Building Energy Simulation Software Research Articles

Building Information Modeling/Building Energy Simulation Integration Based on Quantitative and Interpretative Interoperability Analysis

The integration between the building information modeling (BIM) methodology and the building energy simulation (BES) can contribute to a thermo-energetic analysis since the model generated and fed into BIM is exported to simulation software. This integration, also called interoperability, is satisfactory when the information flow is carried out without the loss of essential information. Several studies point out interoperability flaws between the methodologies; however, most of them occur in low-geometry-complexity models during quantitative experiments. The purpose of this research was to analyze the BIM/BES integration based on a quantitative and interpretative interoperability analysis of two buildings with complex geometries located on the UFU Campus (library and Building 5T) in Uberlândia, Brazil. To accomplish this, two geometries of each building were modeled, detailed, and simplified to analyze the data import, workflow, and model correction in the BES software. In the case of the library, the integration of Revit with DesignBuilder and IES-VE was analyzed, and in Block 5T, Revit was used with DesignBuilder and eQUEST. The BES software that presented the best integration with Revit for complex geometries was DesignBuilder, with the best performance being in the interpretative criteria. It was concluded that the simplification of complex geometries is essential for better data transfers. To determine the BES software that has better integration with BIM, a comprehensive evaluation is necessary, considering not only data transfers but also ease of working within BES software, the possibility of corrections in these, as well as the availability of tutorials and developer support.

Integrating Technology and Heritage Design for Climate Resilient Courtyard House in Arid Region

This research has investigated the sustainability and climate resilience of courtyard houses of adobe architecture in the UAE. It analyzed design effectiveness in terms of power consumption, CO2 emissions, thermal comfort, and daylight use, employing simulations to assess building structures and construction systems. Adopting a three-phase mixed-methods approach, the study began with a literature review on courtyard house design, construction, and environmental performance, emphasizing sustainable design and passive ventilation. The second phase involved a case study of a UAE courtyard house (Al Midfa), including site visits, interviews, and energy consumption and CO2 emission data collection. The final phase used building energy simulation software to model energy performance and evaluate passive ventilation's role in reducing energy consumption and CO2 emissions, with simulation results validated against real-world data. Advanced Sefaira simulations with the Energy Plus Engine identified one out of seven modified models (M5) as exceptionally thermally efficient, influencing the architectural design of the Al Midfa house. To transform the Al Midfa house into a sustainable climate-resistant structure, the research suggested retrofitting with new glazing and insulation on the inside of external walls and on the roof surface at a combined U-value of 0.4 W/m2to enhance energy efficiency without altering the exterior. A notable innovation was the use of injected cellulose insulation in wall systems, combining efficient insulation with architectural aesthetics, signifying a shift towards energy-efficient interior modifications. The study's findings contribute to the evolution of traditional house designs toward climate change resilience and a sustainable future. Doi: 10.28991/CEJ-2024-010-03-018 Full Text: PDF

Development of a dynamic demand response quantification and control framework for fan-coil air-conditioning systems based on prediction models

The air-conditioning demand response has gradually become an effective approach to reduce peak load of the national grid. However, with the increasing penetration of renewable energy generation sources, static performance evaluation of demand response that only fits one certain scenario cannot meet the demandsofthe times. To address this issue, a novel dynamic demand response quantification and control framework was built based on the secondary development of building energy simulation software and multilayer perceptron algorithm. In this framework, the demand response evaluation method can be used for different global temperature adjustment strategies under different external disturbances and internal loads, and the dynamic demand response control method could be further adopted for load reduction prediction and setpoint adjustment recommendation. The performance prediction model based on artificial neural network can predict the maximum load shedding during demand response period with an average absolute percentage error of 15 %, and the prediction error of load shedding intensity is ± 1 W/m2 for over 80 % of samples. Besides, the setpoint adjustment prediction model achieved an average absolute percentage error of 3 % while its prediction error is ± 0.25 °C for over 99 % of samples. The results prove that this research can provide reasonable suggestions for customers and load aggregators to achieve accurate control of setpoint adjustment in dynamic demand response conditions.

