits. Stability and accuracy of different coupling strategies are analyzed to give a guideline for the required coupling frequency. The paper concludes by defining requirements and recommendations for generic co-simulation implementations.}, url = {http://www.ibpsa.org/proceedings/BS2009/BS09_0724_731.pdf}, author = {Marija Trcka and Michael Wetter and Jan Hensen} } @conference {3383, title = {Multi-criteria optimisation using past, historical, real time and predictive performance benchmarks}, booktitle = {SEEP 2009: 3rd International Conference on Sustainable Energy \& Environmental Protection}, year = {2009}, author = {Torrens, J. Ignacio and Marcus Keane and James O{\textquoteright}Donnell and Andrea Costa} } @conference {3390, title = {Comparison of Co-Simulation Approaches for Building and HVAC/R System Simulation}, booktitle = {Proc. Building Simulation 2007}, year = {2007}, month = {09/2007}, address = {Beijing, China}, author = {Marija Trcka and Michael Wetter and Jan Hensen} } @proceedings {63, title = {Comparison of co-simulation approaches for building and HVAC/R system simulation.
}, journal = {Proc. of the 10th IBPSA Conference}, year = {2007}, month = {09/2007}, pages = {1418-1425}, address = {Beijing, China}, abstract = {Appraisal of modern performance-based energy codes, as well as heating, ventilation, air- conditioning and refrigeration (HVAC/R) system*design require use of an integrated building and system performance simulation program. However, the required scope of the modeling library of such integrated tools often goes beyond those offered in available simulation programs. One remedy for this situation would be to develop the required models in an existing simulation program. However, due to the lack of model interoperability, the model would not be available in other simulation programs. We suggest co-simulation for HVAC/R system simulation as an approach to alleviate the above issues. In co-simulation, each subsystem is modeled and simulated in the appropriate simulation program, potentially on different computers, and intermediate results are communicated over the network during execution time. We discuss different co-simulation approaches and give insights into specific prototypes. Based on the prototypes, we compare the approaches in terms of accuracy, stability and execution time, using a simple case study. We finish with results discussions and recommendations on how to perform co-simulation to maintain the required accuracy of simulation results.}, url = {http://www.ibpsa.org/proceedings/BS2007/p503_final.pdf}, author = {Marija Trcka and Michael Wetter and Jan Hensen} } @conference {2660, title = {Simulation of Energy Management Systems in EnergyPlus}, booktitle = {Proc. Building Simulation 2007}, year = {2007}, month = {09/2007}, address = {Beijing, China}, author = {Peter G. Ellis and Paul A. Torcellini and Drury B. Crawley} } @proceedings {2794, title = {Assessment of the Technical Potential for Achieving Zero-Energy Commercial Buildings}, journal = {2006 ACEEE Summer Study on Energy Efficiency in Buildings}, year = {2006}, month = {08/2006}, author = {Brent T. Griffith and Paul A. Torcellini and John Ryan} } @proceedings {2795, title = {Automated Multivariate Optimization Tool for Energy Analysis}, journal = {SimBuild 2006}, year = {2006}, month = {08/2006}, address = {Cambridge, MA, USA}, author = {Peter G. Ellis and Brent T. Griffith and Nicholas Long and Paul A. Torcellini} } @article {378, title = {Characterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids}, journal = {International Journal of Heat and Mass Transfer}, volume = {49}, year = {2006}, month = {08/2006}, pages = {2950-2956}, chapter = {2950}, abstract = {

The temperature oscillation technique to measure the thermal diffusivity of a fluid consists of filling a cylindrical volume with the fluid, applying an oscillating temperature boundary condition at the two ends of the cylinder, measuring the amplitude and phase of the temperature oscillation at any point inside the cylinder, and finally calculating the fluid thermal diffusivity from the amplitude and phase values of the temperature oscillations at the ends and at the point inside the cylinder. Although this experimental technique was introduced by Santucci and co-workers nearly two decades ago, its application is still limited, perhaps because of the perceived difficulties in obtaining accurate results. Here, we attempt to clarify this approach by first estimating the maximum size of the liquid{\textquoteright}s cylindrical volume, performing a systematic series of experiments to find the allowable amplitude and frequency of the imposed temperature oscillations, and then validating our experimental setup and the characterization method by measuring the thermal conductivity of pure water at different temperatures and comparing our results with previously published work.

