Building Energy Modeling Tools Research Articles

Understanding the Integration of Building Energy Modeling into the Building Design Process: Insights from Two Collaborative Construction Projects

This research investigates the integration of building energy modeling (BEM) within collaborative construction projects to inform design decisions for achieving high-energy performance goals. The study aims to understand current practices, benefits, and challenges associated with this integration. Using an ethnographic case study approach focused on two high-energy performance social housing projects with integrated project delivery and integrated design processes, data were collected through direct observations, document analysis, and interviews with project team members. Design process modeling was utilized to dissect current practices, followed by a hybrid inductive and deductive thematic analysis to find challenges related to energy performance design in collaborative projects. Findings from this research revealed that BEM experts often operate in isolation, with late integration of energy models into design decisions. Compliance-centric BEM usage and challenges related to interoperability of design and BEM tools further compound the issue of seamless collaboration. However, the study highlights that early collaboration among project stakeholders emerges as a pivotal factor in informed design decisions, bridging the gap between energy modeling and design. This research provides valuable insights for practitioners seeking to optimize BEM in their design process, and offers support to policymakers aiming to enhance the role of BEM in projects.

Going beyond Climate Neutral Cities: Evidence from a Positive Energy District in the Stuttgart Region

Abstract To successfully develops Positive Energy Districts (PEDs), it is essential to increase citizen awareness and encourage their engagement. Without acceptance and active participation from the citizens, they might not benefit from the technologies and inclusive business models that PEDs offer to foster energy efficiency, productivity, and flexibility. This contribution provides new evidence about the importance of prioritising citizens’ needs when developing PEDs, describing key findings from a case study recently conducted by the authors, in Nürtingen, in the Stuttgart Metropolitan Region, Germany. The research used urban building energy modeling tools, analyzed on-going projects of the city administration, and directly engaged with citizens by different research methods such as d go-along interviewing, SWOT analysis, and the promotion of best practices. The analysis and conclusions on working on the municipality project “Bahnstadt” were checked against the understanding of a “human-centred” PED. Five action areas were considered: energy efficiency, production and flexibility, carbon footprint, water management, and capital expenditures. This action research, part of the EU-funded project TELOS (2020-2024), aimed to provide recommendations to improve the quality of life of the community and enhancing citizen participation in planning and implementation processes, and towards the ambitious European climate neutrality goals.

BIM to BEM for Building Energy Analysis: A Review of Interoperability Strategies

The main objective of this review is to summarize and thoroughly investigate the most popular and promising BIM (building information modeling) and BEM (building energy modeling) interoperability strategies employed in the last years (2004–2023), highlighting pros and cons of each strategy and trying to understand the reason for the still limited BIM–BEM interaction. This review summarizes the main countries, areas, modeling tools, and interoperability strategies, with the advantages and disadvantages of each one. The methodology is based on the PRISMA protocol, and two databases were used for the research: Scopus and Google Scholar. A total of 532 publications were selected and 100 papers were deemed useful for the purposes of this review. The main findings led to the identification of four different interoperability strategies between BIM and BEM tools: (1) real-time connection; (2) standardized exchange formats and middleware corrective tools; (3) adherence to model view definitions; (4) proprietary tool-chain. These strategies were found to be characterized by different degrees of complexity, time required for information exchange, proprietary or opensource software, ability to reduce information loss, and detailed energy results. The results of this study showed that, to date, there is no better interoperability strategy, and that further efforts are needed so that interoperability between the two tools can become commonplace.

Transient Behavior Analysis of the Infiltration Heat Recovery of Exterior Building Walls