Building energy model automated calibration using Pymoo

This paper describes a two-stage calibration method, starting with detailed modeling and followed by optimization-based calibration. To put the proposed calibration method to the test, a real-life existing building was modeled using TRNSYS (Transient System Simulation Tool). The first stage approach emphasizes the significance of a detailed description of the building. In this regard, we compared the results of two different simulations: one that considers the dynamic behavior of infiltration and convective heat exchange, and the other that uses average values for the aforementioned quantities. The detailed model helped reduce the gap between simulation and measurement, where one of the simulated zones' mean absolute error (MAE) decreased from 1.11°C to 0.63°C. The calibration problem is viewed as an optimization problem in the second-stage approach, and the MAE between the simulated and measured values is regarded as an objective function to be minimized. In this work, we demonstrate the implementation of Pymoo (Multi-Objective Optimization in Python) as a calibration tool for building energy simulation software. The detailed energy simulation was fine-tuned using the genetic algorithm. The optimization results produced an MAE of around 0.3°C for both studied zones.

Design optimization of a solar system integrated double-skin façade for a clustered housing unit

The study compares a conventional double façade (DF), a building-integrated photovoltaic double façade (BIPV DF), and building-integrated photovoltaic thermal double façade (BIPVT DF), by considering the impact of the depth of the cavity between the photovoltaic system and the façade. The problematization aims at augmenting the function of an interstitial spaces between building envelope and programmed space both in terms of spatial expansion, but also in terms of energy production. The approach involves examining this space in terms of its spatial parameters and ability to accept an architecturally integrated solar system.To conduct the present analyses, three distinct systems were employed and applied to a sample thermal zone, where the cavity space shaped by the double façade was considered as a veranda space. The energy systems were modelled utilizing the commercial software DesignBuilder, and various dynamic simulations were performed using the building energy simulation software EnergyPlus for a representative South-Eastern Mediterranean weather zone. A parametric analysis was conducted, which involved varying the cavity depths from 0.25 m to 1.50 m.Results show that the conventional DF system demonstrates lower heating demands than the other systems, whereas the opposites occur for the cooling needs. Furthermore, an increase in the cavity depth between the PV system and the façade resulted in an increase in heating thermal loads and a decrease in cooling loads. The primary energy minimization approach provided interesting results, including the optimal depth cavity of the veranda (0.97 m).

An Integrated Decision-Making Framework for Mitigating the Impact of Urban Heat Islands on Energy Consumption and Thermal Comfort of Residential Buildings

Urban heat island (UHI) is a zone that is significantly warmer than its surrounding rural zones as a result of human activities and rapid and dense urbanization. Excessive air temperature due to the UHI phenomenon affects the energy performance of buildings and human health and contributes to global warming. Knowing that most of the building energy is consumed by residential buildings, therefore, developing a framework to mitigate the impact of the UHI on residential building energy performance is vital. This study develops an integrated framework that combines hybrid micro-climate and building energy performance simulations and multi-criteria decision-making techniques. As a case study, an urban area is analyzed under the Urban GreenUP project funded by the European Union’s Horizon 2020 Programme. Four different strategies to mitigate the UHI effect, including the current situation, changing the low-albedo materials with high-albedo ones, nature-based solutions, and changing building façade materials, are investigated with a micro-climatic simulation tool. Then, the output of the strategies, which is potential air temperature, is used in a dynamic building energy simulation software to obtain energy consumption and thermal comfort data of the residential buildings in the case area. Finally, a multi-criteria decision-making model, using real-life criteria, such as total energy consumption, thermal comfort, capital cost, lifetime and installation flexibility, is used to make a decision for decreasing the UHI effect on residential energy performance of buildings. The results showed that applying NBSs, such as green roofs and changing existing trees with high leaf area density ones, have the highest ranking among all mitigation strategies. The output of this study may help urban planners, architects, and engineers in the decision-making processes during the design phase of urban planning.

