Building Energy Model Research Articles

Thermal energy performance of compressed earth building in two different cities in Moroccan semi-arid climate

The assessment of the thermal energy performance of earth-based buildings with respect to the semi-arid Mediterranean climate of Morocco is scarce, even if the country is historically known for its earthen dwellings. According to this need, this work aims to understand and evaluate the indoor thermal comfort and energy performances of passive building in two different locations. We have used EnergyPlus modeling tool and in addition, the monitored ten-day indoor temperatures in two different thermal zones in the test prototype to justify the empirical validation of Building Energy Model. The findings demonstrate, the use of compressed earth blocks coupled with passive design strategies provides better comfort and great sustainability. Thus, the summer discomfort hours are reduced about 12% for both cities compared to conventional building. However, the combination of semi-arid climate-responsive passive design has allowed to reach a thermal energy intensity reduction difference from 20 to 65 kWh.m−2. y−1.

Building energy consumption under occupants’ behavior uncertainty in pre and post-renovation scenarios: a case study in Italy

In Europe, the energy renovation of the existing building stock is a great opportunity to significantly reduce energy consumption and greenhouse gas (GHG) emissions and reach the European sustainability targets. In this framework, building energy simulations (BES) tools are very useful in verifying energy retrofit measures' effectiveness and compliance with national standards. However, an inaccurate numerical prediction, the so-called “performance gap” between measured and numerical performance, is often obtained, mainly due to the inherent uncertainty of model input. Due to its stochastic nature, the occupants’ behavior (OB) is considered among the key contributors to this gap. However, the most recent Building Energy Model (BEM) approaches adopt deterministic hourly-defined profiles for characterizing OB, thus neglecting the related uncertainty. In this work, the impact of OB uncertainties on energy consumption (EC) prediction is evaluated by adopting a Karhunen-Loève Expansion sampling technique, used to randomly perturb OB profiles such as heating setpoint (HS), internal thermal loads (IL), and windows opening (NV). Two BEMs of a typical Italian residential building in pre- and post-renovation scenarios are considered and calibrated on real EC data. The results demonstrated that HS uncertainty has the highest impact on EC in all scenarios. Moreover, the higher the energy performance of the building, the higher the impact of OB, especially for IL and NV patterns.

Automated energy performance certificate based urban building energy modelling approach for predicting heat load profiles of districts

Urban building energy modelling (UBEM) for analysing buildings in their spatial and functional context is an arising method. Only a few UBEM procedures use detailed building simulation tools, which are essential for high temporal and spatial resolution. This paper aims at developing a detailed automated physical bottom-up UBEM framework based on archetypes using Energy Performance Certificate data for predicting hourly heat load profiles of residential buildings. Simulation results are compared to and validated with measurements of two district heating networks and values from the TABULA typology. A comparison of the simulated hourly heat load profile for space heating and domestic hot water with measurement data results in a CV(RMSE) of 0.3, NMBE of 0.085, R2 of 0.85 and r of 0.94 for a sample size of 66 residential buildings, solely based on an estimation of the 3 classification criteria of the archetypes (building period, building condition and building type) and an estimation of the conditioned gross floor area for each measured building. Hence, the model can be declared as calibrated according to acceptance criteria in literature.

Thermal heat transfer and energy modeling through incorporation of phase change materials (PCMs) into polyurethane foam

Decreasing HVAC energy consumption through insulation effectiveness enables low energy use, low carbon emissions, and high thermal comfort in sustainable buildings. PCMs for energy storage are an innovative option, but in buildings, their high thermal conductivity is a concern.The PCM latent heat of fusion and thereby it’s transition temperature has been a key parameter in PCM selection but little is known of the effect of PCM on building energy contributions arising from increasing thermal conductivity (k), density (ρ), and specific heat capacity (Cp). To resolve this, in this paper, two PCMs with transition temperature above and below the outdoor temperature incorporated in highly porous flexible polyurethane foam were evaluated using a one-dimensional (1D) foam core panel built in COMSOL Multiphysics®. The simulations were run using the summer season in a humid subtropical climate zone with an outdoor temperature of 35 °C. It was found that apart from the latent heat of fusion, the combined effects of high ρ and Cp could mitigate the increased k of PU-PCM composites to improve the insulation performance. Building energy modeling using EnergyPlus with the foam core incorporated into a structural insulated panel (SIP), revealed that the use of PCMs regardless of the transition temperature could reduce annual electricity consumption by 15–20%, with a significant amount of energy savings arising from the cooling load in summer.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change.

