Building Energy Simulation Program Research Articles

Incidence of circular refurbishment measures on indoor air quality and comfort conditions in two real buildings: Experimental and numerical analysis

The application of Circular Economy to construction sector is a key to attain carbon neutrality, since it is responsible of 40 % of natural resource consumption. In this frame the importance of an efficient building refurbishment process throughout recycled material and renewable energy is fundamental. From an overview about building refurbishment emerges the need to investigate aspects related to Indoor Environmental Quality and the comparison between in-field measurements with output of dynamic simulation models. The present study aims to fill these two gaps by means an energy renovation of two real buildings in Greece. The work develops within the European project “Drive 0″, born to promote deep environmentally friendly retrofitting by means of circular renovation concepts. The methodological approach involves on-site monitoring of a series of parameters describing the energy, microclimate environmental and air quality, before and after the energy requalification. In addition, a numerical model developed in Building Energy Simulation program is calibrated and a Computational Fluid Dynamics is developed. From the in-field measurements emerges that, on one hand, the refurbishment of heating system shows a great improvement of indoor thermal conditions, with Total Volatile Organic Compounds concentration that sometimes exceed 3.0 mg/m3; on the other hand an integrated thermal insulation reduces infiltrations and changes the envelope behaviour, with a global energy saving of 30 % during winter and autumn periods.Another result of the study shows that a numerical model developed in Building Energy Simulation program and calibrated on energy consumption can greatly fit the local thermal comfort distribution of the occupant zone and predict the indoor air quality, if it outputs are used as input data in a Computational Fluid Dynamics study. These results can be beneficial to decision makers and designers for evaluating emitters positioning, opening design and mechanical ventilation strategies, aimed at reducing energy costs.

Assessing energy saving potential of wavelength-dependent passive daytime radiative cooler implemented with EnergyPlus by a roof model

The Passive Daytime Radiative Cooler (PDRC) exhibits potential in enhancing building energy conservation due to its high emissivity in wavelength between 8 μm and 13 μm. However, current building energy simulation programs, e.g., EnergyPlus, generally adopt a constant emissivity. To implement the wavelength-dependent PDRC model into EnergyPlus, a roof model was introduced to couple it with EnergyPlus to assess the energy saving potential of PDRC more reasonably. To assess the PDRC roofs, the impact of buildings constructed in different eras, building heights, and climatic conditions in China on building energy saving was investigated. The results showed that more energy was saved in regions with high cooling demands e.g., Shanghai and Guangzhou. Applying PDRC on roofs of buildings constructed pre 2001 in severe cold regions increased building energy consumptions. Furthermore, buildings with well insulated roofs coated by PDRC were not equally beneficial for reducing cooling demands in summer compared to buildings with non-insulated roofs, but heating demands in winter could be reduced. With an increase in building height, the energy saving potential of PDRC roofs was reduced. Therefore, a comprehensive analysis was imperative considering the local climates as well as the building itself when PDRC is to be applied.

Container farms: Energy modeling considering crop growth and energy-saving potential in different climates

Container farms (CFs), integrating plant factories into mobile prefabricated buildings, are emerging as a novel decentralized food production system to fortify sustainable urban development. However, the high energy demand needs to be optimized to promote wider CF application. This study investigates the energy efficiency of CFs in various climates, covering eight ASHRAE climate zones. The CF energy model was developed in the building energy simulation program EnergyPlus, fed with plant (lettuce) growth data measured from a real case. Four energy-saving methods were evaluated, with how different climates impact their potential. The results show that, in colder climates, the CF energy demand is lower and energy-saving methods are more effective. Air-side economizers reduce CF cooling demand by 3.5–58.3% from the hottest to coldest regions. Shifting lighting schedules into nocturnal lighting, as a cost-free method, reduces HVAC energy demand by up to 19.4% and can further reinforce the effectiveness of air-side economizers. The optimal envelope U-values should be among 0.5–3 W/m2-K, without being either poorly or overly insulated. Improving lighting efficacy can save over 20% total energy demand by reducing intense lighting power. We conclude that locating CFs in colder climates presents greater potential in energy conservation along with benefits for improving urban sustainability. The results can support decision-making for cleaner CF design and operation, and the methodology could be replicated to other indoor food production systems.

Façade typology development in high-rise office building envelope

AbstractThe design of the envelope in high-rise office buildings is a task of great importance as it can impact the entire building's energy performance. The study presented in this paper is an extension of a previous work reporting on the optimization of the façade and the shading systems of an east-west facing high-rise office building. This study aims to investigate the façade geometry design factors for other potential orientations, e.g., south, south-east, and south-west directions. The IDA ICE 4.8 complex dynamic building energy simulation program was used to assess thermal and lighting simulations. The optimization results revealed the best-performing façade configurations, appropriate for each orientation examined in terms of thermal comfort, visual comfort, and energy consumption.

