Building performance simulation

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Building performance simulation model with input and some resulting output Building performance simulation.png
Building performance simulation model with input and some resulting output

Building performance simulation (BPS) is the replication of aspects of building performance using a computer-based, mathematical model created on the basis of fundamental physical principles and sound engineering practice. The objective of building performance simulation is the quantification of aspects of building performance which are relevant to the design, construction, operation and control of buildings. [1] Building performance simulation has various sub-domains; most prominent are thermal simulation, lighting simulation, acoustical simulation and air flow simulation. Most building performance simulation is based on the use of bespoke simulation software. Building performance simulation itself is a field within the wider realm of scientific computing.

Contents

Introduction

From a physical point of view, a building is a very complex system, influenced by a wide range of parameters. A simulation model is an abstraction of the real building which allows to consider the influences on high level of detail and to analyze key performance indicators without cost-intensive measurements. BPS is a technology of considerable potential that provides the ability to quantify and compare the relative cost and performance attributes of a proposed design in a realistic manner and at relatively low effort and cost. Energy demand, indoor environmental quality (incl. thermal and visual comfort, indoor air quality and moisture phenomena), HVAC and renewable system performance, urban level modeling, building automation, and operational optimization are important aspects of BPS. [2] [3] [4]

Simulation modeling is the process of creating and analyzing a digital prototype of a physical model to predict its performance in the real world. Simulation modeling is used to help designers and engineers understand whether, under what conditions, and in which ways a part could fail and what loads it can withstand. Simulation modeling can also help to predict fluid flow and heat transfer patterns. It analyses the approximate working conditions by applying the simulation software.

Thermal comfort is the condition of mind that expresses satisfaction with the thermal environment and is assessed by subjective evaluation. The human body can be viewed as a heat engine where food is the input energy. The human body will generate excess heat into the environment, so the body can continue to operate. The heat transfer is proportional to temperature difference. In cold environments, the body loses more heat to the environment and in hot environments the body does not exert enough heat. Both the hot and cold scenarios lead to discomfort. Maintaining this standard of thermal comfort for occupants of buildings or other enclosures is one of the important goals of HVAC design engineers. Most people will feel comfortable at room temperature, colloquially a range of temperatures around 20 to 22 °C, but this may vary greatly between individuals and depending on factors such as activity level, clothing, and humidity.

Indoor air quality Air quality within and around buildings and structures

Indoor air quality (IAQ) is the air quality within and around buildings and structures. IAQ is known to affect the health, comfort and well-being of building occupants. Poor indoor air quality has been linked to sick building syndrome, reduced productivity and impaired learning in schools.

Over the last six decades, numerous BPS computer programs have been developed. The most comprehensive listing of BPS software can be found in the BEST directory. [5] Some of them only cover certain parts of BPS (e.g. climate analysis, thermal comfort, energy calculations, plant modeling, daylight simulation etc.). The core tools in the field of BPS are multi-domain, dynamic, whole-building simulation tools, which provide users with key indicators such as heating and cooling load, energy demand, temperature trends, humidity, thermal and visual comfort indicators, air pollutants, ecological impact and costs. [4] [6]

A typical building simulation model has inputs for local weather; building geometry; building envelope characteristics; internal heat gains from lighting, occupants and equipment loads; heating, ventilation, and cooling (HVAC) system specifications; operation schedules and control strategies. [2] The ease of input and accessibility of output data varies widely between BPS tools. Advanced whole-building simulation tools are able to consider almost all of the following in some way with different approaches.

A building envelope is the physical separator between the conditioned and unconditioned environment of a building including the resistance to air, water, heat, light, and noise transfer.

Lighting deliberate use of light to achieve a practical or aesthetic effect

Lighting or illumination is the deliberate use of light to achieve practical or aesthetic effects. Lighting includes the use of both artificial light sources like lamps and light fixtures, as well as natural illumination by capturing daylight. Daylighting is sometimes used as the main source of light during daytime in buildings. This can save energy in place of using artificial lighting, which represents a major component of energy consumption in buildings. Proper lighting can enhance task performance, improve the appearance of an area, or have positive psychological effects on occupants.

Plug load is the energy used by products that are powered by means of an ordinary AC plug. This term generally excludes building energy that is attributed to major end uses

Necessary input data for a whole-building simulation:

Relative humidity

Relative humidity (RH) is the ratio of the partial pressure of water vapor to the equilibrium vapor pressure of water at a given temperature. Relative humidity depends on temperature and the pressure of the system of interest. The same amount of water vapor results in higher relative humidity in cool air than warm air. A related parameter is that of dew point.

