Building Simulation Programs Research Articles

A framework for assessing the current and future capability of mechanical night ventilation in the context of climate change

Night ventilation is a technique in which the indoor air and the building’s thermal mass is cooled down during nighttime to provide a heat sink available during the next day to help mitigating overheating and reducing the daytime space cooling demand. This paper proposes a framework on evaluating the use of mechanical night ventilation today and in the future. It considers analysis of the ventilative cooling system, including mechanical night ventilation, by means of key performance indicators that involve thermal comfort, energy use and resiliency criteria as suggested by IEA Annex 80. It, additionally, introduces an economic parameter in form of diurnal and nocturnal price ratio of electricity as economic trade-off between nighttime fan- and daytime fan and chiller use in terms of electricity. A historic office building in north-central Sweden is presented as a detailed case as to illustrate the use of the framework. The investigation was done using a validated model of the building in IDA-ICE building simulation program at both current climate and future climate in 2050s. It was revealed that an upgraded ventilative cooling system with three times larger capacity is required to fulfill thermal comfort. Even though mechanical night ventilation could result in the annual cooling source electricity saving intensity up to 0.9 kWh/(m²∙a) at extreme current climate (2018), it could just insignificantly reduce the total electricity use for space cooling (up to 2 %) and only at some night ventilation rates at all mentioned climates. Mechanical night ventilation, however, could be applied in an economically beneficial way if the electricity network has different nocturnal and diurnal electricity prices. A unitless index of maximum nighttime over daytime electricity price ratio was proposed representing the maximum tolerable price for nighttime electricity, given a daytime electricity price, based on night- and daytime ventilation electricity demand. For economically justified application of mechanical night ventilation, lower nighttime over daytime electricity price ratios were required for higher night ventilation rates. For the typical future climate with night ventilation rates larger than 2.6 ACH, it will be necessary to have nighttime prices that are lower than daytime if mechanical night ventilation is to be economical. The approach used in the framework can be applied to future research and practice, regardless of the case-specific parameters such as building type, climate zone, location, etc.

Simulating Energy Use, Indoor Temperatures, and Utility Cost Impacts Amidst a Warming Climate in a Multi-family Housing Model.

Rising ambient temperatures due to climate change will impact both indoor temperatures and heating and cooling utility costs. In traditionally colder climates, there are potential tradeoffs in how to meet the reduced heating and increased cooling demands, and issues related to lack of air conditioning (AC) access in older homes and among lower-income populations to prevent extreme heat exposure. We modeled a typical multi-family home in Boston (MA) in the building simulation program EnergyPlus to assess indoor temperature and energy consumption in current (2020) and projected future (2050) weather conditions. Selected households were those without AC (no AC), those who ran AC sometimes (some AC), and those with sufficient resources to run AC always (full AC). We considered stylized cooling subsidy policies that allowed households to move between groups, both independently and in conjunction with energy efficiency retrofits. Results showed that future weather conditions without policy changes yielded an increase in indoor summer temperatures of 2.1°C (no AC), increased cooling demand (range: 34-50%), but led to a decrease in net yearly total utility costs per apartment (range: - $21 to - $38). Policies that allowed households to move to greater AC utilization yielded average indoor summer temperature decreases (- 3.5°C to - 6.2°C) and net yearly total utility increases (range: + $2 to + $94) per apartment unit, with greater savings for retrofitted homes primarily due to large decreases in heating use. Our model results reinforce the importance of coordinated public policies addressing climate change that have an equity lens for both health and climate goals.

Optimization-informed Rule Extraction for HVAC system: A Case Study of Dedicated Outdoor Air System Control in a Mixed-Humid Climate Zone.

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control.

