Building Energy Model Research Articles

An Integrated Urban Building Energy Benchmarking Workflow to Support Urban Energy Evaluation: A Case Study of Sheffield UK

Understanding energy demand and supply flow at a large urban scale is an essential step for urban designers, planners and policymakers in investigating how buildings within an existing urban context could be designed as a whole to support the future sustainable built environment. The contemporary approach is to model energy use activities at various building and urban scales. This, albeit a practical approach, poses significant challenges in acquiring good quality data concerning buildings and their interactions at an urban scale at an affordable price. This paper presents a streamlined benchmarking methodology with a parametric modelling workflow to complement the mainstream urban building energy modelling (UBEM) approach. The proposed building energy benchmarking workflow integrates multiple databases concerning building energy consumption, energy generation and underlying grid infrastructure. Parametric modelling serves as a tool for integrating databases through the underlying sortable geometric characteristics. This is envisaged to afford stockholders, such as policymakers or urban planners, greater flexibility to investigate energy demand and supply scenarios at an urban neighbourhood scale and further explore potential applications. Using the proposed workflow, we look at renewable solar energy to experiment with offsetting urban building energy consumption through reconfiguring existing electricity microgrids in the Sheffield city centre. The result of this study demonstrates how the presented urban building energy benchmarking (UBEB) workflow would afford capabilities and flexibility to support stakeholders, e.g., urban planners, policymakers, and end-users, to better understand existing barriers and explore actionable opportunities via re-configurable electricity microgrids.

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.

Reducing uncertainty of building shape information in urban building energy modeling using Bayesian calibration

This study proposes urban building energy modeling that extends beyond single-building-level models to the urban level. However, most urban building energy models use representative buildings that may not accurately reflect the diversity of building shapes, systems, and envelope performance when conducting building energy evaluations at the urban scale. To address this issue, previous studies have utilized representative buildings and Bayesian calibration to estimate uncertain building information parameters without considering building shape information. Therefore, the primary objective of this study is to estimate building shape information using artificial neural networks and Bayesian calibration based on building energy consumption data to identify the shape information uncertainty of representative buildings. The results indicate that some shape information can be estimated by comparing the overall distribution of the building stock using the two-sample Kolmogorov–Smirnov test. Furthermore, we found that the proposed energy modeling methodology yields energy consumption patterns similar to those of the target building stock. This preliminary investigation addresses the uncertainty of representative buildings in urban-scale modeling, elucidates the relationship between building form and energy consumption, and introduces a method for inferring shape information from energy consumption data.

UBEM calibration method by energy consumption disaggregation using a change-point model

Urban Building Energy Modeling (UBEM) is valuable for analyzing urban energy demand, but uncertainties can create gaps between predictions and actual usage. Calibration often uses utility bill data due to its urban-scale accessibility. However, comparing this data with simulation results for specific energy uses like cooling and heating is challenging. This study introduces a UBEM calibration method using a change-point model to disaggregate energy into heating, cooling, and base load. Applied to 222 office buildings in Seoul, this method provided more appropriate calibration results by considering individual energy consumption characteristics rather than just total consumption. The CVRMSE for base load, heating, and cooling energy decreased compared to uncalibrated simulations. This research highlights the importance of energy disaggregation when calibrating UBEM with utility bill data, offering a more nuanced approach to urban energy modeling.

Comparative study of development scenarios to decipher carbon emissions of new/old campuses in China with urban building energy model: A case study of Southeast University

Comparative study of development scenarios to decipher carbon emissions of new/old campuses in China with urban building energy model: A case study of Southeast University

Modeling building energy self-sufficiency of using rooftop photovoltaics on an urban scale

The significant contribution of buildings to global energy-related CO2 emissions and climate change has led to projections of a carbon–neutral building stock by 2050. This study evaluates the potential contribution of rooftop photovoltaics to urban energy self-sufficiency by developing an enhanced CityBEM framework, our in-house urban building energy model (UBEM). This methodology enables city-scale, simultaneous simulations of building energy use and rooftop photovoltaic retrofitting with low computational time. CityBEM’s robustness and high spatiotemporal resolution facilitate transient simulations of individual buildings with diverse usage types across large urban areas, addressing common computational constraints and input data limitations in UBEM applications. The rooftop photovoltaic model in CityBEM utilizes a comprehensive approach with physics-based modeling of crystalline PVs, model validation, and a proper design of array networks to manage self-shading effects. More than 57,000 buildings with a combined footprint of 32.37 million square meters are located in the simulation test case covering downtown Montreal (Quebec, Canada) and the surrounding areas. Results indicate that rooftop PV adoption in these buildings could potentially generate 5.9 TWh of electricity annually, reduce energy demand by 27.3%, and achieve an average annual energy saving of 40%. This local generation could also reduce operational CO2 equivalent emissions by over 0.2 megatons. Overall, this study highlights the significant potential of rooftop photovoltaics for a cleaner, more energy self-sufficient urban future.

Optimizing energy efficiency through building orientation and building information modelling (BIM) in diverse terrains: A case study in Pakistan

Building orientation plays a key role in enhancing the built environment's energy efficiency through the application of Building Information Modelling (BIM) and energy simulation. Due to their significant global energy consumption, buildings are the focus of energy optimization efforts. This study explores how building orientation, when combined with modern methods like BIM and energy modelling, affects energy usage and savings throughout Pakistan's numerous geographic terrains. The methodology is quantitative, beginning with an extensive review of literature on energy analysis, BIM applications, and urban building energy modelling. Autodesk Insight 360 is employed for energy simulations, which enables the precise prediction of energy consumption by considering various factors such as building orientation, window-to-wall ratios, shading, wall and roof construction, infiltration rates, lighting efficiency, occupancy controls, plug load efficiency, and HVAC systems. Energy simulation of the data indicates that optimizing building orientation alone can result in an average energy savings of 18 %, while combining orientation optimization with improvements in window arrangements and construction materials can achieve savings of up to 30 % over 30 years. The energy simulation results were validated through case study representing typical terrains and building types (commercial and residential), with simulated energy expenditures showing a high correlation (R2 = 0.92) with actual utility bills, adjusted for local energy rates. The findings of this study highlight substantial financial benefits, with potential annual savings ranging from $2500 to $4000 for residential buildings and $10,000 to $15,000 for commercial buildings, depending on building size and location. These results suggest broader applicability to diverse geographical areas and building types in future research. This research contributes significantly to energy-efficient building practices, paving the way for sustainable construction and reduced environmental impact across the varied landscapes of Pakistan.

Hybrid building energy modeling method with parameterized prototype models and rapid calibration

Building energy models were widely used in building energy performance analysis and efficiency improvement, such as building demand response assessment and energy system optimization. However, building energy modeling was a time-consuming and complex process. Therefore, this paper proposed a hybrid building energy modeling method based on parameterized prototype models and rapid calibration. The main feature of the method was the rapid generation of target building energy models using the prototype building energy performance database. The parameterized prototype building energy model considered 13 uncertainty parameters. Based on the range of 13 uncertainty parameters, such as wall U-value and solar heat gain coefficient, 1000 simulations were performed by Monte Carlo sampling to generate the prototype building energy performance database. The target building energy model was quickly calibrated by learning from this database. It filtered the models that met the requirements based on the actual building measurement data. Based on this hybrid modeling method, the rapid modeling tool AutoBPS-Hybrid was developed in a ruby environment. Shopping mall buildings located in three different climate zones were selected as case studies for this study. The results showed that if only one building energy model meeting the percentage errors was needed, only 3–4 simulations were required. If it was necessary to match the real uncertainty parameter distributions, an average of about 53 simulations was needed. The building energy models were applied to plug load and lighting control in buildings. The shopping mall buildings in Harbin, Beijing and Chengdu could reduce energy consumption by 10.18–13.33 kWh/m2, 14.43–18.15 kWh/m2 and 11.54–14.69 kWh/m2 per year, respectively.

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.

Energy Efficiency and Sustainability in Food Retail Buildings: Introducing a Novel Assessment Framework

One of the primary global objectives is to decrease building energy consumption to promote energy efficiency and environmental sustainability. The large-scale food retail trade sector accounts for over 15% of total primary energy consumption in Europe, posing a significant challenge to the transition towards green energy. This study proposes a simple method for energy efficiency, environmental sustainability, and cost-saving assessment and improvement in large-scale food retail trade buildings. It aims to analyze the energy and environmental performance of building–plant systems, establishing an interactive network to assess intervention potential for the energy transition. The investigation focuses on the proper selection and analysis of the benefits of retrofit solution implementation, emphasizing potential energy savings in current and future climate change scenarios. Dynamic simulation with the Building Energy Model (BEM) was used to evaluate the impacts of building–plant system retrofit solutions, such as high thermal insulation, photovoltaic (PV) panels, Light Emitting Diode (LED) installation, waste heat recovery, and improvement in refrigeration units. The results show a reduction in annual energy consumption for the PV panel installation by up to 29% and lighting systems with high-quality LED to 60%. Additionally, CO2 emissions can be decreased by up to 41% by combining these two strategies.