Solar irradiance power per unit area received from the Sun in the form of electromagnetic radiation

Solar irradiance (SI) is the power per unit area, received from the Sun in the form of electromagnetic radiation as reported in the wavelength range of the measuring instrument. Solar irradiance is often integrated over a given time period in order to report the radiant energy emitted into the surrounding environment, during that time period. This integrated solar irradiance is called solar irradiation, solar exposure, solar insolation, or insolation.

Some examples for key performance indicators:

Other use of BPS software

History

The history of BPS is approximately as long as that of computers. The very early developments in this direction started in the late 50's and early 60's in the United States and Sweden. During this period, several methods had been introduced for analyzing single system components (e.g. gas boiler) using steady state calculations.The very first reported simulation tool for buildings was BRIS, introduced in 1963 by the Royal Institute of Technology in Stockholm. [7] Until the late 60's, several models with hourly resolution had been developed focusing on energy assessments and heating/cooling load calculations. This effort resulted in more powerful simulation engines released in the early 70's, among those were BLAST, DOE-2, ESP-r, HVACSIM+ and TRNSYS. [8] In the United States, the 1970's energy crisis intensified these efforts, as reducing the energy consumption of buildings became an urgent domestic policy interest. The energy crisis also initiated development of U.S. building energy standards, beginning with ASHRAE 90-75. [9]

The development of building simulation represents a combined effort between academia, governmental institutions, industry, and professional organizations. Over the past decades the building simulation discipline has matured into a field that offers unique expertise, methods and tools for building performance evaluation. Several review papers and state of the art analysis were carried out during that time giving an overview about the development. [10] [11] [12]

In the 1980s, a discussion about future directions for BPS among a group of leading building simulation specialists started. There was a consensus that most of the tools, that had been developed until then, were too rigid in their structure to be able to accommodate the improvements and flexibility that would be called for in the future. [13] Around this time, the very first equation-based building simulation environment ENET [14] was developed, which provided the foundation of SPARK. In 1989, Sahlin and Sowell presented a Neutral Model Format (NMF) for building simulation models, which is used today in the commercial software IDA ICE. [15] Four years later, Klein introduced the Engineering Equation Solver (EES) [16] and in 1997, Mattsson and Elmqvist reported on an international effort to design Modelica . [17]

BPS still presents challenges relating to problem representation, support for performance appraisal, enabling operational application, and delivering user education, training, and accreditation. Clarke (2015) describes a future vision of BPS with the following, most important tasks which should be addressed by the global BPS community. [18]

Accuracy

In the context of building simulation models, error refers to the discrepancy between simulation results and the actual measured performance of the building. There are normally occurring uncertainties in building design and building assessment, which generally stem from approximations in model inputs, such as occupancy behavior. Calibration refers to the process of "tuning" or adjusting assumed simulation model inputs to match observed data from the utilities or Building Management System (BMS). [19] [20] [21]

The number of publications dealing with accuracy in building modeling and simulation increased significantly over the past decade. Many papers report large gaps between simulation results and measurements, [22] [23] [24] [25] while other studies show that they can match very well. [26] [27] [28] The reliability of results from BPS depends on many different things, e.g. on the quality of input data, [29] the competence of the simulation engineers [30] and on the applied methods in the simulation engine. [31] [32] An overview about possible causes for the widely discussed performance gap from design stage to operation is given by de Wilde (2014) and a progress report by the Zero Carbon Hub (2013). Both conclude the factors mentioned above as the main uncertainties in BPS. [33] [34]

ASHRAE Standard 140-2017 "Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs (ANSI Approved)" provides a method to validate the technical capability and range of applicability of computer programs to calculate thermal performance. [35] ASHRAE Guideline 4-2014 provides performance indices criteria for model calibration. [36] The performance indices used are normalized mean bias error (NMBE), coefficient of variation (CV) of the root mean square error (RMSE), and R2 (coefficient of determination). ASHRAE recommends a R2 greater than 0.75 for calibrated models. The criteria for NMBE and CV RMSE depends on if measured data is available at a monthly or hourly timescale.