This research study investigated the transient behavior of the convection–diffusion model for the infiltration heat recovery (IHR) and the influence of the building envelope heat capacity, along with other factors. A transient numerical model was developed and validated to analyze the IHR under various conditions. The results highlight the role of heat capacity, thermal conductivity, wall thickness, airflow rate, airflow direction, and wall porosity on the temperature distribution and the heat recovery factor within the wall. Higher-heat-capacity walls displayed a delayed temperature rise, while low-thermal-conductivity walls reduced the conduction heat transfer and increased the IHR factor. The impact of heat capacity diminished with very low thermal conductivity walls but became evident for high-thermal-conductivity walls, particularly at higher Peclet numbers. Thicker walls enhanced the heat retention and improved the IHR, with a reduced influence of airflow rate. Higher IHR factors were associated with thicker walls, lower Peclet numbers, and higher heat capacities. The analysis also showed that the wall porosity affected the IHR with less significance than the other factors. Incorporating these findings into building energy modeling tools could improve the prediction accuracy of the thermal behavior of buildings. Accordingly, this study contributes to building physics by understanding IHR dynamics and thermal mass interactions, as well as improving building energy modeling accuracy for performance prediction. Future research can explore the impacts of additional factors on IHR and investigate the effect of IHR on the overall energy consumption of buildings.

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.

Empirical validation and comparison of PCM modeling algorithms commonly used in building energy and hygrothermal software

Whole building energy modeling has become extremely important for designers, architects, engineers, and researchers to predict energy performance of buildings. This is particularly important for phase change materials (PCMs) due to their variable properties. For this reason, building energy modeling tools have been developed and validated against different sources of experimental data. However, an IEA Annex 23 surveyed over 250 research publications concluding that the general confidence in currently used numerical models is still too low to use them for designing and code purposes. The objective of this study is to assess the capability of different simulation programs to model the PCMs in building envelope using data from two independent studies using Nano-encapsulated PCMs (Nano-PCM) and shape-stabilized PCMs. The study finds that the investigated PCM models accurately predict the PCM behavior in the building envelope.

Empirical validation of building energy modeling for multi-zones commercial buildings in cooling season

Recent nationwide efforts have provided reliable empirical data for ASHRAE standard 140, “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs,” to enable improved accuracy of building energy modeling (BEM) engines and improved characterization of their accuracy. Use of reliable empirical validation datasets in the evaluation of BEM tools will lead to more consistent and validated simulation engines across all software vendors. This will expedite the use of BEM in designing new buildings and retrofitting existing buildings, which delivers more energy-efficient buildings.In this study, a set of validation tests was performed in an occupancy-emulated small office building during a cooling season based on the test plan carefully designed per ASHRAE standard 140. Without making any calibration effort, major building simulation modules such as main heating, ventilation, and air conditioning (HVAC) system and infiltration model are validated with actual experimental data. Finally, an EnergyPlus simulation model was built based on as-built drawings, HVAC specifications, and measured data. Hourly simulation outputs were compared with the measured datasets from the tests to examine the goodness of fit. The generated experimental datasets and model input documentation of the test building will help industries and researchers to validate new BEM tools and improve their simulation engines. The validated simulation models can be leveraged as a rigorously validated benchmark commercial building.

Representing Low-Income Households in Building Energy Modeling Tools

Representing Low-Income Households in Building Energy Modeling Tools

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

To improve the energy prediction performance of a building energy model, the occupancy status information is very important. This is more important in real buildings, rather than under construction buildings, because actual building occupancy can significantly influence its energy consumption. In this study, a machine learning based framework for a consecutive occupancy estimation is proposed by utilizing internet of things data, such as indoor temperature and luminance, CO2 density, electricity consumption of lighting, HVAC (heating, ventilation, and air conditioning), electric appliances, etc. Three machine learning based occupancy estimation algorithms (decision tree, support vector machine, artificial neural networks) are selected and evaluated in terms of the performance of estimating the occupancy status for each season. The selection process of the input variables that have crucial impact on the algorithms’ performance are described in detail. Finally, an occupancy estimation framework that can repeat model training and estimation consecutively in a situation when time-series data are continuously provided over the entire measurement period is suggested. In addition, the performance of the framework is evaluated to identify how it improves the energy prediction performance of the building energy model compared to conventional energy modeling practices. The suggested framework is distinguished from similar previous studies in two ways: (1) The proposed framework reveals that input variables for the occupancy estimation model can be occasionally changed by an occupant response to certain times and seasons, and (2) the framework incorporates time-series indirect occupancy sensing data and classification algorithms to consecutively provide occupancy information for the energy modeling effort.