Potential and Benefit of Green Roof Energy Renovation of Existing Residential Buildings with a Flat Roof in Belgrade

Green roofs are considered to be one of the optimal tools for saving energy and protecting the environment in developed countries. In this paper, an analysis of the possible application of green roofs on existing residential buildings with flat roofs is presented. In the economic analysis, models of existing buildings in Belgrade, with two different types of green roofs, are studied. A key indicator of investment profitability in this investigation is the net present value (NPV) of the green roof project. Besides the private economic impact, other aspects of green roof applications, significant for sustainable development, have been highlighted. The values of the reductions in the annual energy needed for heating and cooling are compared for different scenarios. A maximum energy saving of 22% in the heating season is determined in the building energy simulation program for the model with an intensive green roof. Life cycle profit analysis was based on the probabilistic approach. The corresponding variance-based sensitivity analysis determined the impact of various parameters on the final result. In all models, the first order sensitivity index, which measures the impact of the number of residential units on the NPV, ranges from 12.2% to 63.6%. Sensitivity analysis showed that the benefit of property value increase has the highest influence on the calculated NPV in scenarios that account for this benefit. The obtained results in those scenarios indicate that the most probable NPV at the end of the life cycle is EUR 43/m2 and EUR 82/m2 for extensive and intensive green roofs, respectively.

Güneş enerjisi destekli ısıtma sistemlerinde güneş kolektörü ve sıcak su deposu kapasitesi seçimi için parametrik analiz

In this study, a study was carried out on integrating solar assisted systems into heating systems in a building. For the study, an existing building was modeled with the DesignBuilder building energy simulation program. The scenarios were created on the model to determine the number of solar collectors and the capacity of the water tank used in solar assisted heating systems. The primary energy consumption, global costs and payback periods of the scenarios created were obtained by parametric analysis. According to the results obtained, the solution that reduces primary energy consumption the most is SYS20, the solution that reduces the global cost the most is SYS1 and the optimum solution is SYS7. Considering the payback periods, SYS1 was determined as the most appropriate solution to be applied in the study since it was the solution with the minimum payback period. At the end of the study, it was seen that primary energy savings could be reduced by up to 47% with solar energy assisted heating system. Thanks to the study, a reference source for the number of solar collectors and water tank capacity selection in Isparta and provinces with similar climates has been created.

Experimental and numerical investigations on the heat transfer characteristics of a real-sized radiant cooled wall system supported by machine learning

Despite the extensive utilization of radiant air conditioning units in rooms, challenging points of design associated with the calculation of cooling load are still present. Except for the radiant wall cooling studies aimed at conducting heat transfer-focused analyses carried out by the authors of this investigation, neither experimental nor computational studies exist in the related literature. The current experimental and computational study aims to address the deficiencies in the radiant-cooled wall problem. Differing from other conditioned rooms, the heat is exposed through the back surface of the analyzed wall, whose heat flux range lies between 1.60 and 10.84 W/m2. The total, radiative, and convective heat transfer coefficients of 7.78, 5.13, and 2.52 W/m2.K are acquired as values for use in building energy simulation programs. Seven different artificial neural network models are designed to estimate the total, radiative, and convective heat transfer coefficients and heat transfer rates. Dependency analyses are also performed using various inputs in the investigated numerical models. The margin of deviation values computed for six different output factors are found below −1.80%, the mean square error values are less than 1.51E-04, the R values are greater than 0.98, and the data points do not surpass the 10% deviation line. Artificial neural networks have been found to outperform well-known correlations in estimating experimental results. Extensive measured experimental data are presented for the sake of other researchers numerical modelling and validation issues. Building energy simulation software designers and engineers in the field of thermal comfort are thought to benefit from these findings.