Technological aspects

Given the complexity of building energy and mass flows, it is generally not possible to find an analytical solution, so the simulation software employs other techniques, such as response function methods, or numerical methods in finite differences or finite volume, as an approximation. [2] Most of today's whole building simulation programs formulate models using imperative programming languages. These languages assign values to variables, declare the sequence of execution of these assignments and change the state of the program, as is done for example in C/C++, Fortran or MATLAB/Simulink. In such programs, model equations are tightly connected to the solution methods, often by making the solution procedure part of the actual model equations. [37] The use of imperative programming languages limits the applicability and extensibility of models. More flexibility offer simulation engines using symbolic Differential Algebraic Equations (DAEs) with general purpose solvers that increase model reuse, transparency and accuracy. Since some of these engines have been developed for more than 20 years (e.g. IDA ICE) and due to the key advantages of equation-based modeling, these simulation engines can be considered as state of the art technology. [38] [39]

Applications

Building simulation models may be developed for both new or existing buildings. Major use categories of building performance simulation include: [3]

Software tools

There are hundreds of software tools available for simulating the performance of buildings and building subsystems, which range in capability from whole-building simulations to model input calibration to building auditing. Among whole-building simulation software tools, it is important to draw a distinction between the simulation engine, which dynamically solves equations rooted in thermodynamics and building science, and the modeler application (interface). [6]

In general, BPS software can be classified into [41]

Contrary to this presentation, there are some tools that in fact do not meet these sharp classification criteria, such as ESP-r which can also be used as a modeler application for EnergyPlus [42] and there are also other applications using the IDA simulation environment, [43] which makes "IDA" the engine and "ICE" the modeler. Most modeler applications support the user with a graphical user interface to make data input easier. The modeler creates an input file for the simulation engine to solve. The engine returns output data to the modeler application or another visualization tool which in turn presents the results to the user. For some software packages, the calculation engine and the interface may be the same product. The table below gives an overview about commonly used simulation engines and modeler applications for BPS. [41] [44]

Simulation engineDeveloperfirst ReleaseTechnologyModeling LanguageLicenselatest VersionModeler applications and GUI
ApacheSim [45] Integrated Environmental Solutions Ltd., UKCommercial6.0VE 2018 [46]
Carrier HAP [47] United Technologies, USCommercial5.11Carrier HAP
DOE-2 [48] James J. Hirsch & Associates, US1978Freeware2.2eQuest, [49] RIUSKA, [50] EnergyPro, [51] GBS [52]
Energy+ [53] Lawrence Berkeley National Laboratory, US2001Freeware8.9.0DesignBuilder, [54] OpenStudio, [55] Many other [56]
ESP-r [57] University of Strathclyde, UK1974Freeware11.11ESP-r
IDA [39] EQUA Simulation AB, SE1998DAENMF, ModelicaCommercial4.8ICE, [39] ESBO [58]
SPARK [59] Lawrence Berkeley National Laboratory, US1986DAEFreeware2.01VisualSPARK
TAS [60] Environmental Design Solutions Limited, UKCommercial9.4.4TAS 3D Modeler
TRNSYS [61] University of Wisconsin-Madison, US1975FORTRAN, C/C++Commercial18.0Simulation Studio, [62] TRNBuild

BPS in practice

Since the 90's, building performance simulation has undergone the transition from a method used mainly for research to a design tool for mainstream industrial projects. However, the utilization in different countries still varies greatly. Building certification programs like LEED (USA), BREEAM (UK) or DGNB (Germany) showed to be a good driving force for BPS to find broader application. Also, national building standards that allow BPS based analysis are of good help for an increasing industrial adoption, such as in the United States (ASHRAE 90.1), [63] Sweden (BBR), [64] Switzerland (SIA) [65] and the United Kingdom (NCM). [66]

The Swedish building regulations are unique in that computed energy use has to be verified by measurements within the first two years of building operation. Since the introduction in 2007, experience shows that highly detailed simulation models are preferred by modelers to reliably achieve the required level of accuracy. Furthermore, this has fostered a simulation culture where the design predictions are close to the actual performance. This in turn has led to offers of formal energy guarantees based on simulated predictions, highlighting the general business potential of BPS. [67]

Performance-based compliance

In a performance-based approach, compliance with building codes or standards is based on the predicted energy use from a building simulation, rather than a prescriptive approach, which requires adherence to stipulated technologies or design features. Performance-based compliance provides greater flexibility in the building design as it allows designers to miss some prescriptive requirements if the impact on building performance can be offset by exceeding other prescriptive requirements. [68] The certifying agency provides details on model inputs, software specifications, and performance requirements.

