Building Energy Model Research Articles

Analysis of the energy performance strategies in a historical building used as a music school

Energy consumption in public education buildings depends on use and occupancy. To improve energy performance, energy audits are essential to identify specific solutions for each building. In this study, we conducted an energy audit of a historical building used as a public education centre. We collected gas and electricity bills and recorded indoor temperatures for over a year to determine heating and cooling set points and the schedules. Our analysis showed that 47.42% of the electricity bills were unused. To reduce energy demand and improve thermal comfort, we both developed and validated a Building Energy Modelling (BEM) approach using TRNSYS18 and weather data during year 2021. The BEM model allowed us to propose efficient measures to meet the Standard Passive requirements. Our results demonstrate the effectiveness of energy audits and BEM modelling in reducing energy consumption in public education buildings.

Energy retrofit alternatives for three apartment buildings in Kosovo

AbstractThe need to decrease energy use represents a challenge particularly in developing countries, including Kosovo, considering the unsustainable, insufficient, and inconsistent energy supply. This contribution presents a simplified yet comprehensive approach to evaluate the energy performance improvement potential of three selected multi-family residential buildings using building energy models generated from calibrated and simplified input parameters. To this end, alternative retrofit measures were tested via a simulation-based parametric study. The energy benefit of each retrofit alternative was estimated and analyzed. Results are expected to provide valuable information towards outlining design guidelines for energy-efficient residential buildings in Kosovo.

Urban building energy modelling-based framework to analyze the effectiveness of the community-wide implementation of national energy conservation codes

PurposeThe building sector contributes one-third of the energy-related carbon dioxide globally. Therefore, framing appropriate energy-related policies for the next decades becomes essential in this scenario to realize the global net-zero goals. The purpose of the proposed study is to evaluate the impact of the widespread adoption of such guidelines in a building community in the context of mixed-mode buildings.Design/methodology/approachThis study decentralizes the theme of improving the energy efficiency of the national building stock in parcels by proposing a community-based hybrid bottom-up modelling approach using urban building energy modelling (UBEM) techniques to analyze the effectiveness of the community-wide implementation of energy conservation guidelines.FindingsIn this study, the UBEM is developed and validated for the 14-building residential community in Mumbai, India, adopting the framework. Employing Energy Conservation Building Code (ECBC) compliance on the UBEM shows an energy use reduction potential of up to 15%. The results also reveal that ECBC compliance is more advantageous considering the effects of climate change.Originality/valueIn developing countries where the availability of existing building stock information is minimal, the proposed study formulates a holistic framework for developing a detailed UBEM for the residential building stock from scratch. A unique method of assessing the actual cooling load of the developed UBEM is presented. A thorough sensitivity analysis approach to investigate the effect of cooling space fraction on the energy consumption of the building stock is presented, which would assist in choosing the appropriate retrofit strategies. The proposed study's outcomes can significantly transform the formulation and validation of appropriate energy policies.

A bottom-up approach for community load prediction based on multi-agent model

Accurate prediction of community load is a prerequisite for community-scale demand matching and energy management. However, community-scale load prediction methods are challenging in characterizing complex energy-use scenarios caused by different building types and complicated occupant behaviors. The occupant behavior is the primary source of discrepancy between the predicted and realistic building performance, and this discrepancy will further widen on the community scale. This paper established the community occupant agent model (COAM) to generate occupancy data. The model delineates the agent system boundary from the perspective of complex adaptive systems (CAS). Anylogic was used to randomize and represent the decision behavior of community occupants. The bottom-up community building energy modeling (CBEM) workflow was applied to the case community of Xi'an (China). The measured and simulated values of the community building occupancy were compared, indicating the model's ability to reflect the interactions between single occupants and community building. The case community heating loads under different heating patterns were analyzed, and the workflow performance was evaluated. The proposed method might guide the district energy system to formulate the management strategy in the energy planning stage.

Demystifying energy savings from dynamic temperature setpoints under weather and occupancy variability

Building temperature setpoints affect both HVAC energy consumption and occupant comfort. To reduce HVAC energy usage, researchers often investigate how system operations can be optimized under weather and occupancy variability subject to a fixed setpoint that minimizes any possible discomfort. While previous research has explored the selection of dynamic setpoints to minimize HVAC energy consumption based on outdoor temperature, they have often neglected the impact of varying occupancy rates on the setpoints. This paper aims to demystify energy savings derived from fixed and dynamic temperature setpoints under weather and occupancy variability and explores the additional energy savings that can be achieved through dynamic temperature setpoints. An exhaustive HVAC zone temperature setpoint optimizer was developed to determine dynamic setpoints with respect to weather and occupancy (i.e., setpoints that minimize HVAC energy consumption at different occupancy rates based on outdoor weather). U.S. DOE reference building energy models for small, medium, and large office buildings were simulated at 17 climate zones, 4 occupancy rates (25%, 50%, 75%, 100%) and 7 setpoints (19.5°C to 25.5°C at 1°C interval). It was found that, both fixed and dynamic setpoints benefit from the energy reduction of approximately 2-4% from the lower heat generated by the occupants at lower occupancy rates. However, at outdoor temperatures between 5°C and 32°C where occupant heat loads can swing the building between heating, free-running, and cooling modes, dynamic setpoints yield additional 2-10% energy savings, compared to fixed setpoints.

Coupling urban climate modeling and city-scale building energy simulations with the statistical analysis: Climate and energy implications of high albedo materials in Seoul

Coupling urban climate modeling and city-scale building energy simulations with the statistical analysis: Climate and energy implications of high albedo materials in Seoul

Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases

In 2020, domestic buildings represented 27% of the energy consumption in the European Union. The EC Renovation Wave concluded that the annual renovation rate should be doubled, from 1% to 2%, over the next decade in order to meet the 2030 energy efficiency targets. Funding programs are being implemented at national and European levels to accelerate this process. These aim to prioritize large-scale interventions and the most efficient energy conservation measures by including strict performance targets as eligibility conditions. However, these programs are coordinated by local authorities, and they struggle to generate reliable energy estimates for large areas, not least predict potential savings at both aggregated and individual levels. This paper presents a bespoke energy retrofit analysis methodology based on urban building energy modelling, which is defined and calibrated with the Energy Performance Certificate Database. We apply this method to the case study Barrio das Flores. The model adopts a hybrid approach, defining and simulating representative typologies to extrapolate the results to the whole district according to each dwelling’s specific parameters. This methodology will be used to predict the potential energy savings in the study area and support the grant application for the funding renovation program.

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.