Building performance simulation

Last updated
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]

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 such as Typical Meteorological Year (TMY) file; 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.

Necessary input data for a whole-building simulation:

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 1950s and early 1960s 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 1960s, 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 1970s, among those were BLAST, DOE-2, ESP-r, HVACSIM+ and TRNSYS. [8] In the United States, the 1970s 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
COMFIE [48] Mines ParisTech, then IZUBA énergies, FR1994Commercial5.21.3.0Pleiades
DOE-2 [49] James J. Hirsch & Associates, US1978Freeware2.2eQuest, [50] RIUSKA, [51] EnergyPro, [52] GBS [53]
EnergyPlus [54] Lawrence Berkeley National Laboratory, US2001Freeware9.4.0DesignBuilder, [55] OpenStudio, [56] cove.tool, [57] [58] Many other [59]
ESP-r [60] University of Strathclyde, UK1974Freeware11.11ESP-r
IDA [39] EQUA Simulation AB, SE1998DAENMF, ModelicaCommercial4.8ICE, [39] ESBO [61]
SPARK [62] Lawrence Berkeley National Laboratory, US1986DAEFreeware2.01VisualSPARK
TAS [63] Environmental Design Solutions Limited, UKCommercial9.5.0TAS 3D Modeler
TRNSYS [64] University of Wisconsin-Madison, US1975FORTRAN, C/C++Commercial18.0Simulation Studio, [65] TRNBuild

BPS in practice

Since the 1990s, building performance simulation has undergone the transition from a method used mainly for research to a design tool for mainstream industrial projects. However, the use 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), [66] Sweden (BBR), [67] Switzerland (SIA) [68] and the United Kingdom (NCM). [69]

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. [70]

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. [71] 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

<span class="mw-page-title-main">Heat recovery ventilation</span> Method of reusing thermal energy in a building

Heat recovery ventilation (HRV), also known as mechanical ventilation heat recovery (MVHR), is an energy recovery ventilation system that operates between two air sources at different temperatures. It's a method that is used to reduce the heating and cooling demands of buildings. By recovering the residual heat in the exhaust gas, the fresh air introduced into the air conditioning system is preheated before it enters the room, or the air cooler of the air conditioning unit performs heat and moisture treatment. A typical heat recovery system in buildings comprises a core unit, channels for fresh and exhaust air, and blower fans. Building exhaust air is used as either a heat source or heat sink, depending on the climate conditions, time of year, and requirements of the building. Heat recovery systems typically recover about 60–95% of the heat in the exhaust air and have significantly improved the energy efficiency of buildings.

<span class="mw-page-title-main">Building science</span>

Building science is the science and technology-driven collection of knowledge in order to provide better indoor environmental quality (IEQ), energy-efficient built environments, and occupant comfort and satisfaction. Building physics, architectural science, and applied physics are terms used for the knowledge domain that overlaps with building science. In building science, the methods used in natural and hard sciences are widely applied, which may include controlled and quasi-experiments, randomized control, physical measurements, remote sensing, and simulations. On the other hand, methods from social and soft sciences, such as case study, interviews & focus group, observational method, surveys, and experience sampling, are also widely used in building science to understand occupant satisfaction, comfort, and experiences by acquiring qualitative data. One of the recent trends in building science is a combination of the two different methods. For instance, it is widely known that occupants' thermal sensation and comfort may vary depending on their sex, age, emotion, experiences, etc. even in the same indoor environment. Despite the advancement in data extraction and collection technology in building science, objective measurements alone can hardly represent occupants' state of mind such as comfort and preference. Therefore, researchers are trying to measure both physical contexts and understand human responses to figure out complex interrelationships.

<span class="mw-page-title-main">Variable air volume</span> Heating or air-conditioning system

Variable air volume (VAV) is a type of heating, ventilating, and/or air-conditioning (HVAC) system. Unlike constant air volume (CAV) systems, which supply a constant airflow at a variable temperature, VAV systems vary the airflow at a constant or varying temperature. The advantages of VAV systems over constant-volume systems include more precise temperature control, reduced compressor wear, lower energy consumption by system fans, less fan noise, and additional passive dehumidification.