The following is a list of U.S. based energy codes and standards that reference building simulations to demonstrate compliance:

Professional associations and certifications

Professional associations
Certifications

See also

Related Research Articles

Modelica programming language

Modelica is an object-oriented, declarative, multi-domain modeling language for component-oriented modeling of complex systems, e.g., systems containing mechanical, electrical, electronic, hydraulic, thermal, control, electric power or process-oriented subcomponents. The free Modelica language is developed by the non-profit Modelica Association. The Modelica Association also develops the free Modelica Standard Library that contains about 1360 generic model components and 1280 functions in various domains, as of version 3.2.1.

Micro combined heat and power

Micro combined heat and power or micro-CHP or mCHP is an extension of the idea of cogeneration to the single/multi family home or small office building in the range of up to 50 kW. Local generation has the potential for a higher efficiency than traditional grid-level generators since it lacks the 8-10% energy losses from transporting electricity over long distances. It also lacks the 10–15% energy losses from heat transfer in district heating networks due to the difference between the thermal energy carrier and the colder external environment. The most common systems use natural gas as their primary energy source and emit carbon dioxide.

Building science

Building science is the collection of scientific knowledge that focuses on the analysis of the physical phenomena affecting buildings. Building physics, architectural science and applied physics are terms used for the knowledge domain that overlaps with building science.

Displacement ventilation (DV) It is a room air distribution strategy where conditioned outdoor air is supplied at a low velocity from air supply diffusers located near floor level and extracted above the occupied zone, usually at ceiling height.

Solar gain

Solar gain is the increase in thermal energy of a space, object or structure as it absorbs incident solar radiation. The amount of solar gain a space experiences is a function of the total incident solar irradiance and of the ability of any intervening material to transmit or resist the radiation.

Thermal bridge Area or component of an object which has higher thermal conductivity than the surrounding materials

A thermal bridge, also called a cold bridge, heat bridge, or thermal bypass, is an area or component of an object which has higher thermal conductivity than the surrounding materials, creating a path of least resistance for heat transfer. Thermal bridges result in an overall reduction in thermal resistance of the object. The term is frequently discussed in the context of a building's thermal envelope where thermal bridges result in heat transfer into or out of conditioned space.

Underfloor air distribution air distribution strategy for providing ventilation and space conditioning

Underfloor air distribution (UFAD) is an air distribution strategy for providing ventilation and space conditioning in buildings as part of the design of a HVAC system. UFAD systems use an underfloor supply plenum located between the structural concrete slab and a raised floor system to supply conditioned air through floor diffusers directly into the occupied zone of the building. UFAD systems are similar to conventional overhead systems (OH) in terms of the types of equipment used at the cooling and heating plants and primary air-handling units (AHU). Key differences include the use of an underfloor air supply plenum, warmer supply air temperatures, localized air distribution and thermal stratification. Thermal stratification is one of the featured characteristics of UFAD systems, which allows higher thermostat setpoints compared to the traditional overhead systems (OH). UFAD cooling load profile is different from a traditional OH system due to the impact of raised floor, particularly UFAD may have a higher peak cooling load than that of OH systems. This is because heat is gained from building penetrations and gaps within the structure itself. UFAD has several potential advantages over traditional overhead systems, including layout flexibility, improved thermal comfort and ventilation efficiency, reduced energy use in suitable climates and life-cycle costs. UFAD is often used in office buildings, particularly highly-reconfigurable and open plan offices where raised floors are desirable for cable management. UFAD is appropriate for a number of different building types including commercials, schools, churches, airports, museums, libraries etc. Notable buildings using UFAD system in North America include The New York Times Building, Bank of America Tower and San Francisco Federal Building. Careful considerations need to be made in the construction phase of UFAD systems to ensure a well-sealed plenum to avoid air leakage in UFAD supply plenums.

CFD stands for computational fluid dynamics. As per this technique, the governing differential equations of a flow system or thermal system are known in the form of Navier–Stokes equations, thermal energy equation and species equation with an appropriate equation of state. In the past few years, CFD has been playing an increasingly important role in building design, following its continuing development for over a quarter of a century. The information provided by CFD can be used to analyse the impact of building exhausts to the environment, to predict smoke and fire risks in buildings, to quantify indoor environment quality, and to design natural ventilation systems.

