Building performance simulation (BPS) is the use of software for predicting performance aspects of a building. The objective is to create a virtual model that is accurate to form a useful representation of the actual building. BPS forecasts the various energy and mass flows within a building, in order to evaluate one or more performance aspects using computer simulation.
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. BPS is a technology of considerable potential that provides the ability to quantify and compares the relative cost and performance attributes of a proposed design in a realistic manner and relatively low effort cost. Energy demand, indoor environmental quality, HVAC and renewable system performance, urban level modeling, building automation, and operational optimization are important aspects of BPS. 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. Some of them only cover certain parts of BPS (eg 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 provides 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. 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. The ease of input and accessibility of output varies widely between BPS tools. Advanced whole-building simulation tools are suitable for all types of applications. Necessary input data for a whole-building simulation:
The history of BPS is 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 have been introduced by BRIS, introduced in 1963 by the Royal Institute of Technology in Stockholm. Until the late 60’s, several models with energy efficiency and heating / cooling load calculations. This effort was more powerful in the early 70’s, among those were BLAST, DOE-2, ESP-R, HVACSIM + and TRNSYS. In the United States, the 1970’s energy crisis intensified these efforts, as reducing the energy consumption of buildings has become an urgent domestic policy interest. The energy crisis also began with the development of US energy standards, beginning with ASHRAE 90-75. The development of building simulation is a combined effort between academia, governmental institutions, industry, and professional organizations. Over the past decades the building discipline has been developed in the field of expertise, methods and tools for building performance evaluation. Several review papers and state of the art analysis were made during this time. In the 1980s, a discussion about future directions for BPS. 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 flexibility and flexibility that would be called for in the future. Around this time, the very first equation-based building simulation environment 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 IDA ICE software. Four years later, Klein introduced the Engineering Equation Solver (EES) and in 1997, Mattsson and Elmqvist reported on an international effort to design Modelica. 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,
In the context of building simulation models, the error is related to the discrepancy between simulation results and the actual measured performance of the building. There are normally present 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 “adapted simulation model” inputs to a “matched data” from the utilities or Building Management System (BMS). The number of publications dealing with accuracy in building modeling and simulation significantly over the past decade. Many papers report large gaps between simulation and results, while other studies show that they can match very well. The reliability of results from BPS depends on many different things, eg on the quality of input data, the competence of the simulation engineers and on the applied methods in the simulation engine. An overview about possible causes for the performance of operations is given by de Wilde (2014) and a progress report by the Zero Carbon Hub (2013). Both conclude the factors mentioned above in BPS. ASHRAE Standard 140-2011 “Standard Method of Testing for the Evaluation of Building Energy Analysis Computer Programs” provides a method to validate the technical capability and range of applicability of computer programs to calculate thermal performance. ASHRAE Guideline 14-2002 and 14-2014 provides performance indexes for building energy model calibration. The performance indices are normalized mean bias error (NMBE), coefficient of variation (CV) of the root mean square error (RMSE), and R 2 (coefficient of determination). ASHRAE recommends a R 2 greater than 0.75 for calibrated models. The criteria for NMBE and CV RMSE are available at a monthly or hourly timescale.
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 methods, or numerical methods in finite differences or finite volume, as an approximation. Most of today’s all-in-one simulation programs. These languages are assignments to variables, declare the sequence of execution of these assignments and change the state of the program, 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. The use of imperative programming languages limits the applicability and extensibility of models. 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 (eg IDA ICE), these techniques can be considered as state of the art technology.
Building simulation models can be developed for both new and existing buildings. Major use categories of building performance simulation include:
There are hundreds of software tools available for simulating the performance of buildings and building subsystems, which range in size from whole-building simulations to model input calibration to building auditing. Among all-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). In general, BPS software can be classified into
Since the 90’s, building performance simulation has undergone the transition from a method used for research and design to 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 wider application. Also, in the United States (ASHRAE 90.1), Sweden (BBR), Switzerland (SIA) and the United Kingdom (NCM). The Swedish building regulations are unique in that computed energy. Since the introduction in 2007, experience shows that highly detailed models are preferred by modelers to reliably achieve the required level of accuracy. Furthermore, this has fostered a simulation of culture where the design predictions are close to the actual performance. This in turn is based on simulated predictions, highlighting the general business potential of BPS.
In a performance-based approach, compliance with building codes or standards is based on a predictive energy approach, which requires 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 reduce the need for prescriptive requirements. The certifying agency provides details on model inputs, software specifications, and performance requirements. The following is a list of US based energy codes and standards that reference building simulations to demonstrate compliance: