Building Energy Model Research Articles

A new workflow for load forecasting in planning areas: Considering uncertainties in building scenarios

Urban Building Energy Model (UBEM) is a widely used method for load forecasting in urban energy planning. It typically depends on reference buildings to simplify particular scenarios and uses energy calculation software for load forecasting. However, significant challenges exist when using this approach in unbuilt areas, because there is no established distribution of buildings or a widely recognized database of reference buildings. To overcome this technical barrier, this paper introduces a new workflow - Scenario Urban Building Energy Model (S-UBEM). This approach utilizes publicly available databases to establish a reference building database and a stochastic human behavior database. By utilizing these databases and planning documents, the methodology can construct building energy use scenarios for the undeveloped area, ultimately achieving load forecasting. In this paper, the proposed method is validated by transforming the built-up area into an unbuilt state. The results demonstrate that S-UBEM more accurately represents the spatial and temporal distribution of energy consumption. This is evidenced by a 28.3 % reduction in the annual energy prediction error compared to the conventional method, as well as by visualizing the distribution of energy consumption at the building level.

Retraining surrogate models in increasingly restricted design spaces: a novel building energy model calibration method

Surrogate (i.e. meta) models can approximate building energy models (BEMs) accurately and quickly, hence they have been widely used in BEM calibration studies. Typically, the surrogate models are trained a single time over the entire unknown building parameter space with a design such as Latin hypercube sampling. In this article, a multiple polynomial regression surrogate model is, instead, retrained with increasingly restricted designs. In each training repetition, the bounds of the design narrow around the unknown building parameter values that minimize the error between the surrogate model’s predictions and the measured energy. This ‘cascading surrogate’ calibration method finds CVRMSE values that are much lower than those of a powerful black box optimizer in a case study with simulated ‘measured’ data. However, the method has similar performance to the black box optimizer in a case study with real hourly measured energy, probably since the BEM was not configured accurately enough.

Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development

Urban building energy models (UBEMs), developed to understand the energy performance of building stocks of a region, can aid in key decisions related to energy policy and climate change solutions. However, creating a city-scale UBEM is challenging due to the requirements of diverse geometric and non-geometric datasets. Thus, we aimed to further elucidate the process of creating a UBEM with disparate and scarce data based on a bottom-up, physics-based approach. We focused on three typically overlooked but functionally important commercial building stocks, which are sales and shopping, healthcare facilities, and food sales and services, in the region of Pittsburgh, Pennsylvania. We harvested relevant local building information and employed photogrammetry and image processing. We created archetypes for key building types, designed 3D buildings with SketchUp, and performed an energy analysis using EnergyPlus. The average annual simulated energy use intensities (EUIs) were 528 kWh/m2, 822 kWh/m2, and 2894 kWh/m2 for sales and shopping, healthcare facilities, and food sales and services, respectively. In addition to variations found in the simulated energy use pattern among the stocks, considerable variations were observed within buildings of the same stock. About 9% and 11% errors were observed for sales and shopping and healthcare facilities when validating the simulated results with the actual data. The suggested energy conservation measures could reduce the annual EUI by 10–26% depending on the building use type. The UBEM results can assist in finding energy-efficient retrofit solutions with respect to the energy and carbon reduction goal for commercial building stocks at the city scale. The limitations highlighted may be considered for higher accuracy, and the UBEM has a high potential to integrate with urban climate and energy models, circular economy, and life cycle assessment for sustainable urban planning.

Occupant counting model development for urban building energy modeling using commercial off-the-shelf Wi-Fi sensing technology

Urban Building Energy Models (UBEMs) are vital for estimating building energy use and related greenhouse gas emissions. However, their reliability needs boosting by using more real-world data, especially regarding occupancy behavior. Presently, UBEMs often use standard occupancy patterns, which may not reflect the real building use, especially for commercial and institutional buildings.Wi-Fi sensing is a reliable approach that can improve UBEMs due to its wide availability and cost-effectiveness. In this study, we leverage detailed signal data derived from Wi-Fi sensing technology to create a realistic, scalable and cost-effective occupancy model. A framework has been developed to derive the number of people in a building, which will influence energy usage patterns and enhance UBEMs.The developed model employs various machine learning techniques and achieves a test accuracy of 77 %. Limited availability and diversity of the initial dataset necessitated the use of data augmentation techniques, enabling the model to learn varied representations and thus achieve better test performance of 91 % post-augmentation.To evaluate the effectiveness of the developed model, it has been applied to two institutional buildings of a specific inner-city district in Montreal, Canada, to compute their heating and cooling demands. The outcomes are then compared with those obtained using standard schedules, revealing a considerable discrepancy in annual peak and total annual cooling demand of about %5 and 20 % respectively.

Developing urban building energy models for shanghai city with multi-source open data

Urban building energy modeling is crucial for guiding carbon reduction policies, but acquiring reliable data at the urban scale remains challenging. This study develops a model for Shanghai City, China, by integrating multi-source open data. Eight data sources were collected, including maps, satellite imagery, and GIS data, covering 609,763 building footprints and 539,119 buildings (1.57 billion m2). Spatial analysis, supervised learning, and unsupervised machine learning methods were used to categorize buildings into 63 prototypes, and classification accuracy reached 95 %. Historical satellite data and community boundaries determined the year built for over 95 % of buildings. Prototypes were modeled in AutoBPS using local energy saving standards and simulated in EnergyPlus to derive energy use intensities aligning with government ranges. This work demonstrates a practical data fusion approach to develop large-scale, reliable urban building energy models. Integrating heterogeneous open data sources expands the coverage of open data and improves accuracy. The framework and insights provide a valuable foundation to leverage open data for advancing city-scale energy modeling and sustainability planning.

A simplified dynamic tool for building heating and cooling energy requirements estimation on a daily time scale

A persistent dilemma in building energy modeling consists of finding the proper trade-off between accurate results, data availability and limited modelling effort. In fact, quasi-steady state models require a limited number of inputs, however providing often rough results; dynamic tools are instead usually very accurate but involve a huge effort to obtain all the needed information and implementing it into the tool. In this context, we propose a simplified dynamic model for building heating and cooling demand estimation on a daily basis, according to the assessment of all the involved heat transfer mechanisms of the thermal zone. The required inputs are limited and comparable to those needed for other quasi-steady state models, but three tuning coefficients allow the simulation to be performed at the daily time scale. The proposed tool is here successfully tested on a wide set of test cases, differing for building structure, external climate, and occupants’ profile. In addition, reliable results are provided also in case of multi-year application and availability of a little amount of data for calibration, with average errors typically below 10% in daily cooling and heating energy requirement estimation, compared to benchmark demands. Finally, an example of application explores the application field of the tool.

A Comparison of Programmed Controlled Existing System vs. Electric Resistive Heating for a University Building in Newfoundland

Buildings consume in excess of 30% of the total energy worldwide. In the Canadian context, commercial and institutional buildings contribute to around 14% of the overall energy usage, and space heating emerges as the predominant end-use category, constituting approximately 57% of this consumption. This underscores a considerable potential for energy savings in the realm of building energy consumption. This paper compares the energy consumption for space heating at the Core Science Facility (CSF) of the Memorial University of Newfoundland (MUN), Canada. The analysis contrasts the current system, utilizing hot water from fuel oil-fired boilers, with a proposed system suggesting the replacement of the oil-fired boiler with an electric resistive boiler, by employing a building energy model (BEM) created with the OpenStudio application. The findings indicate that beyond the anticipated enhancements in energy efficiency, a supplementary energy saving of approximately 7% is attainable through the proposed transition. Comparing the simulation outcomes with actual data reveals that the projected consumption from the Building Energy Model (BEM) is lower than the actual figures. This difference is attributed to the model’s development, which involved distinct considerations and assumptions compared to the actual conditions such as construction materials, building occupancy, infiltration and exfiltration, interconnected buildings, energy usage by equipment and lighting, HVAC system energy consumption, and transmission losses through piping which can significantly influence the building’s energy consumption.

A Comparative Study on Discrepancies in Residential Building Energy Performance Certification in a Mediterranean Context

Energy Performance Certification (EPC) systems are pivotal in addressing the global energy challenge, particularly in the building sector. This study evaluates the efficacy of the EPC offered by the Simplified Building Energy Model interface designed to indicate compliance with the Cypriot building regulations, widely known as iSBEM-Cy Version 3.4a, by examining a typical residential unit in Cyprus. Data on construction features and electromechanical systems were collected, and actual monthly electricity and oil bills were analyzed to determine the total primary energy consumption. Various factors were considered, including energy efficiency and operational parameters for heating, cooling, lighting, auxiliary systems, and domestic hot water. The building energy performance was simulated using iSBEM-Cy, allowing for comparison with real-world energy consumption. Notable discrepancies were observed, particularly in cooling, with deviations reaching 377.4%. Conversely, domestic hot water consumption exhibited minimal variance at 7%, while heating and lighting showed moderate discrepancies (24.3% and −113.9%, respectively). This study underscores the need for rigorous evaluations to shape effective EPC and provides insights into building energy performance in Mediterranean Cyprus. This research contributes to the broader discourse on sustainable construction practices by aligning simulation results with real-world energy consumption.

Mapping the CityGML Energy ADE to CityGML 3.0 Using a Model-Driven Approach

With the increasing adoption of semantic 3D city models, the relevance of applications in the field of Urban Building Energy Modelling (UBEM) has rapidly grown, as the building sector accounts for a large part of the total energy consumption. UBEM allows us to better understand the energy performance of the building stock and can contribute to defining refurbishment strategies. However, UBEM applications require lots of heterogeneous data, eventually advocating for standards for data interoperability. The Energy Application Domain Extension has been created to cope with UBEM data requirements and offers a standardised data model that builds upon the CityGML standard. The Energy ADE 1.0, released in 2018, creates new classes and adds new properties to existing classes of the CityGML 2.0 Core and Building modules. CityGML 3.0, released in 2021, has introduced several changes to the data model and its ADE mechanism. These changes render the Energy ADE incompatible with CityGML 3.0. This article presents how the Energy ADE has been ported to CityGML 3.0 to allow, on the one hand, for a lossless data conversion and, on the other hand, to exploit the new characteristics of CityGML 3.0 while keeping a logical symmetry between the ADE classes of both CityGML versions. The article describes the chosen methodology, the mapping strategies, the implementation steps, as well as the data conversion tests to check the validity of the “new” Energy ADE for CityGML 3.0.

Impact of time resolution on estimation of energy savings using a copula-based calibration in UBEM

Urban Building Energy modelling (UBEM) has emerged in the last decade as an important tool to accelerate energy transition in the building sector. To fulfil one of its major purposes - forecasting energy savings for potential energy conservation measures at an urban scale, several challenges are still to be addressed. Two key challenges include: 1) the need to calibrate models with a large number of unknown parameters across numerous buildings (either individually or through archetype representation); 2) the limited availability of high-resolution measured data, which raises concerns about calibrated models based solely on yearly values for accurate energy savings forecast. This study addresses these challenges using a case study of 35 buildings in a district in Stockholm, Sweden. Firstly, a new iterative Approximate Bayesian Computation (ABC) method for calibration is proposed, incorporating a copula-based sampling process at each iteration. Secondly, the new calibration process is applied with three different time resolutions to examine the impact of data resolution on forecasted energy usage, particularly when applied to a classical energy conservation measure. Results demonstrate the efficiency of the new method across the 35 buildings, facilitating the rapid population of a final joint distribution for the nine unknown parameters considered in each building. While the marginals may be strongly influenced by the time resolution, the forecasted energy consumption remains identical across the three analysed time resolutions. However, a noticeable difference is observed when the ECM pertains to a formerly unknown or known parameter.

Enhancing Urban Climate‐Energy Modeling in the Community Earth System Model (CESM) Through Explicit Representation of Urban Air‐Conditioning Adoption

AbstractImproved representation of urban processes in Earth System Models (ESMs) is a pressing need for climate modeling and climate‐driven urban energy studies. Despite recent improvements to its fully coupled Building Energy Model (BEM), the current Community Land Model Urban (CLMU) in the Community Earth System Model (CESM) lacks the infrastructure to model air‐conditioning (AC) adoption explicitly. This undermines CESM's fidelity in modeling urban climate and energy use, and limits its use in climate and energy risk assessments. Here, we establish a new parameterization scheme in CESM that represents AC adoption explicitly through an AC adoption rate parameter in the BEM of CLMU, and build a present‐day, global, survey‐based, and spatially explicit AC adoption rate data set at country and sub‐country level that is integrated within CESM. The new data set can be leveraged for other ESMs or global‐scale models and analyses. The explicit AC adoption scheme and the AC adoption rate data set significantly improve the accuracy of anthropogenic heat modeling due to AC in CESM. The new parameterization scheme makes it possible to evaluate the effects of changing AC adoption on global urban energy and climate using CESM. These developments enhance CESM in its use for climate impact assessments under future climate and socioeconomic development scenarios, and represent continued efforts in better representing urban processes and coupled human‐urban‐Earth dynamics in ESMs.

An ontology-driven method for urban building energy modeling

The field of urban building energy modeling, embracing diverse aspects such as geography, construction, and materials. There is a lack of a comprehensive information integration framework to streamline cross-domain data in a systematic manner and generate simulation files that are easily calculable. This study addresses this gap by proposing two key ontologies: Building Template Ontology for managing building energy simulation templates and Urban Building Ontology for organizing building physics information. Utilizing this ontology-driven method, this study enhances flexibility in information fusion, exploits logical relationships between simulation inputs, and provides a lightweight solution for structured urban energy performance analysis. Energy simulations were conducted on over 5,000 buildings across three cities, and the impact of energy retrofit measures was further quantified, revealing potential savings of 0.1 % to 4.0 %, 1.2 % to 10.2 %, and 2.5 % to 6.9 % for building envelope, lighting, and air conditioning improvements, respectively. This study empowers urban stakeholders, designers, and managers to streamline the model construction and provides valuable insights into energy consumption patterns. Acknowledging limitations in current ontologies, including restricted building type templates, lack of real-time instance information, and absence of direct energy verification, this study underscores the imperative to address these for future advancements.

Sensitivities of energy use reduction in subtropical high-rise office buildings: A Hong Kong case

Office buildings are a major contributor to energy consumption in high-density urban contexts where building high-rises is a norm. It is important to understand the parameters affecting high-rise office buildings’ energy consumption. However, previous research generally used isolated buildings as target buildings, regardless of the impact brought by the surrounding buildings. Also, there lacked a comprehensive calibration method to validate the energy baseline model. This paper aims to quantitatively analyse the energy use in high-rise office buildings by tackling these two issues and examining the impacts of various energy-saving measures (ESMs). A real-life 26-story office building in Hong Kong was selected as a base case. Multiple methods were used to calibrate the building energy model, including onsite microclimate measurement, metered data, architectural drawings and official documents, and onsite surveys. Parametric sensitivity analyses were then conducted to quantify the importance of ten selected parameters from five types of ESMs. Results indicate that fan efficiency and cooling setpoint temperature had the most significant energy-saving potential. The base case of the ESMs in use was considered as the base scenario. Two new scenarios were proposed to examine the range of total energy consumption by adopting the most and least energy-efficient ESMs, which were referred to as the “optimistic scenario” and the “pessimistic scenario”. The maximum reduction/increase in energy use is expected to be approximately 13.2%/39.3% for the optimistic/pessimistic scenario compared to the base scenario. The findings on the sensitivities and scenarios contribute to a more comprehensive understanding of the critical parameters that influence energy use in high-rise office buildings in subtropical climate. The findings also provide scenario-based design solutions for delivering low-energy office buildings.

Validating ‘GIS-UBEM’—A Residential Open Data-Driven Urban Building Energy Model

The study of energy consumption in buildings, particularly residential ones, brings with it significant socio-economic and environmental implications, as it accounts for approximately 40% of CO2 emissions, 18% in the case of residential buildings, in Europe. On a number of levels, energy consumption serves as a key parameter in urban sustainability indicators and energy plans. Access to data on energy consumption is crucial for energy planning, management, knowledge generation, and awareness. Urban Building Energy Models (UBEMs), which are emerging tools for simulating energy consumption at neighborhood scale, allow for more efficient intervention and energy rehabilitation planning. However, UBEM validation requires reliable reference data, which are often challenging to obtain at urban scale due to privacy concerns and data accessibility issues. Recent advances, such as automation and open data utilization, are proving promising in addressing these challenges. This study aims to provide a standardized UBEM validation process by presenting a case study that was carried out utilizing open data to develop bottom-up engineering models of residential energy demand at urban scale, with a resolution level of individual buildings, and a subsequent adjustment and validation using reference tools. This study confirms that the validated GIS-UBEM model heating and cooling demands and consumption fall within the confidence bands of ±15% and ±12.5%, i.e., the confidence bands required for the approval of official alternative simulation methods for energy certification. This paves the way for its application in urban-scale studies and practices with a well-established margin of confidence, covering a wide range of building typologies, construction models, and climates comparable to those considered in the validation process. The primary application of this model is to determine the starting point and subsequent evaluation of improvement scenarios at a district scale, examining issues such as massive energy rehabilitation interventions, energy planning, demand analysis, vulnerability studies, etc.

Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight

Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, are typically required to reflect the non-linear behavior of these anomalies. As an ongoing research question related to Deep Learning, a model's applicability to limited data settings can be explored by introducing prior knowledge in the network. This same strategy can also lead to more interpretable predictions, hence facilitating the field application of the approach. For that purpose, the aim of this paper is to propose the use of Physics-informed Denoising Autoencoders (PI-DAE) for missing data imputation in commercial buildings. In particular, the presented method enforces physics-inspired soft constraints to the loss function of a Denoising Autoencoder (DAE). In order to quantify the benefits of the physical component, an ablation study between different DAE configurations is conducted. First, three univariate DAEs are optimized separately on indoor air temperature, heating, and cooling data. Then, two multivariate DAEs are derived from the previous configurations. Eventually, a building thermal balance equation is coupled to the last multivariate configuration to obtain PI-DAE. Additionally, two commonly used benchmarks are employed to support the findings. It is shown how introducing physical knowledge in a multivariate Denoising Autoencoder can enhance the inherent model interpretability through the optimized physics-based coefficients. While no significant improvement is observed in terms of reconstruction error with the proposed PI-DAE, its enhanced robustness to varying rates of missing data and the valuable insights derived from the physics-based coefficients create opportunities for wider applications within building systems and the built environment.

The role of above-code labeling programs in reducing CO2e emissions in residential buildings

Residential buildings account for 15 % of total U.S. carbon dioxide (CO2) emissions. Voluntary, above-code labeling programs, such as the ENERGY STAR® and Zero Energy Ready Home™ programs, can play a crucial role in reducing the carbon footprint of the new construction residential building sector by encouraging adoption of more energy-efficient and lower emissions building practices and technologies. It’s important for the industry to understand the carbon dioxide equivalent (CO2e) and energy cost savings potential of these programs. To estimate the potential CO2e emissions savings for homes built to above-code labeling program levels, it is imperative to have reliable and accurate tools for estimating emissions from buildings. The open-source OpenStudio-ERI workflow employs detailed physics-based building energy modeling (BEM) to estimate energy usage and operational CO2e emissions. In this study we use the OpenStudio-ERI workflow and regional, time-varying emissions factors from the Cambium database to compare the CO2e emissions and energy costs of homes built to performance levels required by these above-code labeling programs relative to homes built to model energy codes. We present the results of our analysis and provide a range of emissions and annual energy cost savings for the above-code labeling programs.

A New Model for Building Energy Modeling and Management Using Predictive Analytics: Partitioned Hierarchical Multitask Regression (PHMR)

Buildings are major consumers of energy, accounting for a significant proportion of total energy use worldwide. This substantial energy consumption not only leads to increased operational costs but also contributes to environmental concerns such as greenhouse gas emissions. In the United States, building energy consumption accounts for about 40% of total energy use, highlighting the importance of efficient energy management. Therefore, accurate prediction of energy usage in buildings is crucial. However, accurate prediction of building energy consumption remains a challenge due to the intricate interaction of indoor and outdoor variables. This study introduces the Partitioned Hierarchical Multitask Regression (PHMR), an innovative model integrating recursive partition regression (RPR) with multitask learning (hierML). PHMR adeptly predicts building energy consumption by integrating both indoor factors, such as building design and operational variables, and outdoor environmental influences. Rigorous simulation studies illustrate PHMR’s efficacy. It outperforms traditional single-predictor regression models, achieving a 32.88% to 41.80% higher prediction accuracy, especially in scenarios with limited training data. This highlights PHMR’s robustness and adaptability. The practical application of PHMR in managing a modular house’s Heating, Ventilation, and Air Conditioning (HVAC) system in Spain resulted in a 37% improvement in prediction accuracy. This significant efficiency gain is evidenced by a high Pearson correlation coefficient (0.8) between PHMR’s predictions and actual energy consumption. PHMR not only offers precise predictions for energy consumption but also facilitates operational cost reductions, thereby enhancing sustainability in building energy management. Its application in a real-world setting demonstrates the model’s potential as a valuable tool for architects, engineers, and facility managers in designing and maintaining energy-efficient buildings. The model’s integration of comprehensive data analysis with domain-specific knowledge positions it as a crucial asset in advancing sustainable energy practices in the building sector.

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities: Current trends and future outlook

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities: Current trends and future outlook

Influence of building thermal envelope modeling parameters on results of building energy simulation

The paper investigates the influence of input values for building energy model parameters on simulation results. The methodology is based on dynamic simulations in TRNSYS software, including models for three different buildings, with variations in different parameters such as referent dimensions, infiltration, envelope thermophysical properties, and thermal capacitance of interior space. The method is applied to a case study of theoretical buildings located in Slovenia. Obtained results suggest that the accuracy of model parameters’ inputs related to thermal properties of glazing have the most significant impact on simulation results, where reduction in g-value from 0.62 to 0.22 reduces simulated qH,nd and qC,nd for 25% and 95% respectively. Referent dimensions for modeling floor area are the least influential parameter, where for Building III (BSF = 0.36) the change of referent dimensions results in variations of simulated qH,nd and qC,nd for less than ±1%, while for Building I (BSF = 0.62) qH,nd and qC,nd variations are up to ±20%. In general, for all model parameters, with a reduction of BSF, the influence of input variations is also reduced, meaning energy models for large buildings are more robust and less biased to deviation in simulation results from the inaccuracy of model inputs.

Quantifying energy poverty vulnerability with minimal data – A building energy simulation approach

Energy poverty is a pressing issue that many countries currently face. The income per household and energy use data per household are variables used in widely known methodologies on the topic of energy poverty. However, due to different reasons, this data isn’t made available in many locations of interest, which prevents them from quantifying energy poverty. To overcome this problem, this study introduces a not yet explored asset-based workflow that makes use of income distributions and building energy modelling to quantify a household’s energy poverty vulnerability. This study defines a novel energy poverty vulnerability index (EVI) that makes use of the widely known ten-percent rule’s fundamental concepts but avoids the latter’s main critics through two major improvements; a varying threshold is introduced with a linear function and distortion is prevented using a weighted average. The methodology has been applied to more than 85,000 low-rise apartment buildings in Montreal, Canada to quantify their energy poverty vulnerability, a feat that wasn’t possible using typical methodologies. The case study results have shown that Montreal’s buildings’ energy poverty vulnerability is highly dependent on the household’s income, but also on the insulation level and floor area. A sensitivity analysis has been conducted to determine the EVI’s requirements and limitations. From this analysis, the EVI is most sensitive to variables related to the building’s energy consumption and is least sensitive to income.