Stochastic scheduling for commercial building cooling systems: considering uncertainty in zone temperature prediction

This paper presents the first attempt to address the uncertainty in zone temperature prediction with stochastic optimization. The uncertain zone temperature is a process uncertainty and has not been considered in the existing stochastic optimization for building control. To fill this gap, we proposed a novel formulation of stochastic optimization to handle process uncertainty in building control. Specifically, we first examined the accuracy of a typical linear model for predicting zone temperature. We then formulated the scheduling of the building cooling system as a stochastic optimization problem over a 24-hour look-ahead period to minimize the electricity cost of the studied building cooling system. After that, we applied the proposed stochastic load scheduling (SLS) to a direct expansion (DX) cooling system that serves a medium office building. Through simulation with a detailed building energy simulation software, EnergyPlus, we evaluated the operational cost and the thermal comfort compared with a deterministic load scheduling. The operation cost of scheduling was found to vary with the level of zone temperature prediction uncertainty. The proposed SLS can mitigate the impacts of uncertain zone temperature predictions on both operational cost and thermal comfort. The evaluation results indicate that the proposed SLS works better when the uncertainty level is more significant.

Impact of heating and cooling loads on battery energy storage system sizing in extreme cold climates

Efficient operation of battery energy storage systems requires that battery temperature remains within a specific range. Current techno-economic models neglect the parasitic loads heating and cooling operations have on these devices, assuming they operate at constant temperature. In this work, these effects are investigated considering the optimal sizing of battery energy storage systems when deployed in cold environments. A peak shaving application is presented as a linear programming problem which is then formulated in the PYOMO optimization programming language. The building energy simulation software EnergyPlus is used to model the heating, ventilation, and air conditioning load of the battery energy storage system enclosure. Case studies are conducted for eight locations in the United States considering a nickel manganese cobalt oxide lithium ion battery type and whether the power conversion system is inside or outside the enclosure. The results show an increase of 42% to 300% in energy capacity size, 43% to 217% in power rating, and 43% to 296% increase in capital cost dependent on location. This analysis shows that the heating, ventilation, and air conditioning load can have a large impact on the optimal sizes and cost of a battery energy storage system and merit consideration in techno-economic studies.

Comparing economic benefits of HVAC control strategies in grid-interactive residential buildings

Energy consumption in buildings continues to rise with increased deployment of energy-consuming equipment such as Heating, Ventilation, and Air Conditioning (HVAC) amid a growing world economy. Renewable energy is projected to comprise a majority of the future electricity supply, but the intermittent nature of renewables means that consumption must respond to dynamic supply for optimal utilization. This paper proposes a novel HVAC control strategy for residential buildings using the adaptive comfort model, considering occupancy through probability and real-time information, and optimizing the HVAC schedule to reduce cost, maintain thermal comfort, and respond to the dynamic availability of renewable energy while being generalizable to different situations. To validate this approach, the Universal CPS Environment for Federation (UCEF) co-simulation platform is used to connect advanced building controls with the building energy simulation software EnergyPlus. Simulations are performed for a residential building in Sacramento, CA during a typical summer week. Economic impacts, energy consumption, and thermal comfort are analyzed for traditional, adaptive, and occupancy-based control strategies under demand-based, tiered, and fixed electric tariff systems. Simulation results show that occupancy consideration, adaptive thermal comfort, and optimization can reduce cost by 50.1 %, electricity consumption by 52.9 %, and discomfort by 56.2 % compared to traditional fixed setpoints. The ability of the proposed HVAC control strategy to shift energy consumption away from peak times under a demand-based tariff system is qualitatively analyzed and findings suggest that maximum load-shifting on a grid-scale is attained using occupancy consideration with optimized control and demand-based pricing. For individual residential buildings, similar economic benefits can be gained using the less-complex adaptive HVAC control strategy with existing tiered or simple electric tariff systems.

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.

Quantification and economic analysis of virtual energy storage caused by thermal inertia in buildings

The virtual energy storage caused by the thermal inertia of the building is the property and can participate in the demand response. However, the quantification of this virtual energy storage part is not clear. To determine the energy flexibility potential of building virtual energy storage, quantitative indicators such as charging and discharging time, and charging and discharging energy are proposed. The building model was created using the building energy simulation software EnergyPlus, and the quantification of each performance indicator of the building’s virtual energy storage was developed. Among them, the optimal charging time for this building model is 30 minutes, and the optimal discharging time is 60 minutes. Under the optimal virtual energy storage operation strategy, the charging energy is 57 kWh and the discharging energy is 84 kWh. Compared with the reference strategy, the building virtual energy storage operation strategy saves 48.76 yuan in single-day cooling electricity cost, with a 3% savings rate. The results show that building virtual energy storage has certain energy flexibility without additional investment and can generate certain economic benefits at the same time.

Exploring the effect of longwave radiation exchange on the energy balance of building façades in subtropical regions

The energy balance of the building façade affects the accuracy of building energy prediction. Although algorithms for energy balance have been applied in building energy simulation software, the longwave radiation exchange of building façades is often considered in a simple (implicit) way. This study proposes the concept of ambient equivalent temperature for quantifying the longwave radiation emitted by other buildings and trees in the surrounding environment. A more detailed radiation transfer model was developed to assess the energy balance of the longwave radiation of building façades. The input parameters of the model were simplified and the applicability of the model was improved by fitting the ambient equivalent temperature and meteorological parameters. Through quantitative and qualitative analysis, the effect of view factor parameters on the model output was assessed, and a universal value for the sky view factor (SVF) was determined to calculate the longwave radiation exchange of the building façade. The calculation accuracy of the longwave radiation was 71.4% higher in the simplified SVF model than in the original model. These findings provide a more accurate method for calculating the longwave radiation energy balance of building façades and have implications for building energy prediction and building energy efficiency design.

A Novel Approach for Optimizing Building Energy Models Using Machine Learning Algorithms

The current practice with building energy simulation software tools requires the manual entry of a large list of detailed inputs pertaining to the building characteristics, geographical region, schedule of operation, end users, occupancy, control aspects, and more. While these software tools allow the evaluation of the energy consumption of a building with various combinations of building parameters, with the manual information entry and considering the large number of parameters related to building design and operation, global optimization is extremely challenging. In the present paper, a novel approach is developed for the global optimization of building energy models (BEMs) using Python EnergyPlus. A Python-based script is developed to automate the data entry into the building energy modeling tool (EnergyPlus) and numerous possible designs that cover the desired ranges of multiple variables are simulated. The resulting datasets are then used to establish a surrogate BEM using an artificial neural network (ANN) which is optimized through two different approaches, including Bayesian optimization and a genetic algorithm. To demonstrate the proposed approach, a case study is performed for a building on the campus of the Florida Institute of Technology, located in Melbourne, FL, USA. Eight parameters are selected and 200 variations of them are supplied to EnergyPlus, and the produced results from the simulations are used to train an ANN-based surrogate model. The surrogate model achieved a maximum of 90% R2 through hyperparameter tuning. The two optimization approaches, including the genetic algorithm and the Bayesian method, were applied to the surrogate model, and the optimal designs achieved annual energy consumptions of 11.3 MWh and 12.7 MWh, respectively. It was shown that the approach presented bridges between the physics-based building energy models and the strong optimization tools available in Python, which can allow the achievement of global optimization in a computationally efficient fashion.

Adapting façade performances to climate change in northern Europe: Analysis of future scenarios for an office building in Stockholm

Future climate change will affect many human activities and sectors. Among those, the built environment will face several challenges about the varying climate conditions, including increased demand for summer cooling and related heat stress indoor conditions. In this framework, the paper presents the results of a recent study that investigated the global warming impacts on energy demand and indoor climate comfort for an office building in Stockholm over the next 50-60 years. The future climate conditions were investigated in 2070 and 2080 with different climate morphing approaches. Three different passive cooling solutions to decrease the cooling demand (such as external roller shade, electrochromic glazing, and internally ventilated shading) have been preliminarily assessed about thermal and optical properties, then integrated into the building energy simulation software IDA-ICE to evaluate the building energy performances regarding different Swedish climates, and finally economically estimated with a simplified LCC analysis. The results indicated that an increment of the cooling demand from 3 up to 24 kWh/m2 and a reduction of the heating usage of 20-50 % will be experienced in 50-60 years. The different weather data morphing approaches displayed the inherent uncertainties when future evaluations are performed, although similar weather patterns were found. The improvement of the solar and optical properties indicated a lower cooling and ventilation usage with reductions of about 10-16 %. The electrochromic technology reported the lowest cooling demand (decrease up to 24 %), while the internally ventilated shading option outperformed the others with anannual energy consumption 4-9 % lower and the lowest LCC.