<span class="mw-page-title-main">Underfloor heating</span> Form of central heating and cooling

Underfloor heating and cooling is a form of central heating and cooling that achieves indoor climate control for thermal comfort using hydronic or electrical heating elements embedded in a floor. Heating is achieved by conduction, radiation and convection. Use of underfloor heating dates back to the Neoglacial and Neolithic periods.

<span class="mw-page-title-main">Solar gain</span> Solar energy effect

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 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 release 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 release 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.

<span class="mw-page-title-main">Thermal bridge</span>

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.

<span class="mw-page-title-main">Energy audit</span> Inspection, survey and analysis of energy flows in a building

An energy audit is an inspection survey and an analysis of energy flows for energy conservation in a building. It may include a process or system to reduce the amount of energy input into the system without negatively affecting the output. In commercial and industrial real estate, an energy audit is the first step in identifying opportunities to reduce energy expense and carbon footprint.

<span class="mw-page-title-main">Underfloor air distribution</span>

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 to supply outlets, located at or near floor level within the occupied space. Air returns from the room at ceiling level or the maximum allowable height above the occupied zone.

<span class="mw-page-title-main">ESP-r</span>

ESP-r is a research-oriented open-source building performance simulation software. ESP-r can model heat flow in thermal zones, fluid flow using networks or CFD, electrical power flow, moisture flow, contaminant flow, hygrothermal and fluid flow in HVAC systems, as well as visual and acoustic performance aspects within a modeled energy system/building.

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.

<span class="mw-page-title-main">Radiant heating and cooling</span> Category of HVAC technologies

Radiant heating and cooling is a category of HVAC technologies that exchange heat by both convection and radiation with the environments they are designed to heat or cool. There are many subcategories of radiant heating and cooling, including: "radiant ceiling panels", "embedded surface systems", "thermally active building systems", and infrared heaters. According to some definitions, a technology is only included in this category if radiation comprises more than 50% of its heat exchange with the environment; therefore technologies such as radiators and chilled beams are usually not considered radiant heating or cooling. Within this category, it is practical to distinguish between high temperature radiant heating, and radiant heating or cooling with more moderate source temperatures. This article mainly addresses radiant heating and cooling with moderate source temperatures, used to heat or cool indoor environments. Moderate temperature radiant heating and cooling is usually composed of relatively large surfaces that are internally heated or cooled using hydronic or electrical sources. For high temperature indoor or outdoor radiant heating, see: Infrared heater. For snow melt applications see: Snowmelt system.

<span class="mw-page-title-main">MOOSE (software)</span>

MOOSE is an object-oriented C++ finite element framework for the development of tightly coupled multiphysics solvers from Idaho National Laboratory. MOOSE makes use of the PETSc non-linear solver package and libmesh to provide the finite element discretization.

<span class="mw-page-title-main">International Building Performance Simulation Association</span>

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.

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

<span class="mw-page-title-main">IDA Indoor Climate and Energy</span>

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 phenomena as well as energy use. The implemented models are state of the art, many studies show that simulation results and measured data compare well.

<span class="mw-page-title-main">Ventilative cooling</span>

Ventilative cooling is the use of natural or mechanical ventilation to cool indoor spaces. The use of outside air reduces the cooling load and the energy consumption of these systems, while maintaining high quality indoor conditions; passive ventilative cooling may eliminate energy consumption. Ventilative cooling strategies are applied in a wide range of buildings and may even be critical to realize renovated or new high efficient buildings and zero-energy buildings (ZEBs). Ventilation is present in buildings mainly for air quality reasons. It can be used additionally to remove both excess heat gains, as well as increase the velocity of the air and thereby widen the thermal comfort range. Ventilative cooling is assessed by long-term evaluation indices. Ventilative cooling is dependent on the availability of appropriate external conditions and on the thermal physical characteristics of the building.

<span class="mw-page-title-main">Ardeshir Mahdavi</span>

Ardeshir Mahdavi is an expert in building physics, architectural science, and human ecology.