}, keywords = {Temperature oscillation technique, Thermal conductivity, thermal diffusivity}, url = {http://www.sciencedirect.com/science/article/pii/S001793100600144X}, author = {Prajesh Bhattacharya and S. Nara and P. Vijayan and Tang, T. and W. Lai and Patrick E. Phelan and Ravi S. Prasher and David W. Song and J. Wang} } @article {378, title = {Characterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids}, journal = {International Journal of Heat and Mass Transfer}, volume = {49}, year = {2006}, month = {08/2006}, pages = {2950-2956}, chapter = {2950}, abstract = {

The temperature oscillation technique to measure the thermal diffusivity of a fluid consists of filling a cylindrical volume with the fluid, applying an oscillating temperature boundary condition at the two ends of the cylinder, measuring the amplitude and phase of the temperature oscillation at any point inside the cylinder, and finally calculating the fluid thermal diffusivity from the amplitude and phase values of the temperature oscillations at the ends and at the point inside the cylinder. Although this experimental technique was introduced by Santucci and co-workers nearly two decades ago, its application is still limited, perhaps because of the perceived difficulties in obtaining accurate results. Here, we attempt to clarify this approach by first estimating the maximum size of the liquid{\textquoteright}s cylindrical volume, performing a systematic series of experiments to find the allowable amplitude and frequency of the imposed temperature oscillations, and then validating our experimental setup and the characterization method by measuring the thermal conductivity of pure water at different temperatures and comparing our results with previously published work.

}, keywords = {Temperature oscillation technique, Thermal conductivity, thermal diffusivity}, url = {http://www.sciencedirect.com/science/article/pii/S001793100600144X}, author = {Prajesh Bhattacharya and S. Nara and P. Vijayan and Tang, T. and W. Lai and Patrick E. Phelan and Ravi S. Prasher and David W. Song and J. Wang} } @proceedings {2789, title = {Halfway to Zero Energy in a Large Office Building}, journal = {2006 ACEEE Summer Study on Energy Efficiency in Buildings}, year = {2006}, month = {08/2006}, address = {Pacific Grove, CA, USA}, author = {Mark Hanson and Steven Carlson and Dan Sammartano and Thomas Taylor} } @proceedings {2801, title = {Radiant Slab Cooling: A Case Study of Building Energy Performance}, journal = {SimBuild 2006}, year = {2006}, month = {08/2006}, address = {Cambridge, MA, USA}, author = {Zhen Tian and James A. Love} } @proceedings {2808, title = {Thermal Performance Simulation of an Atrium Building}, journal = {eSim 2006}, year = {2006}, month = {05/2006}, address = {Toronto, Canada}, author = {{\"O}zg{\"u}r G{\"o}{\c c}er and Aslihan Tavil and Ertan {\"O}zkan} } @proceedings {2790, title = {Zero Energy Buildings: A Critical Look at the Definition}, journal = {2006 ACEEE Summer Study on Energy Efficiency in Buildings}, year = {2006}, month = {08/2006}, address = {Pacific Grove, CA, USA}, author = {Paul A. Torcellini and Shanti Pless and Michael Deru and Drury B. Crawley} } @proceedings {2814, title = {Simulating Tall Buildings Using EnergyPlus}, journal = {IBPSA Building Simulation 2005}, year = {2005}, month = {08/2005}, address = {Montreal, Canada}, author = {Peter G. Ellis and Paul A. Torcellini} } @conference {390, title = {Evaluation of the Temperature Oscillation Technique to Calculate Thermal Conductivity of Water and Systematic Measurement of the Thermal Conductivity of Aluminum Oxide {\textendash} Water Nanofluiids}, booktitle = {International Mechanical Engineering Congress \& Exposition,}, year = {2004}, month = {11/2004}, address = {Anaheim, CA}, author = {Prajesh Bhattacharya and P. Vijayan and Tang, T. and S. Nara and Patrick E. Phelan and Ravi S. Prasher and J. Wang and David W. Song} } @conference {349, title = {Towards an Environment for HVAC Control System Evaluation}, booktitle = {USER-1 Building Simulation Conference}, series = {European Society for Computer Simulation}, year = {1988}, month = {09/1988}, address = {Ostend, Belgium}, author = {Philip Haves and Trewella, D.} }