Validation of Building Energy Modeling Tools for a Residential Building in Brasov Area-Romania

Abstract A building energy model is a simulation tool which calculates the thermal loads and energy use in buildings. Building energy models provide valuable insight into energy use in buildings based on architecture, materials and thermal loads. In addition, building energy models also must account for the effects of the building’s occupants in terms of energy use. In this paper we discuss building energy models and their accuracy in predicting energy use. In particular, we focus on two types of validation methods which have been used to investigate the accuracy of building energy models and on how they account for their effects on occupants. The analyzed building is P + M located in the climatic zone 4, Sânpetru / Braşov. We have carried out a detailed and exemplary energy needs analysis using two methods of analysis.

Review of BIM's application in energy simulation: Tools, issues, and solutions

Abstract Building Information Modeling (BIM) initially emerged as a capability to transfer information and allow interaction or interoperability of various software tools used for architectural, structural, mechanical, electrical design, and building construction. Its application from developing 3D models, structural analysis, cost estimation, and mechanical analysis has now expanded to other applications such as energy simulation. Multiple computer-aided design (CAD) tools work as BIM authoring tools to generate BIM files in different formats containing various types of building information. In addition, there are various building energy modeling (BEM) tools capable of importing these BIM files to perform energy simulation. However, such tools have various capabilities and limitations and need to be investigated and categorized in order to facilitate choosing a proper tool for design professionals in different phases of project and purposes. In addition, interoperability and data exchange issues between BIM and BEM tools should be understood in order to find solutions such as developing proper corrective middleware tools to rectify them. This paper reviews the challenges, issues, and shortcomings in BIM-to-BEM interoperability process (BBIP) by proposing a detailed classification for these issues and studying the available solutions. The paper also explains how a corrective middleware, which is developed by the authors using Python, can be utilized to modify a gbXML file prior to adoption in energy simulation to resolve the issues related to building envelope in BBIP. To do so, initially a review is presented on research studies focused on different types of BIM schemas such as IFC and gbXML and energy simulation tools capable of reading these files such as Green Building Studio (GBS), DesignBuilder, Integrated Environmental Solutions-Virtual Environment (IES), and OpenStudio. In addition, some of the challenges in the application of BIM for energy simulation such as interoperability issues, lack of standards, and lack of easy solutions for extending existing BIM schemas and available corresponding solutions are also reviewed. With the focus on building envelope in mind, three case studies are discussed to observe the challenges and issues with respect to BBIP using Revit, GBS, and OpenStudio. Moreover, these case studies provide an opportunity to investigate the application of corrective middleware tools similar to what is developed in the study presented.

Experimental investigation and data-driven regression models for performance characterization of single and multiple passive chilled beam systems

The performance of passive chilled beams is characterized in this study to understand their physical behavior when applied in real indoor environments. Two full-scale experimental studies were conducted to map the performance of passive chilled beams in two different experimental settings: (i) single passive chilled beam testing in a controlled laboratory environment and (ii) multiple passive chilled beams testing in a real open plan office setting. The experimental results were then used to develop regression models for predicting the total cooling capacity and chilled surface temperature of single and multiple passive chilled beams, as a function of water flow rate, water supply temperature, air temperature above the chilled beam and area-weighted uncooled surface temperature in the space. The developed models showed good agreement with experimental results and can be used in building energy modeling tools for system simulation using passive chilled beams. Finally, it was found that the conventional method of predicting the total cooling capacity of a passive chilled beam from individual beam laboratory tests or manufacturers’ catalogs may significantly underestimate the system performance in multi-unit configurations. These differences could influence optimal system sizing and commissioning and should be considered in future studies.

Model calibration of a variable refrigerant flow system with a dedicated outdoor air system: A case study

