4.1 Introduction

Building simulation (BS) is the process of creating a digital replica of a building, while building performance simulation (BPS) is a model that evaluates how the building performs under real-life conditions (Mahdavi, 2020). During the replication process, digital copies are created of the whole building—its exterior and interior, and, in some cases, the building’s distinct parts (e.g., apartments and rooms) if needed. The BPS process consists of five main phases as depicted in Fig. 4.1. The main objective of this process is to define the best performance criteria and the most suitable actions by applying performance simulations. Once results are generated, they are evaluated against initial expectations and requirements.

Fig. 4.1
A circular cyclic diagram with 5 exploded sections. The B P S process has the following steps. 1. Definition of performance criteria. 2. Developing strategies or actions. 3. Simulation for the selected strategies. 4. Evaluation of simulation results. 5. Retrofit proposals.

Building performance simulation process (Bramstoft et al., 2018)

Mathematical and intelligent models and applications are exploited to recreate (simulate) various external and internal conditions while representing them in a virtual environment (Mahdavi, 2020). BPS makes it easier for different stakeholders (building managers, architects, engineers, etc.) to inspect and check salient points, elements, and other aspects of the building’s life cycle (i.e., early design, construction, retrofitting, monitoring, inspection, and demolition) (Bramstoft et al., 2018).

Exploiting BS tools and applications is faster, safer, and less expensive than producing a real use case scenario. It supports product and system testing without having to build them in real life and is often less time-consuming and costly while also being safer. Moreover, BPS may be exploited for identifying building problems by replicating and producing different conditions. Finally, it may be used to model specific changes to check how the building reacts in the short or the long term (Fernandez-Antolin et al., 2022).

The remainder of the chapter is structured as follows: Sect. 4.2 describes the main applications of and approaches to BPS. Section 4.3 introduces BPS in more detail. Finally, Sect. 4.4 presents some concluding remarks.

4.2 Building Performance Simulation Approaches and Applications

BPS is a dynamic technique that is used to predict the behaviour of a building while optimising energy efficiency (Attia et al., 2013). The key objective of BPS is to reduce the building’s environmental footprint while improving indoor environmental quality (IEQ). At the same time, if applied correctly, BPS may facilitate technological innovation and progress in building construction (Loonen et al., 2017). BPS energy models are applied in a number of real-life applications. These include, for example, energy conservation, energy monitoring, energy savings, and fault detection. Simulations may include load and energy simulation, energy management simulation, virtual reality simulations, and a wide range of other simulations based on stakeholders’ needs (Martins, 2022).

4.2.1 Integrating Simulations with Other Knowledge-Based Decision Support Systems

Knowledge-based systems employ various and numerous techniques such as statistical analysis, artificial intelligence methods, knowledge and data visualisation, engineering, and other methods (Alor-Hernández & Valencia-García, 2017). These techniques have been developed to be integrated into heterogeneous systems including decision support systems (DSS), software agents (SAs), and knowledge engineering (KE) which may be strongly related to BS as described in Table 4.1. All these systems use prior knowledge to exclude, determine, and propose further knowledge. To deploy a knowledge-based system, an analysis of the building’s energy conservation is needed to inspect the building’s energy efficiency. In general, knowledge-based systems are the main component of building simulation and are mainly used as the process that emulates the simulation outcome.

Table 4.1 Knowledge-based systems and exemplar simulation use cases

4.2.2 Using Simulations to Evaluate and Compare Design Scenarios

Simulations provide many advantages compared to a conceptual design. For example, BPS provides the ability to examine various solutions during the design stage like the efficiency of the building’s equipment and integration, therefore reducing development time and energy consumption and emissions. Moreover, BPS may facilitate the optimisation of thermal and visual comfort by simulating the building’s fenestration and massing. This process may also be deployed in the early stages while designing the façade. Finally, BPS enables the simulation of heating, ventilation, and air conditioning (HVAC) systems to define the optimal setup.

4.2.3 Integrating Simulations with Real-Time Monitoring and Diagnostic Systems for Building Energy Management and Control

Radiation, conduction, and convection are mass and heat transfer phenomena that take place in a building and are the key inputs for energy and load simulations (Yu, 2019). As a result, every BS needs to take into account different mechanisms behind such phenomena. Heat and mass transfer are carried in the building by air movements and are embodied by the indoor air pressure stack between outdoor and indoor places (Vera, 2018). Those phenomena are influenced by peoples’ activities, heating and cooling systems, and ventilation as well as building insulation and orientation (Puttur et al., 2022). Therefore, thermal and airflow models are applied to represent heat and mass transfers of buildings as they are significantly associated with the energy transfer (Tian, 2018b) and are used to calculate loads and simulate energy consumption (Tan et al., 2022).

Frequently used models for simulating energy consumption include Computational Fluid Dynamics (CFD) models, zonal models, and multi-zone models (Laghmich et al., 2022). CFD models separate the building into cells to simulate load and energy consumption (Shen et al., 2020). A multi-zone model uses rooms as computational elements for the simulation, while a zonal model uses several zones by separating a room into smaller units (Yu, 2019). An overall assessment of the aforementioned models is presented in Table 4.2.