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization. Urban building energy modeling (UBEM) is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations, supporting the implementation of carbon emission reduction policies. Currently, most studies focus on the energy performance of archetype buildings under climate change, which is hard to obtain refined results for individual buildings when scaling up to an urban area. Therefore, this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas, by taking two urban neighborhoods comprising 483 buildings in Geneva, Switzerland as case studies. In this regard, GIS datasets and Swiss building norms were collected to develop an archetype library. The building heating energy consumption was calculated by the UBEM tool-AutoBPS, which was then calibrated against annual metered data. A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%. The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results showed a decrease of 22%-31% and 21%-29% for heating energy consumption, an increase of 113%-173% and 95%-144% for cooling energy consumption in the two neighborhoods by 2050. The average annual heating intensity dropped from 81 kWh/m2 in the current typical climate to 57 kWh/m2 in the SSP5-8.5, while the cooling intensity rose from 12 kWh/m2 to 32 kWh/m2. The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7% and 18.6%, respectively, in the SSP scenarios. The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

TOM.D: Taking advantage of microclimate data for urban building energy modeling

TOM.D: Taking advantage of microclimate data for urban building energy modeling

Urban building energy modeling from geo-referenced energy performance certificate data: Development, calibration, and validation

Urban building energy models (UBEMs) are considered as applicable tools for urban energy planning. Model developers use different strategies to simulate urban building energy use appropriately, and yet they are often doing so in the absence of high-quality data. While data collection is challenging in many cases, in Sweden, the availability of national databases is relatively good and is expected to facilitate the modeling procedure considerably. This study aims to develop, calibrate and validate an UBEM using available national data, including GIS-based property maps and energy performance certificates (EPCs). The developed UBEM offers an automated framework for constructing simple building-level models from open data and conducting energy simulations in EnergyPlus. The developed UBEM was calibrated and validated for two case study cities in Sweden, Borlänge and Uppsala, where the mean absolute percentage error (MAPE) between simulated results and EPC data was 26% and 22%, respectively. Furthermore, a downward trend was observed in the MAPE with increasing spatial aggregation from building to district and city levels (from 26% to 21% and 10%), which highlights the performance of the UBEM in this study to support accurate urban-scale energy analyses for buildings.

Rapid Building Energy Modeling Using Prototype Model and Automatic Model Calibration for Retrofit Analysis with Uncertainty

Building performance simulation can be used for retrofit analysis. However, it is time-consuming to create building energy models for existing buildings. This paper presented and implemented a rapid building energy modeling method for existing buildings by using prototype models and automatic model calibration for retrofit analysis with uncertainty. A shopping mall building located in Changsha, China, was selected as a case study to demonstrate the rapid modeling method. First, a toolkit named AutoBPS-Param was developed to generate building energy models with parameterized geometry data. A baseline EnergyPlus model was generated based on the building’s basic information, including vintage, climate zone, total floor area, and percentage of each function type. Next, Monte Carlo sampling was applied to generate 1000 combinations for fourteen parameters. One thousand EnergyPlus models were created by modifying the baseline model with each parameter combination. Moreover, the 1000 simulation results were compared with the measured monthly electricity and natural gas usage data to find 29 calibrated solutions. Finally, the 29 calibrated energy models were used to evaluate the energy-saving potential of three energy conservation measures with uncertainty. The retrofit analysis results indicated that the electrical energy saving percentage of chiller replacement ranged from 1.57% to 13.51%, with an average of 8.27%. The energy-saving rate of lighting system replacement ranged from 1.92% to 11.66%, with an average of 6.43%. The energy-saving rate of window replacement ranges from 0.31% to 1.81%, with an average of 0.55%. The results showed that AutoBPS-Param could rapidly create building energy models for existing buildings and can be used for retrofit analysis after model calibration.

A combined workflow to generate citywide building energy demand profiles from low-level datasets

Urban building energy models provide an efficient way to estimate the district's energy demand and have been vigorously developed over the years. However, due to the lack of crucial information about building's physical descriptions, existing datasets cannot always meet the significant data demand for modeling. In addition, it is challenging to balance simulation time while ensuring simulation accuracy. Therefore, in order to solve the above difficulties, this paper makes some improvements and optimizations based on reduced-order building modeling and experience-based archetypes and presents a novel two-step approach combining bottom-up and top-down methods. A case study in a suburban district in Germany was chosen as a sample test case to demonstrate these workflow automation tools. The proposed workflow achieved a mean absolute percentage error of less than 2% for citywide energy demand prediction using low-level public data, compared to an error of 15% in the scenario without public data. The results indicate that although there was a lack of specific input data, the presented workflow was capable of obtaining accurate results with reduced computation time. The presented methodology offers an efficient way to establish urban building energy models using low-level datasets and provide relatively accurate results on a district level.