Evaluating the Use of Waste Materials in Building Envelopes for Energy Efficiency: A Theoretical and Empirical Study

With the increasing interest in energy-efficient building design, building energy simulation programs are increasingly employed in the design process to help architects and engineers decide which design alternatives save energy more. This research is an empirical and analytical study using a ready-made software package to evaluate the energy efficiency performance of building envelopes using recycled waste material. These methods used available environmental alternatives material in the experiment. To verify the results, two practical cases are tested. Validation is examined by comparing experimentally measured data and computational simulation data during the same summer period time. Real cases with small-scale actual rooms that were used to generate data to validate numerical models. The focus of this study is to compare the experimental measured thermal behavior of traditional and waste materials in residential buildings with simulation results. This comparison allows determining the effect of using waste material in southern walls of a residential building at an indoor temperature during hot summer days, in which the measurements took place during June and July. Meanwhile, the results indicated that the indoor temperature of real models was higher than the temperature of simulation models with about 3°C to 6 °C at measuring time (six summer days). This increase is about o 5.4% to 11.8%. The findings also revealed that the use of waste material in the southern wall of a residential building could decrease the indoor temperature by about 0.2 - 3°C degree compared with the traditional material at the same wall. It means that waste materials in southern walls could decrease 0.9% to 4.1% of indoor temperature compared with traditional materials. This study found that the analytical and empirical small-scale models could contribute to determining the usage of waste material at southern walls of residential buildings. Also, the comparison of the results indicates that Design-Builder can predict indoor temperature to achieve thermal comfort with good accuracy and it can be used by researchers and designers to evaluate the thermal performance of buildings.

Simulation-supported design of high-rise office building envelope

Abstract The reduction of energy consumption is a major issue nowadays that should be considered during the design process. High-rise buildings represent a building type with significantly high energy consumption. They serve typically as offices with fully glazed façades, generating considerable energy demand. This study aims to optimize the envelope and the shading systems of a high-rise office building (Middle Europe). For this purpose, multiple façade variants were tested by assessing the thermal and visual comfort, as well as energy demand. The IDA ICE 4.8 building energy simulation program was used for thermal and lighting modeling and to carry out building physics calculations. Results revealed the best performing, optimized façade configuration in terms of comfort and energy efficiency.

유전알고리즘을 이용한 오피스 건물의 피크전력 감소를 위한 일일 예냉설정온도 최적화 연구

In this paper, the peak electric power of an office building was reduced with a precooling strategy. Precooling was conducted two hours before work time. Set-point temperatures for precooling were optimized using a genetic algorithm to minimize daily peak electric power. Searching for optimized set-point temperature was conducted with an objective function that was developed with a multiple regression model. The regression model used dry-bulb temperature, relative humidity, and the set-point temperature as the input parameters, and set daily peak power of the building as the output parameter. Model data were collected from ‘EnergyPlus’, a building energy simulation program. The applied building was ‘Medium Office’ provided by EnergyPlus. Based on our results, there was a 0.71~5.64% reduction in daily building peak power with the application of the optimized precooling setpoint temperature. In addition, the reduction of electricity cost due to the reduction of peak electric power was observed.

Implementation of a coupled simulation framework with neural network and Modelica for fast building energy simulation considering non-uniform indoor environment

The coupling of computational fluid dynamics (CFD) and building energy simulation (BES) program enables the simultaneous consideration of the indoor environment quality and building energy performance in architecture design. Meanwhile, it improves the energy simulation accuracy for non-uniform indoor environments as compared to a standalone BES. However, a CFD-BES coupled simulation confronts a great challenge in that conducting high-fidelity CFD simulations is time-consuming, making it impractical for scenarios requiring a large number of CFD executions. In this study, an innovative coupled simulation framework is proposed and implemented for fast energy simulation considering non-uniform indoor environments. As a surrogate for CFD, a neural network (NN) for achieving a fast and accurate prediction of the indoor air distribution is coupled with a Modelica-based energy simulation program. The crucial functionality of the coupled simulation framework is validated against the results of a coupled simulation between fast fluid dynamics (FFD) and Modelica. Furthermore, the framework is tested with a more complicated case to explore its feasibility and potential for practical applications. Compared to a standalone BES, the coupled simulation considering a non-uniform environment demonstrates more reasonable results regarding heat transfer calculations and system operations. Moreover, the proposed framework requires only 52 s to execute a simulation with a physical process of 24 h, resulting in an approximately 94% reduction in calculation time relative to a coupled simulation between CFD and BES.

Realization of bi-level optimization of adaptive building envelope with a finite-difference model featuring short execution time and versatility

The application of adaptive building envelope (ABE) is an important approach to energy conservation and carbon emissions reduction in buildings. In order to achieve high performance, both the design and control of an ABE system require optimization. Bi-level optimization is proposed for this type of problem. The greatest challenge to the application of bi-level optimization is the tremendous amount of computational resources required to solve the problem. This paper realizes bi-level optimization of ABE with a finite-difference (FD) model written in MATLAB. This FD model, compared to packaged building energy simulation (BES) programs like EnergyPlus, can potentially shorten the execution time by more than 84% and model various ABE systems whose modelling is not supported by packaged BES programs. The design and control of a ventilated façade is optimized using this model as an example. It is found that performance of the explicit constraint and the implicit constraint is similar, but their applicability is different. Moreover, design parameters have a great impact on the model predictive control (MPC) sequence. MPC, on the other hand, has the ability to reduce the ratio of the energy cost of the worst design to that of the best design from 7.41 to 3.63.