<span class="mw-page-title-main">Elżbieta Kossecka</span> 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.{{cite book}}: 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" (PDF). 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. Archived from the original on 2019-10-08. 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". Texas A&M Libraries. Archived from the original on 2017-11-09. Retrieved 2017-11-09.
  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. S2CID   60901354.
  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. S2CID   56419662.
  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. S2CID   55153560.
  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. S2CID   117446952.
  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 Between Design and As-Built Performance" (PDF). www.zerocarbonhub.org. Zero Carbon Hub. July 2013. Archived from the original (PDF) on 2021-12-02. 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 . OSTI   1432688.
  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. S2CID   56153278.
  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". Archived from the original on 2017-11-08. Retrieved 2017-11-07.
  46. ↑ "VE2018 Website" . Retrieved 2018-09-26.
  47. ↑ "Hourly Analysis Program HVAC System Design Software | Carrier Building Solutions". Building Solutions. Archived from the original on 2017-11-08. Retrieved 2017-11-07.
  48. ↑ Peuportier, Bruno; Blanc-Sommereux, Isabelle (1990). "Simulation Tool with Its Expert Interface for the Thermal Design of Multizone Buildings". International Journal of Solar Energy. 8 (2): 109–120. Bibcode:1990IJSE....8..109P. doi:10.1080/01425919008909714.
  49. ↑ Lokmanhekim, M.; et al. (1979). "DOE-2: a new state-of-the-art computer program for the energy usea analysis of buildings". Lawrence Berkeley Lab. Report CBC-8977.
  50. ↑ Hirsch, Jeff. "eQUEST". doe2.com. Archived from the original on 2017-11-03. Retrieved 2017-11-07.
  51. ↑ Granlund Consulting Oy. "RIUSKA Website" . Retrieved 2018-04-03.
  52. ↑ "EnergySoft – World Class Building Energy Analysis Software". www.energysoft.com. Archived from the original on 2017-11-08. Retrieved 2017-11-07.
  53. ↑ "Green Building Studio". gbs.autodesk.com. Archived from the original on 2020-02-06. Retrieved 2017-11-07.
  54. ↑ US Departement of Energy's, Building Technology Office. "EnergyPlus Homepage". Archived from the original on 2017-11-08. Retrieved 2021-02-20.
  55. ↑ Tindale, A (2005). "Designbuilder Software". Design-Builder Software Ltd.
  56. ↑ 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. Archived from the original (PDF) on 2017-08-09. Retrieved 2017-12-08.
  57. ↑ "cove.tool - Sustainable Building Design | Energy Modeling Software". www.cove.tools. Retrieved 2021-08-23.
  58. ↑ "loadmodeling.tool optimize and collaborate on HVAC Design". www.cove.tools. Retrieved 2021-08-23.
  59. ↑ BEST directory. "List of graphical user interfaces for Energy+" . Retrieved 2018-04-03.{{cite web}}: |last= has generic name (help)
  60. ↑ "ESP-r | University of Strathclyde". www.strath.ac.uk. Archived from the original on 2017-11-08. Retrieved 2017-11-08.
  61. ↑ EQUA Simulation AB. "IDA ESBO Homepage" . Retrieved 2018-04-03.
  62. ↑ LBNL, US Departement of Energy. "Project SPARK" . Retrieved 2018-04-03.
  63. ↑ "EDSL TAS website" . Retrieved 2018-04-03.
  64. ↑ 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.
  65. ↑ "Manual for Simulation Studio" (PDF). Retrieved 2018-03-29.
  66. 1 2 "Home | ashrae.org". www.ashrae.org. Retrieved 2017-11-08.
  67. ↑ "BBR - Swedish building regulation". Archived from the original on 2018-03-29. Retrieved 2018-03-29.
  68. ↑ "Swiss society of architects and engineers (SIA)" . Retrieved 2018-03-29.
  69. ↑ "UKs National Calculation Method" . Retrieved 2018-03-29.
  70. ↑ "Swedish code summarized in global performance network". Archived from the original on 2021-01-17. Retrieved 2018-03-29.
  71. ↑ Senick, Jennifer. "A new paradigm for building codes". cbei.psu.edu. Retrieved 2017-11-07.
  72. ↑ "IBPSA-USA". IBPSA-USA. Retrieved 13 June 2014.
  73. ↑ "Building Energy Modeling Professional Certification". ashrae.org. ASHRAE. Retrieved 2018-04-03.
  74. ↑ "Certified Building Energy Simulation Analyst". aeecenter.org. Association of Energy Engineers. 2016-08-04. Retrieved 2018-04-03.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.