New ventilation design criteria for energy sustainability and indoor air quality in a post Covid-19 scenario

The Covid-19 outbreak raised great attention to the importance of indoor air quality in buildings. Even if the Covid-19 epidemic is nearing an end, all stakeholders agree that increasing outside air flow rates is beneficial for decreasing the likelihood of contagion, lowering the risk of future pandemics, and enhancing the general safety of the interior environment. Indeed, diverse concerns raised about whether the ventilation standards in place are still adequate. In this context, this research intends to assess the suitability of current ventilation standards in addressing the current pandemic scenario and to offer novel criteria and guidelines for the design and operation of HVAC systems, as well as useful guidance for the creation of future ventilation standards in a post-Covid-19 scenario. To that end, a comprehensive analysis of the ANSI/ASHRAE 62.1 is carried out, with an emphasis on its effectiveness in reducing the risk of infection. Furthermore, the efficacy of various ventilation strategies in reducing the likelihood of contagion has been investigated. Finally, because building ventilation is inextricably linked to energy consumption, the energy and economic implications of the proposed enhancements have been assessed. To carry out the described analysis, a novel method was developed that combines Building Energy Modelling (BEM) and virus contagion risk assessment. The analyses conducted produced interesting insights and criteria for ventilation system design and operation, as well as recommendations for the development of future standards.

Parametric Assessment of Building Heating Demand for Different Levels of Details and User Comfort Levels: A Case Study in London, UK

The Level of Detail (LoD), a parameter used to define the information contained in building models, is an important factor to consider in modeling building energy at the urban scale. In this research, we conducted a parametric study regarding the data requirements for the estimation of the annual residential heat demand in London. More particularly, the requirement of the observation of the actual roof type (LoD2) and the window-to-wall ratio (LoD3) was examined in two different case study areas. Meanwhile, an adaptive comfort level study was implemented using LoD5 models, and its results were assessed holistically with the heat demand to reveal the energy performance of the buildings. The results showed that there was a minor difference in the upgrade of a lower to higher LoD regarding these parameters. At an urban scale, the energy demand of buildings could be estimated using an assumption of archetypes and building ages. However, with a scalable parametric script developed in places, models with a high LoD could provide more detailed insights in the energy performance assessment without generating excessive workload.

How spatio-temporal resolution impacts urban energy calibration

Building Energy Modeling tools help forecast the energy performance of buildings. Urban energy models (UBEMs) emerged as important instruments to analyze the energy performance of buildings aggregated at different spatial resolutions, from the building level to the district level. They heavily rely on available data on geometries and measurements to create accurately calibrated energy models. However, limited research has been conducted to understand the impact of spatial and temporal resolution on the simulation results because of the difficulty of comparing results and not having a standardized procedure to report simulation errors. We review the literature on UBEM validation compared to measured energy data and show the discrepancies in the reporting accuracy. We articulate the need for consistent reporting on model accuracy and introduce a multi-dimensional Level of Detail (LoD) specification for UBEM, including geometry, thermal zoning, and spatio-temporal resolution of the measured data used to calibrate the models. Using a university campus with 70 buildings as an extensive case study, we demonstrate the performance of Bayesian calibration from the building level to the aggregated level. Our results suggest that the accuracy of urban energy prediction with annual temporal resolution can be significantly increased if calibration is performed by using building-level data. However, whenever privacy is a concern, then the data should be provided by aggregating them based on primary use type. Additionally, using monthly data to calibrate uncertain input parameters is not improving the accuracy of the models because the obtained posterior distributions for the selected parameters are not informative for monthly data. To improve this shortcoming, we suggest seasonal calibration, which is computationally costly.

Recent progress on BIM-based sustainable buildings: State of the art review

In recent decades, the planet has been negatively impacted by the construction industry due to the development of various infrastructures. Thus, it became one of the most polluting sectors that have required increasing the environmental awareness of public and private entities. Under these circumstances, there are urgent efforts required to mitigate these negative impacts. Different approaches have been made for sustainable buildings based on construction technologies, where sustainability must be included as the main premise from the initial stage of the project. Through a systematic literature review, different methodologies are analyzed such as Building Information Modeling (BIM), Life Cycle Assessment (LCA), and Building Energy Modeling (BEM) based on the existing regulations and standards of each country. The existing digital tools allow for the development of various low-carbon technologies, including sustainable materials and new integrations between them to find sustainable solutions and reduce carbon emissions from buildings. Several limitations exist as Building Information Modeling (BIM) software does not have all the green applications. Therefore, clear results are needed for effective indicators to help interested parties appropriate these technologies and invest time and money in projects based on Building Information Modeling (BIM) and learn a useful lesson for future projects from these technological tools.