Occupants’ behavioural diversity regarding the indoor environment in social housing. Case study in Northern Spain

Previous research has shown that differences in preferences, habits, and uses can exist in buildings with similar characteristics, which can influence building performance, energy efficiency, and the well-being of occupants. Among this diversity, those residing in social housing have specific socio-economic and cultural characteristics. This study aims to provide evidence of the diversity of thermal preferences and heating-related behaviours in public social rental housing. It also seeks to develop a methodology for identifying behavioural and occupancy patterns that can be applied in building simulation programs and building stock management. To identify occupancy and heating patterns, quantitative and qualitative data analysis methods were applied. The data was collected from a variety of sources, including sensors and surveys. Advanced statistical methods were used to analyse the data and identify patterns and trends. The study was conducted in 58 dwellings of a public social rental housing building in northern Spain. The results showed a lack of association between perceived and monitored thermal comfort. Additionally, variability in the use of the dwelling has been found among similar socioeconomic profiles. The analysis of behavioural diversity revealed six clusters based on energy consumption behaviour, including occupancy patterns and heating usage. The patterns obtained can be integrated into building performance simulation programs, resulting in a more nuanced and accurate representation of energy consumption patterns. Moreover, these patterns can provide valuable insight into the diversity of energy consumption behaviours. This can be leveraged to unlock new opportunities for energy savings, efficiency gains, and enhancing the well-being of occupants across a variety of use cases.

Evaluation of the energy efficiency of an active thermoelectric façade

Innovative sustainable and decentralised energy systems have the potential to contribute significantly to energy saving and carbon emission reduction in the building sector. This article discusses the evaluation of such a novel decentralised heating and cooling system based on small thermoelectric heat pumps called Peltier elements, focusing on its energy efficiency. Experimental studies on the performance of a prototype façade system in outdoor conditions were performed. Based on the results of these experimental studies, thermal simulations were performed using the thermal building simulation program TRNSYS. Combining the results of the outdoor evaluations with those of the building simulations, the energy performance of the developed façade system could be evaluated. It was found that the demand for electrical energy was about 10% higher than that for heating energy, showing that the designed façade system cannot be operated with sufficient efficiency and therefore is not a sustainable alternative to commercially available systems at the current state of the art.

Cosimulation of Integrated Organic Photovoltaic Glazing Systems Based on Functional Mock-Up Unit

This study presents an approach to simulating building-integrated photovoltaic glazing systems composed of semitransparent organic photovoltaic (ST-OPV) elements. The approach consists of a mathematical cosimulation model based on the energy balance of complex glazing systems, considering heat transfer as conduction, mixed convection, and radiation effects. The cosimulation method is based on a functional mock-up unit (FMU) developed in Python and the building simulation program Domus. This work aims at presenting a cosimulation technique that can be easily applied to building energy simulation tools for the assessment of photovoltaic energy generation in glazing systems. The cosimulation glazing model was verified according to ANSI/ASHRAE Standard 140-2011, and the zone temperature was kept within with a root medium square error (RMSE) of 1.45 °C. The simulated building with an ST-OPV system showed promising results and could be applied to near-zero energy buildings since each 6-m2 glazing has a power generation of around 77 W, equivalent to 9% of available solar resource.

The Effect of Probabilistic Window Opening Behavior of Occupants on Adaptive Thermal Comfort (The Case of Courtyard House in Yazd, Bandar Abbas, and Tabriz)

Considering the global energy crisis and the need to reduce energy consumption while providing thermal comfort to occupants, building performance prediction using building simulation programs requires higher accuracy of output data. Therefore, it seems necessary to study the impact of occupant behavior, which is the main source of uncertainty in residential buildings. The traditional courtyard houses, which are recognized as a successful passive house model, respond to different climatic conditions. Therefore, this research focuses on this building type to analyze occupant window opening control scenarios and determine which control works better. For this purpose, several probabilistic controls and their effects on the adaptive thermal comfort of occupants in zones around a central courtyard were compared in the three cities of Yazd, Bandar Abbas, and Tabriz. Energy Plus was used as a simulation program for the application of Grasshopper's energy management system (EMS) along with the Ladybug and Honeybee environmental plugins. The results show that the window control algorithms can increase the adaptive thermal comfort of occupants by 25.7%, 32.2%, and 20.3% in each of the climates of Yazd, Bandar Abbas, and Tabriz cities, respectively. Indoor and outdoor temperature were the most significant variables for opening windows in the warm and cold seasons, respectively.