Developing analytical model for nighttime cooling of internal thermal mass

Nighttime mechanical ventilation of internal building thermal mass has the potential to create energy flexibility by shifting peak cooling demand. The research on nighttime cooling of internal thermal mass is limited due to lacking simplified models by considering mass dimension and real-world discrete climate data like hourly outdoor air temperatures. This study develops an analytical model for nighttime cooling of internal thermal mass with a constant air change rate and hourly varied air temperatures. The analytical model is verified by both numerical, conduction transfer function methods, and ANSI/ASHRAE Standard 140-Case 600. The analytical model is applied to quantify the free cooling energy storage in 48 selected U.S. cities in different climate zones and in the 16 climate zones of California. Among the 48 cities, the maximum free cooling energy storage is reported in Santa Fe, NM with a total free cooling energy storage of 19.1 kWh m-2 a-1 and a net free cooling energy storage of 3.88 kWh m-2 a-1. Coastal regions in California are not suitable for nighttime ventilation of internal thermal mass. The maximum total free cooling energy storage in California achieves 27.5 kWh m-2 a-1, while the maximum net free cooling energy storage is 6.11 kWh m-2 a-1. The analytical model has a potential to be integrated into whole building energy simulation software to improve the calculation of the effect of internal thermal mass.

Effect of roof and wall material on energy usage for house cooling system in hot-humid climate

A model of a typical townhouse in Surabaya is simulated using building energy simulation software to analyse the energy consumption for cooling due to a severe climate. The assessment focused on the internal heat effects caused by the wall and roof material to meet the thermal comfort in a hot-humid climate region. A baseline building was a one-story building for one family with the typical load profile in Surabaya applied. The insulation thickness and material composition of the wall and roof were varied, whereas the internal cooling system was maintained at 25.5oC by the air-conditioning system. Results are presented and compared in terms of the annual energy consumption to meet the Indonesian standards for buildings. The best optimisation shows that the implementation of the insulation in the model can lower the annual energy consumption and conserve the energy by 37%.

Energy and thermal performance of optimised hollow fibre liquid desiccant cooling and dehumidification systems in mediterranean regions: Modelling, validation and case study

The heat and mass transfer of liquid desiccant for dehumidification and cooling has been investigated in recent years as a promising energy-efficient technology to improve indoor thermal comfort. Limited studies, however, investigated integration strategies and energy performance of such systems in real buildings with performance correlations in building energy simulation software. This research considers a low-rise Greek apartment building constructed in 1981 as a case study. The optimal system sizing is determined via parametric analysis in the numerical modelling stage as: 1) air mass flow rate of 57 m3/h; 2) solution mass flow of 2.0 l/min; 3) solution inlet temperature of 14 °C; 4) solution concentration of 40%. Besides, the thermal and electrical COP correlations are derived from the inlet air temperature and relative humidity, with maximum value of 0.51 and 7.32 (inlet air temperature: 35 °C and relative humidity: 90%). The results show that by integrating two photovoltaic thermal (PVT) panels and a 300L water-based thermal storage tank into the desiccant cooler, such a hybrid system could achieve maximum COP value of 4.21 in the case study. Besides, the summer supply air thermal unacceptable level is reduced by at least 65% from 2535 h to 884 h.