With increased use of variable refrigerant flow (VRF) systems in the U.S. building sector, interests in capability and rationality of various building energy modeling tools to simulate VRF systems are rising. This paper presents the detailed procedures for model calibration of a VRF system with a dedicated outdoor air system (DOAS) by comparing to detailed measured data from an occupancy emulated small office building. The building energy model is first developed based on as-built drawings, and building and system characteristics available. The whole building energy modeling tool used for the study is U.S. DOE’s EnergyPlus version 8.1. The initial model is, then, calibrated with the hourly measured data from the target building and VRF-DOAS system. In a detailed calibration procedures of the VRF-DOAS, the original EnergyPlus source code is modified to enable the modeling of the specific VRF-DOAS installed in the building. After a proper calibration during cooling and heating seasons, the VRF-DOAS model can reasonably predict the performance of the actual VRF-DOAS system based on the criteria from ASHRAE Guideline 14-2014. The calibration results show that hourly CV-RMSE and NMBE would be 15.7% and 3.8%, respectively, which is deemed to be calibrated. The whole-building energy usage after calibration of the VRF-DOAS model is 1.9% (78.8kWh) lower than that of the measurements during comparison period.

Visualized strategy for predicting buildings energy consumption during early design stage using parametric analysis

Recently, there has been a high level of demand for sustainable design of buildings construction. The catalysts for such demand are rising energy costs and increasing environmental concerns. Therefore, it is important to evaluate building energy consumption, which is the key problem of building energy saving. There is a need for a reliable energy simulation model to predict buildings energy consumption. In addition, designers suffer from the limitations of energy simulation tools during the early design stage that addresses buildings envelope and orientation. This study proposes a strategy for visualized parametric energy analysis of buildings during the conceptual design phase. It presents an energy-oriented workflow that accommodates the Egyptian context. A residential building model is developed by coupling parametric analysis and building energy modeling tools as a means of developing an energy consumption database. The parallel coordinate plot is used to visualize the database to allow decision makers to flexibly evaluate energy performance. The advantages of this paper stem from: 1) providing a new strategy for visualizing the predicting building energy consumption data; 2) facilitating the process of decision making for energy design alternatives during early stages; 3) integrating parametric modeling and energy simulation engine in one platform to come over the problem of interoperability; 4) developing a generic model with variant energy simulation parameters; and 5) presenting an automated and simplified approach to enable modelers to simulate and analyze building energy performance with a more timely, accurate and efficient process.

Guidelines for Using Building Information Modeling for Energy Analysis of Buildings

Building energy modeling (BEM), a subset of building information modeling (BIM), integrates energy analysis into the design, construction, and operation and maintenance of buildings. As there are various existing BEM tools available, there is a need to evaluate the utility of these tools in various phases of the building lifecycle. The goal of this research was to develop guidelines for evaluation and selection of BEM tools to be used in particular building lifecycle phases. The objectives of this research were to: (1) Evaluate existing BEM tools; (2) Illustrate the application of the three BEM tools; (3) Re-evaluate the three BEM tools; and (4) Develop guidelines for evaluation, selection and application of BEM tools in the design, construction and operation/maintenance phases of buildings. Twelve BEM tools were initially evaluated using four criteria: interoperability, usability, available inputs, and available outputs. Each of the top three BEM tools selected based on this initial evaluation was used in a case study to simulate and evaluate energy usage, daylighting performance, and natural ventilation for two academic buildings (LEED-certified and non-LEED-certified). The results of the case study were used to re-evaluate the three BEM tools using the initial criteria with addition of the two new criteria (speed and accuracy), and to develop guidelines for evaluating and selecting BEM tools to analyze building energy performance. The major contribution of this research is the development of these guidelines that can help potential BEM users to identify the most appropriate BEM tool for application in particular building lifecycle phases.

Validation of building energy modeling tools under idealized and realistic conditions

Building energy models provide valuable insight into energy use in commercial and residential buildings based on architecture, materials and thermal loads. They are used in the design of new buildings and retrofitting to increase the efficiency of older buildings. The accuracy of these models is crucial to reducing energy use in the US and building a sustainable energy future. In addition to the architecture and thermal loads, building energy models also must account for the effects of the building's occupants on energy use. Traditionally simple schedule based methods have been used to account for the effects of occupants. However, newer research has shown that these methods often result in large differences between the modeled and actual energy use of buildings. In this paper we discuss building energy models and their accuracy in predicting energy use. In particular we focus on the different types of validation methods which have been used to investigate the accuracy of building energy models and how they account for (or do not) the effects of occupants. We also review newer work on stochastic methods for estimating the effects of occupants on energy use and discuss the improvements necessary to increase the accuracy of building energy models.