A methodology for mapping the performance of variable-speed residential cooling equipment using load-based testing

A load-based testing methodology has recently been developed for laboratory testing of residential cooling equipment with its integrated controls that employs a model to emulate building dynamics and their interaction with equipment controls. This approach is part of a new performance rating standard that utilizes a bin method to estimate a seasonal energy efficiency (CSA EXP-07, 2019; Cheng et al., 2021). However, a “holy grail” for future equipment performance rating is to be able to map equipment performance using load-based testing results and then implement the map as a "model" in building energy simulations to generate seasonal performance ratings that are specific to various building and climate types. With this goal in mind, this paper presents a performance mapping methodology that incorporates a “gray-box” model structure that uses inputs that are consistent with building energy simulation programs (sensible cooling load and equipment inlet conditions) and outputs key performance metrics (total sensible and latent cooling rates, power consumption, and COP). A strategy and a test matrix for training the model with a relatively small number of testing points were established by developing a successive optimization approach to identify the best 12 test conditions to apply for training from a larger set of available data. In order to develop, demonstrate, and validate the modeling and training approach, load-based laboratory testing was set up within psychrometric chambers and testing was performed to generate 39 quasi-steady-state data points over a range of loads and boundary conditions with a test unit operating with its normal integrated controls. The best model trained using the optimal training subset of 12 data points was able to represent the equipment performance across the operating envelope within ±10%.

Informing the design of courtyard street blocks using solar energy models: a case study of a university campus in Singapore

This study discusses the interplays between urban form and energy performance using a case study in Singapore. We investigate educational urban quarters in the tropical climate of Singapore using simulation-based parametric geometric modelling. Three input variables of urban form were examined: street network orientation, street canyon width, and building depth. In total, 280 scenarios were generated using a quasi-Monte Carlo Saltelli sampler and Grasshopper. For each scenario, the City Energy Analyst, an open-source urban building energy simulation program, calculated solar energy penetration. To assess the variables’ importance, we applied Sobol’ sensitivity analysis. Results suggest that the street width and building depth were the most influential parameters.

The dynamic thermal energy simulation of historic buildings in Mediterranean climate: knowledge and simplification

The European built heritage is strongly characterized by “historic buildings”. This built heritage represents an important cultural resource, constitutes a public good and testifies the community identity. The current challenge to reduce the consumption of energy resources is aimed at the requalification of this built heritage. To meet this challenge, energy analysis is increasingly being used also for historic buildings through complex building energy simulation program. Although their application to modern buildings leads to significant results, in the modelling of historic buildings there are numerous approximations and uncertainties relating to materials, thermal bridge, geometry and construction techniques which can lead to inaccurate results. Furthermore, the lack of input data and the complex geometries make even more difficult to determine the correct energy performance of the simulated built heritage. To the aim of investigates the validity, accuracy and reliability of Open BIM software, widely used by Architects and Engineers, based on the simple hourly method (EN ISO 52016-1), an energy analysis of historic building was performed. The work is intended to provide a calculation method to evaluate energy performance (heating, cooling, lighting, etc) of historic buildings (load bearing masonry structure with vaults) with the use of simple dynamic models, which comply with Italian energy laws and regulations, and to predict the energy saving potentials related to building retrofit actions.

Application Method of a Simplified Heat and Moisture Transfer Model of Building Construction in Residential Buildings

Several building energy simulation programs have been developed to evaluate the indoor conditions and energy performance of buildings. As a fundamental component of heating, ventilating, and air conditioning loads, each building energy modeling tool calculates the heat and moisture exchange among the outdoor environment, building envelope, and indoor environments. This paper presents a simplified heat and moisture transfer model of the building envelope, and case studies for building performance obtained by different heat and moisture transfer models are conducted to investigate the contribution of the proposed steady-state moisture flux (SSMF) method. For the analysis, three representative humid locations in the United States are considered: Miami, Atlanta, and Chicago. The results show that the SSMF model effectively complements the latent heat transfer calculation in conduction transfer function (CTF) and effective moisture penetration depth (EMPD) models during the cooling season. In addition, it is found that the ceiling part of a building largely constitutes the latent heat generated by the SSMF model.