The essential properties governing the appropriate selection of phase change materials integrated into heavy structure buildings

This paper examines the essential properties governing the appropriate selection of phase change materials (PCMs) integrated into heavy structure buildings during different seasons. Nine PCMs with various thermophysical properties were evaluated, in the free-floating mode, under hot and dry climate conditions. The practical examined range of each property is extracted from the data of the nine considered PCMs. An indoor experiment was designed and constructed to validate the simulation models created with the EnergyPlus and Designbuilder building simulation programs. The results confirm that the large latent heat of fusion and low thermal conductivity of PCMs enhance the indoor thermal comfort at the same melting temperature. The Intensity of Thermal Discomfort (ITD) decreases from 51 to 36.4% as the energy density increases from 138.6 to 180.6 MJ/m3, for two PCMs with a melting temperature of 21 °C. As the PCM melting temperature increased from 21 to 29 °C, the ITD index decreased from 27 to 10 °C.day. Thus, the occupants’ thermal comfort enhances as the PCM melting temperature approaches the upper limit of the adaptive thermal comfort. In brief, the ITD sensitivity index (the relative change in ITD due to a relative change in PCM property) is highly affected by the PCM melting temperature (5.85), followed by latent heat (1.37), density (0.71), thermal conductivity (0.41), and the phase transition region (0.096) that has a negligible effect on the indoor thermal comfort.

THEORETICAL AND EXPERIMENTAL INVESTIGATION OF V-CORRUGATED SOLAR AIR HEATER FOR SPACE HEATING

This work is an effort to investigate the thermal performance of a V-Corrugated Solar Air Heater (SAH), which is intended for supplying heating to an office space having a floor area of 84 m2. Thermal performance investigation has been carried out both theoretically and experimentally. V-Corrugated SAHs have not been investigated for space heating in offices, hence this study aims to contribute by proposing and promoting them for this purpose. The load of the office space has been evaluated by the Energy Plus building simulation program as 4546 W. Thermal performance of the SAH is investigated by solving the governing equations with developed MATLAB code and concurrently by carrying out real-time monitoring of the operating parameters (e.g. component temperatures, air speed, etc.) of the SAH. It is aimed to obtain the temperature of each component of the SAH, useful heat output, thermal efficiency, number of SAHs and the corresponding area that is necessary to meet the heating load. It is found that 9 SAHs with 16 m2 are required to supply the target load for the experimental case and 6 SAHs with 10 m2 for the theoretical case.

A simulation-based framework to optimize occupant-centric controls given stochastic occupant behaviour

Occupant-centric control (OCC) strategies represent a novel approach for indoor climate control in which occupancy patterns and occupant preferences are embedded within control sequences. They aim to improve occupant comfort and energy efficiency by learning and predicting occupant behaviour (OB), then optimizing building operations accordingly. Previous studies estimate that OCC can increase energy savings by up to 60% while improving occupant comfort. However, their performance is subject to several factors, including uncertainty due to OB, OCC configurational settings, as well as building design parameters. To this end, testing OCCs and adjusting their configurational settings prior to implementation are critical to ensure optimal performance. Furthermore, identifying building design alternatives that optimize such performance given different occupant preferences is an important step that faces many logistical constraints during field implementations. This paper presents a framework to optimize OCC performance in a simulation environment, which entails coupling synthetic OB with OCCs that learn preferences. The genetic algorithm for multi-objective optimization is then used to identify the configurational settings and design parameters that minimize energy consumption and maximize occupant comfort under various occupant scenarios. To demonstrate the proposed framework, three OCCs were implemented in the building simulation program, EnergyPlus, and executed through Python packages to optimize OCC configurations and design parameters. Results revealed significant improvement of OCC performance when they were customized with the identified optimal configurational settings for different occupant scenarios. It was found that these optimal points could reduce energy consumption by up to 33% while improving occupant comfort by up to 28%, relative to a baseline scenario with non-optimized OCC implementation. The proposed framework aims to improve OCC performance in actual buildings and avoid discomfort issues that may arise during its initial implementation phases.