The house energy rating (HER) is a standard measure of comparison by which one can evaluate the energy efficiency of a new or an existing building. The comparison is generally done for energy requirements for heating and cooling of indoor space. The energy is the main criterion considered by any international building energy rating scheme but there are some other important factors such as production of greenhouse gases emission, indoor environment quality, cost efficiency and thermal comfort, which are considered by some schemes. Basically, the energy rating of a residential building provides detailed information on the energy consumption and the relative energy efficiency of the building. Hence, HERs inform consumers about the relative energy efficiency of homes and encourage them to use this information in making their house purchase decision. There are many energy rating tools by which one can calculate the energy performance of a building. Basically all these tools involve a numerical description or prepare a computer-based model for the rating of a building against standard occupancy and activity templates. So, HERS uses a computer-simulation based methods for assessing the energy efficiency of buildings under standard conditions and its potential for improvement.

Multibody simulation (MBS) is a method of numerical simulation in which multibody systems are composed of various rigid or elastic bodies. Connections between the bodies can be modeled with kinematic constraints or force elements. Unilateral constraints and Coulomb-friction can also be used to model frictional contacts between bodies. Multibody simulation is a useful tool for conducting motion analysis. It is often used during product development to evaluate characteristics of comfort, safety, and performance. For example, multibody simulation has been widely used since the 1990s as a component of automotive suspension design. It can also be used to study issues of biomechanics, with applications including sports medicine, osteopathy, and human-machine interaction.

Radiant heating and cooling Systems using temperature-controlled surfaces to exchange heat with their surrounding environment through convection and radiation

Radiant heating and cooling systems are temperature-controlled surfaces that exchange heat with their surrounding environment through convection and radiation. By definition, in radiant heating and cooling systems, thermal radiation covers more than 50% of heat exchange within the space. Hydronic radiant heating and cooling systems are water-based. It refers to panels or embedded building components. Other types include air-based and electrical systems. Important portions of building surfaces are usually required for the radiant exchange.

International Building Performance Simulation Association

The International Building Performance Simulation Association (IBPSA), is a non-profit international society of building performance simulation researchers, developers and practitioners, dedicated to improving the built environment. IBPSA aims to provide a forum for researchers, developers and practitioners to review building model developments, encourage the use of software programs, address standardization, accelerate integration and technology transfer, via exchange of knowledge and organization of (inter)national conferences.

OpenStudio is a suite of free and open-source software applications for building energy analysis used in building information modeling. OpenStudio applications run on Microsoft Windows, Macintosh, and Linux platforms. Its primary application is a plugin for SketchUp, that enables engineers to view and edit 3D models for EnergyPlus simulation software.

Sensitivity analysis identifies how uncertainties in input parameters affect impotrant measures of building performance, such as cost, indoor thermal comfort, or CO2 emissions. Input parameters for buildings fall into roughly three categories:

IDA Indoor Climate and Energy

IDA IndoorClimate andEnergy is a Building performance simulation (BPS) software. IDA ICE is a simulation application for the multi-zonal and dynamic study of indoor climate phenomenas as well as energy use. The implemented models are state of the art, many studies show that simulation results and measured data compare well.

Ardeshir Mahdavi

Ardeshir Mahdavi is the Chair of the Institute of Architectural Sciences as well as the Director of the Department of Building Physics and Building Ecology at TU Wien. He is also the Director of the Graduate Studies Program "Building Science and Technology" at TU Wien.

Building informationmodeling (BIM) in green buildings enables sustainable designs, allowing architects and engineers to integrate and analyse building performance. BIM enhances design and construction efficiency. Designers can quantify the environmental impacts of systems and materials to support the decisions needed to produce sustainable buildings, using information about sustainable materials that are stored in the database and interoperability between design and analysis tools. Such data is useful for building life cycle assessments.

Elżbieta Kossecka Polish physicist

Elżbieta Kossecka is a Polish physicist. She is a professor of technical sciences and a researcher at the Institute of Fundamental Technological Research, Polish Academy of Sciences.