Towards an automated image-based estimation of building age as input for Building Energy Modeling (BEM)

Today's methods for data acquisition usually involve a considerable amount of manual work and, depending on the level of detail, must find a compromise between accuracy and workload. As a result, various typologies of building stocks have been developed. These typologies provide engineers and architects with rough descriptions for classifying buildings into certain periods and construction types. However, real-world applications lack objective decision-making, as these classifications are usually conducted by manual investigations of buildings. To overcome this subjective approach and to ensure the reproducibility of data acquisition, this paper proposes an image-based classification using Deep Learning (DL) methods. We present the acquisition of ground truth and describe the training and testing routines on a dataset of 5,307 images obtained in the city of Weimar (located in the Free State of Thuringia, Germany), a mid-sized city representative of eastern Germany. All building images are linked to an existing building typology, allowing a classification of buildings in ten predefined and acknowledged periods. These periods are ranging from pre-1859 to post-2002. As a benchmark, an expert human baseline is acquired, showing a lower prediction accuracy than the results obtained by DL methods. Our results highlight the potential for automation in image analysis and beneficial impacts for Building Energy Modeling (BEM) on urban scale. Thus, a trustworthy data acquisition regarding the building age holds benefits for various stakeholders in BEM (engineers, architects, policymakers as well as customers).

BIM to BEM Transition for Optimizing Envelope Design Selection to Enhance Building Energy Efficiency and Cost-Effectiveness

This article focuses on developing an energy simulation model through Building Information Modeling (BIM) and Building Energy Modeling (BEM)to optimize energy in building design in Vietnam. Reducing the energy consumption in buildings will help reduce operating costs, impact the environment, and increase the efficiency of buildings. However, there is limited research on buildings with complex structures and configurations, detailed surface design, and envelope construction, especially in simulating details through BIM. The author proposes converting from BIM to BEM to simulate energy in buildings and optimize the factors related to building construction in the envelope design of the building. These factors include wall cladding materials, mirror materials, the window ratio on the walls, and other details. This study has effectively created an energy model for a public building, allowing for the calculation of the Energy Intensity Index (EUI) and annual energy costs for various scenarios. By altering factors associated with the design and construction process, the system has the potential to decrease both energy intensity and usage costs for the building. The study results will help designers and building managers improve and enhance energy efficiency in building projects.

A Community Building Energy Modelling – Life Cycle Cost Analysis framework to design and operate net zero energy communities

Despite building codes and standards helping build energy-efficient structures, most focus only on typical individual buildings. Second, they do not discuss community-based renewable energy adoption strategies as part of the standards. However, to facilitate the transition to net zero emissions in the upcoming decades, policies and recommendations from standards must enhance their scope from the buildings to communities and exploit the maximum energy retrofit opportunities available within the community premises. This study proposes a Community Building Energy Modelling – Life Cycle Cost Analysis (CBEM - LCCA) decision-making framework to analyze the techno-economical feasibility of the residential community-wide implementation of any building retrofit scheme. The framework developed is applied in a 14-building residential community in Mumbai, India, to compare the techno-economic benefit of implementing community-wide renewable installations against the existing national Energy Conservation Building Code (ECBC). The results reveal that all the communities together have the potential to generate 333 MWh of electricity per year from solar rooftops alone against the consumption of 369 MWh. The communities are observed to be capable of achieving nearly net-zero energy after adopting a community-wide rooftop solar photovoltaic installation with fewer than eight years of simple payback period (SPP). A thorough sensitivity analysis also reveals that the community rooftop solar strategy proves economically far superior to ECBC compliance of individual building envelopes. Among the retrofits, the roof-wall combination gave the minimum SPP of 8.38 years at a 100 % cooling space fraction. The findings provide evidence for why only a small number of states support ECBC code compliance, and it is recommended that policymakers use the proposed CBEM-LCCA framework to create appropriate energy policies for developing community-wide rather than individual building-level retrofit strategies.