Optimizing occupant-centric building controls given stochastic occupant behaviour

Occupant-centric control (OCC) strategies represent a novel approach for indoor climate control in which occupancy patterns and occupant preferences are embedded within control sequences. They aim to improve both occupant comfort and energy efficiency by learning and predicting occupant behaviour, then optimizing building operations accordingly. Previous studies estimate that OCC can increase energy savings by up to 60% while improving occupant comfort. However, their performance is subjected to several factors, including uncertainty due to occupant behaviour, OCC configurational settings, as well as building design parameters. To this end, testing OCCs and adjusting their configurational settings are critical to ensure optimal performance. Furthermore, identifying building design alternatives that can optimize such performance given different occupant preferences is an important step that cannot be investigated during field implementations of OCC due to logistical constraints. This paper presents a framework to optimize OCC performance in a simulation environment, which entails coupling synthetic occupant behaviour models with OCCs that learn their preferences. The genetic algorithm for optimization is then used to identify the configurational settings and design parameters that minimize energy consumption under three different occupant scenarios. To demonstrate the proposed framework, three OCCs were implemented in the building simulation program, EnergyPlus, and executed through a Python package, EPPY to optimize OCC configurational settings and design parameters. Results revealed significant improvement of OCC performance under the identified optimal configurational settings and design parameters for each of the investigated occupant scenarios. This approach would improve OCC performance in actual buildings and avoid discomfort issues that arise during the initial implementation phases.

Development of a Reduced Order Model of Solar Heat Gains Prediction

The aim of this study was to elaborate and validate a reduced order model able to forecast solar heat gains as a function of the architectural parameters that determine the solar heat gains. The study focused on office buildings in Catalonia and Spain and their physical values were taken from the Spanish Building Technical Code and European Union Directive 2018/844. A reduced order model with three direct variables (solar heat gain coefficient, shade factor, window to wall ratio) and one indirect design variable (building orientation) was obtained and validated in respect to the International Performance Measurement and Verification Protocol. Building envelope properties were fixed and the values were taken from the national standards of Spain. This work validates solar heat gain coefficient as a primary variable in determining the annual solar heat gains in a building. Further work of developed model could result in building energy need quick evaluation tool in terms of solar heat gains for architects in building early stage as it has an advantage over detailed building simulation programs in terms of instant calculation and the limited need for predefined input data.

An efficient two-dimensional heat transfer model for building envelopes

A two-dimensional model is proposed for energy efficiency assessment through the simulation of heat transfer in building envelopes, considering the influence of the surrounding environment. The model is based on the Du Fort–Frankel approach that provides an explicit scheme with a relaxed stability condition. The model is first validated using an analytical solution and then compared to three other standard schemes. Results show that the proposed model offers a good compromise in terms of high accuracy and reduced computational efforts. Then, a more complex case study is investigated, considering non-uniform shading effects due to the neighboring buildings. In addition, the surface heat transfer coefficient varies with wind velocity and height, which imposes an addition non-uniform boundary condition. After showing the reliability of the model prediction, a comparison over almost 120 cities in France is carried out between the two- and the one-dimensional approaches of the current building simulation programs. Important discrepancies are observed for regions with high magnitudes of solar radiation and wind velocity. Last, a sensitivity analysis is carried out using a derivative-based approach. It enables to assess the variability of the solution according to the modeling of the two-dimensional boundary conditions. Moreover, the proposed model computes efficiently the solution and its sensitivity to the modeling of the urban environment.