References

  1. de Wilde, Pieter (2018). Building Performance Analysis. Chichester: Wiley-Blackwell. pp. 325–422. ISBN   978-1-119-34192-5.
  2. 1 2 3 Clarke, J. A. (2001). Energy simulation in building design (2nd ed.). Oxford: Butterworth-Heinemann. ISBN   978-0750650823. OCLC   46693334.
  3. 1 2 Building performance simulation for design and operation. Hensen, Jan., Lamberts, Roberto. Abingdon, Oxon: Spon Press. 2011. ISBN   9780415474146. OCLC   244063540.CS1 maint: others (link)
  4. 1 2 Clarke, J. A.; Hensen, J. L. M. (2015-09-01). "Integrated building performance simulation: Progress, prospects and requirements". Building and Environment. Fifty Year Anniversary for Building and Environment. 91: 294–306. doi:10.1016/j.buildenv.2015.04.002.
  5. "Best Directory | Building Energy Software Tools". www.buildingenergysoftwaretools.com. Retrieved 2017-11-07.
  6. 1 2 Crawley, Drury B.; Hand, Jon W.; Kummert, Michaël; Griffith, Brent T. (2008-04-01). "Contrasting the capabilities of building energy performance simulation programs" (PDF). Building and Environment. Part Special: Building Performance Simulation. 43 (4): 661–673. doi:10.1016/j.buildenv.2006.10.027.
  7. Brown, Gösta (January 1990). "The BRIS simulation program for thermal design of buildings and their services". Energy and Buildings. 14 (4): 385–400. doi:10.1016/0378-7788(90)90100-W.
  8. Kusuda, T. (1999). "Early history and future prospects of building system simulation" (PDF). IBPSA Proceedings. Retrieved 2017-07-07.
  9. Sukjoon, Oh (2013-08-19). "Origins of Analysis Methods in Energy Simulation Programs Used for High Performance Commercial Buildings".Cite journal requires |journal= (help)
  10. Augenbroe, Godfried; Hensen, Jan (2004-08-01). "Simulation for better building design". Building and Environment. Building Simulation for Better Building Design. 39 (8): 875–877. doi:10.1016/j.buildenv.2004.04.001.
  11. Hensen, J. (2006). About the current state of building performance simulation and ibpsa. In 4th national IBPS-CZ conference (p. 2).
  12. Wang, Haidong; Zhai, Zhiqiang (John) (2016-09-15). "Advances in building simulation and computational techniques: A review between 1987 and 2014". Energy and Buildings. 128: 319–335. doi:10.1016/j.enbuild.2016.06.080.
  13. Clarke, J.A.; Sowell, E.F.; the Simulation Research Group (1985): A Proposal to Develop a Kernel System for the Next Generation of Building Energy Simulation Software, Lawrence Berkeley Laboratory, Berkeley, CA, Nov. 4, 1985
  14. Low, D. and Sowell, E.F. (1982): ENET, a PC-based building energy simulation system, Energy Programs Conference, IBM Real Estate and Construction Division, Austin, Texas (1982), pp. 2-7
  15. Sahlin, P. and Sowell, E.F. (1989). A neutral format for building simulation models, Proceedings of the Second International IBPSA Conference, Vancouver, BC, Canada, pp. 147-154, http://www.ibpsa.org/proceedings/BS1989/BS89_147_154.pdf
  16. Klein, S. A. (1993-01-01). "Development and integration of an equation-solving program for engineering thermodynamics courses". Computer Applications in Engineering Education. 1 (3): 265–275. doi:10.1002/cae.6180010310. ISSN   1099-0542.
  17. Mattsson, Sven Erik; Elmqvist, Hilding (April 1997). "Modelica - An International Effort to Design the Next Generation Modeling Language". IFAC Proceedings Volumes. 7th IFAC Symposium on Computer Aided Control Systems Design (CACSD '97), Gent, Belgium, 28–30 April. 30 (4): 151–155. CiteSeerX   10.1.1.16.5750 . doi:10.1016/S1474-6670(17)43628-7.
  18. Clarke, Joe (2015-03-04). "A vision for building performance simulation: a position paper prepared on behalf of the IBPSA Board". Journal of Building Performance Simulation. 8 (2): 39–43. doi:10.1080/19401493.2015.1007699. ISSN   1940-1493.
  19. Raftery, Paul; Keane, Marcus; Costa, Andrea (2011-12-01). "Calibrating whole building energy models: Detailed case study using hourly measured data". Energy and Buildings. 43 (12): 3666–3679. doi:10.1016/j.enbuild.2011.09.039.