Table 4.2 Airflow models for energy simulation comparison (Laghmich et al., 2022)

Other models estimate a building’s energy consumption by exploiting physical models (Kampelis et al., 2020). These models simulate energy consumption by exploiting mathematical equations and the building’s energy conservation. While such mathematical or computational models are sufficiently precise, they require holistic building information (Oucquier et al., 2013). Furthermore, a unique model is required for each building. Another widely referenced approach to simulating building energy load is data-driven modelling (Bermeo-Ayerbe et al., 2022). These models use indoor monitoring and measurements (e.g., relative humidity, temperature, historical consumption data, historical load, and generations data) to predict energy consumption (Kampelis et al., 2020). A benchmarking analysis of various regression models using energy consumption suggests that, in many cases, these models are sufficiently accurate for building simulation and may be used as a generic solution (Dimara et al., 2021).

To manage the overall energy consumption of a building, various load controls are applied to manage both energy savings and comfort regulation. The energy load of all appliances in a building are simulated in order to build predictive models for energy consumption and deploy an accurate energy management strategy (Fanti et al., 2018). The main problem when trying to find optimal control states is to detect the best strategies for heating, cooling, ventilation, and lighting that result in energy savings while maintaining desirable indoor conditions for the occupants. As such, all possible energy load actions must be simulated accurately.

Building energy modelling and simulation allows stakeholders to better understand certain energy operating characteristics before designing, applying, or testing them in a real-life scenario. Furthermore, it helps with reducing waste and allows energy-saving verification by testing real data against various scenarios which may take into account multiple factors such as weather conditions and occupancy patterns.

4.2.4 Integrating Multiple Simulation Applications

As mentioned previously, most simulations require the deployment of multiple models, applications, and techniques to provide an overall building assessment. In general, deploying and integrating automated multi-simulation applications may produce significant advantages when compared to a single simulation. During this procedure, a couple of models are integrated and their output is combined. For example, a comprehensive evaluation of the HVAC systems in a building would require the combination of airflow, heat losses, atmospheric conditions, and energy performance simulations.

4.3 Building Performance Simulation Use Cases

Deployment of modelling and simulation tools for building performance can be implemented in various and numerous use cases from the design stage to operation and management of a building. Some of the most common BPS simulation use cases are summarised in Table 4.3.

Table 4.3 Simulation use cases along the deep renovation life cycle

To implement any of the use cases above or any type of building simulation, appropriate tools and technologies must be applied. Some indicative technologies and commercial tools for BPS are summarised in Tables 4.4 and 4.5.

Table 4.4 Indicative simulation technologies (Khajavi et al., 2019)
Table 4.5 Commercial simulation tools

4.4 Building Simulation: Challenges and Concerns

In the summary of literature on BPS, Attia (2010) identifies five major challenges—(a) interface usability and information management, (b) integration of decision design support and design optimisation, (c) accuracy and ability to simulate detailed and complex building components, (d) integration with other tools in the building design and construction/renovation process, and (e) BIMFootnote 1 integration and interoperability. These challenges were echoed and expanded more recently by Hong et al. (2018). The ten challenges identified by Hong et al. (2018) cover the full building life cycle and have been updated to include zero-net-energy (ZNE) and grid-responsive buildings, as well as urban-scale building energy modelling (see Table 4.6).

Table 4.6 Ten challenges of building performance simulation

While the BPS challenges Attia (2010) and Hong et al. (2018) present at a high level are not insignificant, at a more granular and operational level, the selection of a BPS tool is also not without challenges. In addition to the level of accuracy and detail, usability and information management, data exchange capacity, database support, interoperability with building modelling, and integration of building design process, Solmaz (2019) highlights the speed (time to implement), cost, and ease of use of BPS tools as significant issues. Given the centrality of BIM in the building and deep renovationFootnote 2 process, it is important to highlight specific challenges noted by many studies with respect to the integration of simulations and specifically energy simulations in BIM (Østergård et al., 2016; Hong et al., 2018; Kamel & Memari, 2019; Solmaz, 2019). Challenges include interactions between components, file-related interoperability issues at the file and syntax level, visualisation level, and semantic level, different calculation methods, attribute support, missing data, and data loss between systems (Kamel & Memari, 2019).

4.5 Conclusion

Building simulation can provide valuable insights across all the stages of the building and deep renovation life cycle. It may be used for many use cases as it is adaptable to various inputs and it can be deployed based on different needs. Furthermore, there is a plethora of tools and technologies that may support the simulation process. Nevertheless, simulation technology is evolving rapidly with advancements in simulation techniques, software, and hardware. Moreover, most of the simulation applications or auxiliary tools (e.g., BIM and DT) have significant data demands, leading to higher demand for storage and computational resources. As a result, new big data handling solutions must be developed to support the simulation process. In the future, building simulation will undoubtedly be a significant element in the whole cycle of the building; however, both integration and interoperability remain significant challenges that should not be underestimated.