An improved part-load airflow and power model for fan-powered terminal units with electronically commutated motors

Part-load performance models for fans driven by electronically commutated motors in fan-powered terminal units were developed based on the analysis of data from 36 series and parallel units provided by four manufacturers. Fan motors ranged from 0.33 to 1 hp (249 to 746 W) and maximum external static pressures from 0.5 to 0.75 in. w.g. (125 to 188 Pa). Part-load performance was estimated from the intersection of the duct system and fan curves. The intersection of the two curves determined the external static pressure for estimating both fan airflow and power. Part-load airflow and power fractions were then estimated by using the airflow and power at the maximum airflow controller setting as the reference. The part-load curve tracked close to the fan law curve at airflow fractions above 0.4. The deviation between the two curves was 18.8% at an airflow fraction of 0.4 and increased at lower airflow fractions. The power consumption of motor controller may contribute to the deviation from the fan law curve at airflow fractions below 0.4. The model can estimate the part-load performance for fan-powered terminal units with electronically commutated motors in building simulation programs considering the wide range in fan motor sizes and maximum static pressures in the dataset.

Simulation of NZE Green Building Design through Optimization of HVAC Systems

Around 40% of the electrical energy produced worldwide is consumed by the Buildings of Residential as well as Commercial types. Efficient usage and optimization of electrical energy leads to Nearly Zero Energy (NZE) Green Buildings and it helps in Economic growth and Social development in all countries. Apart from providing reduced energy consumption, Building energy optimization also minimizes the total energy costs, maximizes energy savings and consequently contribute less greenhouse gases to the environment. Though the installation cost of NZE Green building is quite high, the investment can be regained within the payback period with savings in the energy consumption. In this paper an Eco-friendly, Energy optimized, NZE Green building is designed by using efficient building simulation program known as BEOpt through HVAC technologies by considering various designing parameters at the designing stage and the distinguishments in the energy consumption, energy saving per year and CO2 emissions between conventional building and the designed prototype of NZE Green building are addressed

Integration Of Pipe Models In Delphin And NANDRAD

The component simulation program DELPHIN, which is usually used for component simulation including coupled heat and moisture transport, has also implemented a pipe model that is currently not documented. This model can be used for various purposes, including the modelling of heat input/output by surface heating/cooling systems in building structures. Furthermore, it allows an estimation of energy gains and storage potentials by ground collectors by considering the ground including the collector pipe, etc. The same pipe model is implemented isotropically in NANDRAD in order to model and to consider underfloor heating systems in the thermal building simulation. The implementation of this pipe model for DELPHIN (component simulation program) and NANDRAD (building simulation program) is described, investigated and documented in this paper for underfloor heating and cooling systems. Especially the heat transfer between pipe wall and fluid is discussed in more detail. Therefore, the parameterization and the flaw between the anisotropic tube model (two-dimensional heat radiation inside the component) in Delphin and the isotropic model (heat input to a component layer) in NANDRAD are being examined.

Validation of Models for the Calculation of Sun Positions and mapped Radiation on inclined Surfaces

In order to validate building simulation programs and to identify sources of errors in implemented models, it is necessary to evaluate individual physical effects step by step. For building simulations, the radiation load that hits buildings is a significant boundary condition. In order to reproduce these loads as accurately as possible, sun position models and weather data adapted to them are necessary. With the help of sun position models, the radiation data (usually available in horizontal or normal radiation) are mapped onto inclined and oriented surfaces. This paper examines a possible validation. For the analysis, different building simulation tools (NANDRAD, Radiance, IDA ICE, Modelica, TRNSYS, ETU Simulation) are compared with different time steps for specific dates using the result data azimuth and altitude. The locations are distributed over the northern and southern hemispheres, inside and outside the tropics and polar circles, and with large deviations from the standard meridian, thus allowing errors to be found in the implemented sun position models. In the following step, these building simulation platforms are compared with regard to their minutely radiation loads on building façades. Errors in the radiation mapping on inclined surfaces are hereby determined.