  20. Reddy, T. Agami (2006). "Literature Review on Calibration of Building Energy Simulation Programs: Uses, Problems, Procedures, Uncertainty, and Tools". ASHRAE Transactions. 112 (1): 226–240.
  21. Heo, Y.; Choudhary, R.; Augenbroe, G.A. (2012). "Calibration of building energy models for retrofit analysis under uncertainty". Energy and Buildings. 47: 550–560. doi:10.1016/j.enbuild.2011.12.029.
  22. Coakley, Daniel; Raftery, Paul; Keane, Marcus (2014-09-01). "A review of methods to match building energy simulation models to measured data". Renewable and Sustainable Energy Reviews. 37: 123–141. doi:10.1016/j.rser.2014.05.007.
  23. Li, Nan; Yang, Zheng; Becerik-Gerber, Burcin; Tang, Chao; Chen, Nanlin (2015). "Why is the reliability of building simulation limited as a tool for evaluating energy conservation measures?". Applied Energy. 159: 196–205. doi:10.1016/j.apenergy.2015.09.001.
  24. Hong, Taehoon; Kim, Jimin; Jeong, Jaemin; Lee, Myeonghwi; Ji, Changyoon (2017). "Automatic calibration model of a building energy simulation using optimization algorithm". Energy Procedia. 105: 3698–3704. doi:10.1016/j.egypro.2017.03.855.
  25. Mustafaraj, Giorgio; Marini, Dashamir; Costa, Andrea; Keane, Marcus (2014). "Model calibration for building energy efficiency simulation". Applied Energy. 130: 72–85. doi:10.1016/j.apenergy.2014.05.019.
  26. Christensen, Jørgen Erik; Chasapis, Kleanthis; Gazovic, Libor; Kolarik, Jakub (2015-11-01). "Indoor Environment and Energy Consumption Optimization Using Field Measurements and Building Energy Simulation". Energy Procedia. 6th International Building Physics Conference, IBPC 2015. 78: 2118–2123. doi:10.1016/j.egypro.2015.11.281.
  27. Cornaro, Cristina; Puggioni, Valerio Adoo; Strollo, Rodolfo Maria (2016-06-01). "Dynamic simulation and on-site measurements for energy retrofit of complex historic buildings: Villa Mondragone case study". Journal of Building Engineering. 6: 17–28. doi:10.1016/j.jobe.2016.02.001.
  28. Cornaro, Cristina; Rossi, Stefania; Cordiner, Stefano; Mulone, Vincenzo; Ramazzotti, Luigi; Rinaldi, Zila (2017). "Energy performance analysis of STILE house at the Solar Decathlon 2015: lessons learned". Journal of Building Engineering. 13: 11–27. doi:10.1016/j.jobe.2017.06.015.
  29. Dodoo, Ambrose; Tettey, Uniben Yao Ayikoe; Gustavsson, Leif (2017). "Influence of simulation assumptions and input parameters on energy balance calculations of residential buildings". Energy. 120: 718–730. doi:10.1016/j.energy.2016.11.124.
  30. Imam, Salah; Coley, David A; Walker, Ian (2017-01-18). "The building performance gap: Are modellers literate?" (PDF). Building Services Engineering Research and Technology. 38 (3): 351–375. doi:10.1177/0143624416684641.
  31. Nageler, P.; Schweiger, G.; Pichler, M.; Brandl, D.; Mach, T.; Heimrath, R.; Schranzhofer, H.; Hochenauer, C. (2018). "Validation of dynamic building energy simulation tools based on a real test-box with thermally activated building systems (TABS)". Energy and Buildings. 168: 42–55. doi:10.1016/j.enbuild.2018.03.025.
  32. Choi, Joon-Ho (2017). "Investigation of the correlation of building energy use intensity estimated by six building performance simulation tools". Energy and Buildings. 147: 14–26. doi:10.1016/j.enbuild.2017.04.078.
  33. de Wilde, Pieter (2014-05-01). "The gap between predicted and measured energy performance of buildings: A framework for investigation". Automation in Construction. 41: 40–49. doi:10.1016/j.autcon.2014.02.009.
  34. "Closing the Gap Bewteen Design and As-Built Performance" (PDF). www.zerocarbonhub.org. Zero Carbon Hub. July 2013. Retrieved 2017-06-30.
  35. ASHRAE (2017). ASHRAE/ANSI Standard 140-2017--Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc.
  36. ASHRAE (2014). Guideline 14-2014 Measurement of Energy Demand Savings; Technical Report. Atlanta, GA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.
  37. Wetter, Michael; Bonvini, Marco; Nouidui, Thierry S. (2016-04-01). "Equation-based languages – A new paradigm for building energy modeling, simulation and optimization". Energy and Buildings. 117: 290–300. doi:10.1016/j.enbuild.2015.10.017.