Integrating building energy simulation with a machine learning algorithm for evaluating indoor living walls’ impacts on cooling energy use in commercial buildings

The environmental challenges of climate change increase the energy usage and peak demands of buildings. Most extant studies of greenery systems focus on exterior applications such as green façades and roofs, which indirectly affect the indoor environment. Few studies have focused on quantifying the influence of indoor greenery systems on building energy consumption. The cooling effects of indoor greenery systems such as living walls are largely accounted for by the evapotranspiration (ET) process, in which water is transferred to the ambient environment through the evaporation from and transpiration of plants. Current building energy simulation software such as EnergyPlus does not have a module for modeling indoor greenery systems.In this study, an ET model was created using a machine learning algorithm—Gaussian Mixture Regression (GMR) based on experimental data. An indoor living wall model quantifying sensible and latent loads from the ET process was integrated with the energy simulation software—EnergyPlus through the Python plugin feature. The U.S. Department of Energy (DOE) medium-sized office building reference model was modified and used in this study to evaluate indoor living walls’ impacts on cooling energy use. A parametric study on leaf to floor area ratios (LFAR), orientations, distances from windows, and climates was conducted to evaluate the influence of each factor on indoor living walls’ performance. Cooling effects of indoor living walls were evaluated in three ASHRAE climates with high cooling demands. Observable cooling energy savings were obtained for the south, east, and west perimeter zones while savings for the north perimeter zone was negligible in all three climates. With the consideration of extra electricity use from direct expansion (DX) dehumidification devices for humidity control, the maximum cooling electricity savings in Los Angeles, CA are 25.1 % when LFAR = 1.5 for the south perimeter zone, 14.4 % when LFAR = 0.5 for the east perimeter zone, 0.3 % when LFAR = 0.3 for the north perimeter zone, and 14.5 % when LFAR = 0.5 for the west perimeter zone on the design day.

Energy performance evaluation and modeling for an indoor farming facility

Indoor farming refers to a method of growing crops on vertically stacked layers in a soilless cultivation system (e.g., hydroponics) in a controlled indoor environment. In comparison with conventional open-field farms, indoor farming has advantages such as significantly improved productivity and water use efficiency, and reduction in food miles. However, indoor farming facilities are energy-intensive for maintaining favorable crop growing conditions.In this study, we evaluated the energy performance of indoor farming operations using both measurements and simulation results. Key performance parameters were first analyzed based on utility bills and continuous measurements from an indoor farming facility. Several operational issues were identified for the mechanical system. An energy model for an indoor farming facility was created using building energy simulation software EnergyPlus and calibrated based on measurements. A novel modeling approach simulating the unique mechanical system—misting systems—for indoor farming was developed using one of the advanced features in EnergyPlus— energy management system (EMS). The energy model was used to evaluate the effectiveness of energy-saving strategies to improve the energy efficiency of facility operations. These include simultaneous heating and cooling, hardly met temperature setpoints and improperly controlled dampers. These energy efficiency measures include fixing motorized damper control, eliminating simultaneous heating and cooling, and having wider ranges of temperature setpoints. Up to 48.1% of reduction in annual natural gas consumption was predicted with energy-saving measures. In addition, the limitations of using building energy simulation software for indoor farming modeling were discussed.

Application of Variable Speed Drive to Overcome Air Conditioning Oversizing Issue

This paper was aimed at studying the impact of a variable speed drive in tackling the issue caused by an oversized air conditioning system. In this study, oversizing was determined by calculating the actual load demand in an operating air handling unit (AHU), and the formation of mould growth as an indicator of the failure of the AHU to dehumidify the air. A comparison was made between the performance of an actual operating AHU, the performance of the AHU after the installation of a variable speed drive (VSD), and an appropriately sized simulated model of a building using building energy simulation software. The study found that the installed AHU was oversized by 60% and that the installation of VSD is effective in reducing the effective total capacity to meet actual cooling load demand. This study also shows that VSD installation successfully eliminates favourable condition of mould growth by simultaneously reducing AHU capacity and room relative humidity. However, VSD installation should not be standard practice in lieu of proper calculations to determine the appropriate size for an air conditioning system. The simulation result shows that correctly sized air conditioning system will be able to maintain ideal room condition without incurring energy penalties associated with oversizing.