Toward Improved Urban Building Energy Modeling Using a Place-Based Approach

Urban building energy models present a valuable tool for promoting energy efficiency in building design and control, as well as for managing urban energy systems. However, the current models often overlook the importance of site-specific characteristics, as well as the spatial attributes and variations within a specific area of a city. This methodological paper moves beyond state-of-the-art urban building energy modeling and urban-scale energy models by incorporating an improved place-based approach to address this research gap. This approach allows for a more in-depth understanding of the interactions behind spatial patterns and an increase in the number and quality of energy-related variables. The paper outlines a detailed description of the steps required to create urban energy models and presents sample application results for each model. The pre-modeling phase is highlighted as a critical step in which the geo-database used to create the models is collected, corrected, and integrated. We also discuss the use of spatial auto-correlation within the geo-database, which introduces new spatial-temporal relationships that describe the territorial clusters of complex urban environment systems. This study identifies and redefines three primary types of urban energy modeling, including process-driven, data-driven, and hybrid models, in the context of place-based approaches. The challenges associated with each type are highlighted, with emphasis on data requirements and availability concerns. The study concludes that a place-based approach is crucial to achieving energy self-sufficiency in districts or cities in urban-scale building energy-modeling studies.

Toward a Fast but Reliable Energy Performance Evaluation Method for Existing Residential Building Stock

Building a reliable energy model for old residential buildings with insufficient documentation and user assistance is a challenging and time-consuming task. Nevertheless, the ambitious European decarbonization targets require this building stock to be renovated, making energy assessment a key priority. In line with this goal, the following study explores a more simplified and automatic framework to generate a residential building energy model (BEM). The paper’s approach is based on the concept of urban building energy modelling (UBEM) archetypes or building prototypes and is customized according to the principles of dynamic simulations performed in the existing BEM software, Integrated Environmental Solutions Virtual Environment IES VE, and Solemma Open Studio. Therefore, based on three real starting inputs, a prototype database (DB) of assigned inputs is generated, i.e., an input matrix, using Google Maps as a geometry source. Other data are drawn from tabular DB. The proposed approach is evaluated by benchmarking the simulation results with precise models and monitoring the data that come from the Horizon2020 project REZBUILD. Nevertheless, a level of simplification is introduced that creates less accurate results for total or system-level energy consumption; this is compensated for using a set of simple calibration steps. The approach gives promising results for daily indoor temperature, making it a suitable indicator for evaluating further retrofitting alternatives.

Assessing the performance of the non-hydrostatic RegCM4 with the improved urban parameterization over southeastern China

This study evaluates the performance of the non-hydrostatic RegCM4 model (RegCM4-NH) incorporating the building energy model (BEM) in simulating urban climate over the Pearl River Delta (PRD) and Yangtze River Delta (YRD) regions. Specifically, it focuses on examining how the inclusion of BEM can ameliorate the severe warm bias in urban grids observed in high-resolution RegCM4 simulations by improving the surface energy balance. The prognostic calculation of interior building temperature and the consideration of ventilation and heat transfer between the building and the environment can collectively lead to a better estimate of anthropogenic heat flux (AHF). The effect of BEM does not appear to be uniform, showing spatial and temporal variations. While the magnitude of AHF reduction is roughly proportional to the urban density, the maximum reduction occurs at nighttime, which results in an asymmetric response to the minimum and maximum temperatures. Although the degree of lowering temperature is not sufficient to remove the systematic warm bias, adding physical realism to the model is helpful to make further improvements. The performance evaluation of RegCM4-NH with BEM targeted in vast urban agglomerations for the first time will be a valuable reference to facilitate the wider use of RegCM4-NH in the study of urban surface impacts.

Metal–Organic Frameworks (MOFs) Based Electrospun Nanofiber Membrane for Passive Indoor Moisture Control

Metal–organic frameworks (MOFs), an emerging class of porous sorbents, have become one of the most promising functional materials for indoor moisture regulation. However, the practical application of MOFs in building environments is challenging. Common shaping forms of MOFs (e.g., pellets, tablets, monoliths, granules, coating, etc.) may lead to agglomeration of MOF particles, higher usage costs, low efficiency, and material waste. Here, we report a new MOF-based electrospun nanofiber membrane with high porosity, light weight, and a large surface-area-to-volume ratio. MOF nanoparticles distributed on the fibers can fully contact moisture in the air, thus significantly enhancing MOFs’ utilization and performance. The results of the moisture adsorption test show that the improved MOF membrane has significantly higher water vapor uptake than most conventional hygroscopic materials and textiles in built environments. A building energy model was established to evaluate the MOF membrane’s effect on building energy consumption under different climates worldwide. The simulation results show that the MOF membrane can efficiently moderate indoor moisture fluctuation and has excellent energy-saving potential. The latent heat load reduction rate in summer can be up to 80–90% in arid/semi-arid climates and around 50% in temperate climates in a purely passive manner.