Identifying temporal properties of building components and indoor environment for building performance assessment

Buildings are dynamic thermal systems that require energy to maintain a comfortable indoor environment. Knowing these dynamics allows for identifying relevant building characteristics for assessing building energy performance. However, conventional building simulation programs take daily or monthly mean values for performance assessment. These often aggregate the errors and uncertainties between the theoretical and the actual building performance while missing the temporal dynamics on a minute or hourly basis. The objective of this study is to uncover the temporal aspects of building properties using data obtained by a wireless sensor network (WSN).Our analysis focuses on the following seven variables that are relevant for assessing building performance and retrofit measures: indoor air temperature, window opening instances, CO2 concentration, supply temperature from the heating system, outdoor air temperature, heat flux through the wall and the window. Using a single-family residence as a case study, we identify the temporal dynamics of these variables using polar plots on a high-resolution, 5-minute interval dataset. Following this, we define a set of conditional rules to study ‘expected’ and ‘unexpected’ impact of the six variables on the indoor air temperature. We observe strong temporal dynamics for certain building components, resulting in a large time lag. The conditional rule analysis also allows identifying energy saving potentials and factors contributing to the performance gap. For example, we observe high indoor air temperatures and at times when no occupants are present. Finally, we discuss the benefits of this approach with respect to building retrofit and energy performance gap analysis.

Estimating the sensitivity of design variables in the thermal and energy performance of buildings through a systematic procedure

Abstract In the performance-based design context, buildings can be evaluated through simulation procedures to estimate their performance in different environmental criteria. In this context, a crucial issue is the identification of the most influent input variables in some specific performance criteria, which can only be appropriately assessed through a robust sensitivity analysis approach. Thus, the objective of this study is to develop a method to estimate the influence of design variables in the thermal and energy performance of buildings through a systematic procedure, using building simulation programmes and statistical tools. The method has a broad scope of combining a local with a global sensitivity analysis using the Morris elementary effects method, and uncertainty analysis of the performance criteria. A case study of a low-income house located in southern Brazil was evaluated and simulated in the EnergyPlus™ programme. Four different performance criteria were considered (such as the degree-hours for heating and cooling and the energy consumption for heating and cooling). The simulation experiment considered 21 design variables, such as the thermal properties of the construction components, openings characteristics, airflow parameters and solar orientation of the house. The study identified the most influential design variable in each criterion, highlighting the thermal transmittance and the solar absorptance of the roof, and the ventilation area of the windows. The combination of the local and global approaches leads to a better understanding of the behaviour of the model and can help the decision-maker to optimise the building performance more accurately.

Impact of temperature and moisture dependent conductivity of building insulation materials on estimating heating and cooling load using typical and historical weather data

This paper investigates the impact on heating and cooling load estimation when effective thermal conductivity of materials is incorporated into building energy simulation in conjunction with historical weather data. Under current practice, thermal performance of building envelope systems is usually represented by a lumped nominal conductivity value. In reality, effective conductivity is influenced by many factors such as temperature and moisture content. To minimize computing time, building energy simulation is also conducted with typical meteorological weather data, which is sufficient in estimating average energy use of buildings, but lacks the ability to truly reflect building performance under long term weather conditions. Preliminary research has been conducted by simulating a typical residential house in Toronto using WUFI plus - a comprehensive hygrothermal building simulation program. Historical CWEED - 1998 to 2014 weather data and typical weather files CWEC-1990s and 2016 have been used for this work. The results shows:1)a reduced representativeness of typical weather data in building energy simulation as climate changes over time; 2)estimation using typical weather data is not representative of any individual historical year and 3)performance of insulation materials changes when temperature and moisture dependant conductivity is considered and affects the results of building energy simulation.