  38. Sahlin, Per; Eriksson, Lars; Grozman, Pavel; Johnsson, Hans; Shapovalov, Alexander; Vuolle, Mika (2004-08-01). "Whole-building simulation with symbolic DAE equations and general purpose solvers". Building and Environment. Building Simulation for Better Building Design. 39 (8): 949–958. doi:10.1016/j.buildenv.2004.01.019.
  39. 1 2 3 Sahlin, Per; Eriksson, Lars; Grozman, Pavel; Johnsson, Hans; Shapovalov, Alexander; Vuolle, Mika (August 2003). "Will equation-based building simulation make it?-experiences from the introduction of IDA Indoor Climate And Energy". Proceedings of Building ….
  40. Tian, Wei; Han, Xu; Zuo, Wangda; Sohn, Michael D. (2018). "Building energy simulation coupled with CFD for indoor environment: A critical review and recent applications". Energy and Buildings. 165: 184–199. doi:10.1016/j.enbuild.2018.01.046.
  41. 1 2 Østergård, Torben; Jensen, Rasmus L.; Maagaard, Steffen E. (2016-08-01). "Building simulations supporting decision making in early design – A review". Renewable and Sustainable Energy Reviews. 61: 187–201. doi:10.1016/j.rser.2016.03.045.
  42. "Exporting ESP-r models to E+ .idf files". Answered question in the ESP-r support forum. Retrieved 2017-07-04.
  43. "IDA Tunnel". Software "Tunnel" uses IDA simulation environment. Retrieved 2017-07-04.
  44. Judkoff, Ron (2008). Annex 43/Task 34 Final Task Management Report - Testing and Validation of Building Energy Simulation Tools. International Energy Agency (IEA).
  45. Integrated Environmental Solutions, Ltd (2017). "APACHESIM" . Retrieved 2017-11-07.
  46. "VE2018 Website" . Retrieved 2018-09-26.
  47. "Hourly Analysis Program HVAC System Design Software | Carrier Building Solutions". Building Solutions. Retrieved 2017-11-07.
  48. Lokmanhekim, M.; et al. (1979). "DOE-2: a new state-of-the-art computer program for the energy utilization analysis of buildings". Lawrence Berkeley Lab. Report CBC-8977.
  49. Hirsch, Jeff. "eQUEST". doe2.com. Retrieved 2017-11-07.
  50. Granlund Consulting Oy. "RIUSKA Website" . Retrieved 2018-04-03.
  51. "EnergySoft – World Class Building Energy Analysis Software". www.energysoft.com. Retrieved 2017-11-07.
  52. "Green Building Studio". gbs.autodesk.com. Retrieved 2017-11-07.
  53. US Departement of Energy's, Building Technology office. "Energy+ Homepage" . Retrieved 2018-04-03.
  54. Tindale, A (2005). "Designbuilder Software". Design-Builder Software Ltd.
  55. Guglielmetti, Rob; et al. (2011). "OpenStudio: An Open Source Integrated Analysis Platform" (PDF). Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association: 442–449.
  56. BEST directory. "List of graphical user interfaces for Energy+" . Retrieved 2018-04-03.
  57. "ESP-r | University of Strathclyde". www.strath.ac.uk. Retrieved 2017-11-08.
  58. EQUA Simulation AB. "IDA ESBO Homepage" . Retrieved 2018-04-03.
  59. LBNL, US Departement of Energy. "Project SPARK" . Retrieved 2018-04-03.
  60. "EDSL TAS website" . Retrieved 2018-04-03.
  61. Beckman, William A.; Broman, Lars; Fiksel, Alex; Klein, Sanford A.; Lindberg, Eva; Schuler, Mattias; Thornton, Jeff (1994). "TRNSYS The most complete solar energy system modeling and simulation software". Renewable Energy. 5 (1–4): 486–488. doi:10.1016/0960-1481(94)90420-0.
  62. "Manual for Simulation Studio" (PDF). Retrieved 2018-03-29.
  63. 1 2 "Home | ashrae.org". www.ashrae.org. Retrieved 2017-11-08.
  64. "BBR - Swedish building regulation" . Retrieved 2018-03-29.
  65. "Swiss society of architects and engineers (SIA)" . Retrieved 2018-03-29.
  66. "UKs National Calculation Method" . Retrieved 2018-03-29.
  67. "Swedish code summarized in global performance network" . Retrieved 2018-03-29.
  68. Senick, Jennifer. "A new paradigm for building codes". cbei.psu.edu. Retrieved 2017-11-07.
  69. "IBPSA-USA". IBPSA-USA. Retrieved 13 June 2014.
  70. "Building Energy Modeling Professional Certification". ashrae.org. ASHRAE. Retrieved 2018-04-03.
  71. "Certified Building Energy Simulation Analyst". aeecenter.org. Association of Energy Engineers. 2016-08-04. Retrieved 2018-04-03.