Coupling spectral-dependent radiative cooling with building energy simulation

As a passive cooling strategy without energy consumption, radiative cooling has attracted considerable attention, especially in the building field. Building energy simulations have been conducted to identify the benefits of this technology in buildings. However, in existing studies, constant emissivity was used for radiative cooling materials in the building energy simulation programs, which can cause certain errors. To tackle this problem, this study developed a method to couple the spectral-dependent radiative cooling with building energy simulations in EnergyPlus. Compared with the existing constant-emissivity model, the proposed coupled model can further consider the influence of spectral-dependent emissivity, material surface temperature, and precipitable water on the radiative cooling power in EnergyPlus. Based on the results in a typical strip mall in New York, the radiative cooling power calculated by the proposed spectral-dependent model can be significantly different from that by the existing constant-emissivity model. However, since the energy saving from radiative cooling was relatively small compared with the total energy consumption, the differences in annual cooling electricity and heating natural gas consumption calculated by both models were not significant. Furthermore, case studies in five cities using the proposed model showed that using a broadband radiative cooling roof on a typical strip mall would reduce the annual electricity consumption for cooling, while increasing the annual natural gas consumption for heating. The coupling of spectral-dependent radiative cooling with building energy simulation would improve the accuracy of energy performance assessment for buildings with radiative cooling technology.

Simulation-based optimization of smart windows performance using coupled EnergyPlus - NSGA-II - ANP method

Windows are one of the most important parts of buildings responsible for wasting energy. In recent years, research works have shown that using smart windows is one of the most efficient methods to reduce the negative effects of glazings on buildings' energy consumption. In this paper, the performance of smart windows, including three types of electrochromic and five types of thermochromic ones, on reducing buildings energy consumption has been studied in four major climate regions of Iran (i.e., continental with dry summer, temperate with dry summer, steppe arid, and desert arid). Besides, to optimize the design parameters, including the building orientation, the window dimensions, and the glazing specifications, single and multi-objective genetic algorithms have been applied. The algorithms, coded in MATLAB, have been coupled with EnergyPlus building energy simulation program through jEPlus software as an interface. The results show that in the optimized case, using thermochromic and electrochromic windows decreases the annual total energy consumption 29.6% to 44.7%, and 9.3 to 26.3%, respectively, relative to the base model. The developed method and its results help to build energy engineers and architects to make the most effective decision on building parameters, which improve the energy efficiency of buildings.

Assessing the effects of urban street trees on building cooling energy needs: The role of foliage density and planting pattern

This study presents an one-way coupling approach between the ENVI-met microclimate model and the EnergyPlus building energy simulation program, to assess the effect of the urban greenery on the improvement of the buildings’ cooling energy needs, in a dense urban area in Thessaloniki, Greece. Three commonly encountered urban tree species with different foliage densities are analyzed, whereas 2 different planting patterns are also considered. The obtained results indicate that the potential of trees on cooling the ambient air temperature and regulating the buildings cooling energy needs is mainly attributed to the radiative shading and the respective reduction of the solar heat gains of the exposed building façades. Moreover, the reduction of the building’s cooling energy demand due to the addition of trees is directly related to their foliage density and their planting pattern. The higher energy savings up to 54 % have been achieved when the trees formed a continuous shading canopy and for the Leaf Area Density of 2.5 m2/m3. Yet, the cooling potential of street trees has been found rather minor when they were not tall enough to shade the biggest part of the outer building façade.

Modeling of Building Energy Consumption by Integrating Regression Analysis and Artificial Neural Network with Data Classification

With the constant expansion of the building sector as a major energy consumer in the modern world, the significance of energy-efficient building systems cannot be more emphasized. Most of the buildings are now equipped with an electric dashboard to record consumption data which presents a significant scope of research by utilizing those data in energy modeling. This paper investigates conventional regression modeling in building energy estimation and proposes three models with data classifications to improve their performance. The proposed models are regression models and an artificial neural network model with data classification for predicting hourly or sub-hourly energy usage in four different buildings. Energy data is collected from a building energy simulation program and existing buildings to develop the models for detailed analysis. Data classification is recommended according to the system operating schedules of the buildings and models are tested for their performance in capturing the data trends resulting from those schedules. Proposed regression models and an ANN model with the recommended classification show very accurate results in estimating energy demand compared to conventional regression models. Correlation coefficient and root mean squared error values improve noticeably for the proposed models and they can potentially be utilized for energy conservation purposes and energy savings in the buildings.