Advancing building energy modeling with large language models: Exploration and case studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

Comparative assessment of simple and detailed energy performance models for urban energy modelling based on digital twin and statistical typology database for the renovation of existing building stock

The renovation wave calls for an integrated, participatory, and neighbourhood to neighbourhood approach tailored to the local environments. This can be hindered by the lack of geometric and performance input data about the existing building stock. A parametric digital urban building energy modelling (UBEM) tool was developed for local municipalities to automate calculations for comprehensive renovation strategies utilizing public databases specifically at the neighbourhood level. This article compares the accuracy of two energy performance calculation models (seasonal heat balance method and hourly resistance–capacitance method) used within the tool to detailed energy simulation software and measured energy consumption data of existing group of residential buildings in a same neighbourhood. The accuracy for estimating the total primary energy demand was roughly below 10 % for both simplified models. The seasonal calculation method showed consistent overestimation of space heating, depending on building characteristics. Solar and internal heat gains significantly affect the accuracy of simplified heat balance calculations, particularly in well-insulated buildings, while the more detailed 5R1C lumped capacitance hourly method provides improved accuracy for space heating demand. The comparison of uniform (proportional) and project based window area distribution used in the calculation models showed only marginal difference. The study validated simple seasonal and 5R1C hourly calculation methods using data from an apartment building neighbourhood with 22 apartment buildings. Despite larger deviations in the case of individual buildings, the average deviation from measured heating demand was only around 3 %, the seasonal method slightly overestimating and the 5R1C hourly method slightly underestimating the measured values. The primary energy demand including typical values of domestic hot water and household electricity was overestimated by both simplified methods due to lower electricity and water consumption in reality although the difference was below 7 % for the entire district. This concludes that pooling the building envelope heat loss calculation on a district level improves accuracy on average allowing better assessment of comparative renovation strategies.

The Impact of Building Level of Detail Modelling Strategies: Insights into Building and Urban Energy Modelling

The Impact of Building Level of Detail Modelling Strategies: Insights into Building and Urban Energy Modelling

A review of resistance–capacitance thermal network model in urban building energy simulations

With the increasing population and urbanization promotion, energy consumption and carbon emissions have increased, and concern for inefficient energy use and deteriorating urban environments is growing. The prediction of building energy consumption and environmental evaluation, especially at large scales, are considered to be a major challenge confronting the research community. An outstanding strategy for mitigating energy consumption and carbon emissions resides in the field of energy modeling. As a simplified building energy modeling model, resistance–capacitance (RC) network model has the applications in fast predicting building energy consumption. Notably, in recent years, there has been an evident absence of thorough review endeavors related to RC model for urban building energy loads and climate. This review systematically explores the application of low-order models and reduction methods for urban or regional energy simulation starting from the typical RC model for building. In summation, the challenges associated with employing this model for urban building energy loads and climate can be succinctly summarized as follows: the absence of a unified platform for RC modeling; insufficient readily applicable urban datasets; the need for modeling tools that facilitate cross-platform analyses in conjunction with existing Geographic Information Systems (GIS) and urban thermal environment simulation research platforms, and the essential requirement for seamless integration with other complementary modules.

Energy Analysis & Optimization of A Residential Structure

This project delves into the critical realm of energy analysis and optimization for residential buildings. With global energy demand surging and environmental concerns mounting, the need for efficient energy consumption is paramount. The study employs a multidisciplinary approach, combining architectural design, data-driven insights, and advanced technologies. Real-world data collection, including smart meter readings and sensors, forms the foundation for evaluating energy usage trends, peak demands, and seasonal fluctuations. Building Energy Modeling (BEM) software aids in simulating diverse scenarios, enabling accurate predictions of energy performance. Key Words: Autodesk Revit, Autodesk Insight, (EUI) Energy Use Intensity. BIM (Building Information Modelling).

A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution

The energy demand and associated greenhouse gas (GHG) emissions of buildings are significantly affected by the characteristics of the building and local climate conditions. While energy use datasets with high spatial and temporal resolution are highly needed in the context of climate change, energy use monitoring data are not available for most cities. This study introduces an approach combining building energy simulation, climate change modeling, and GIS spatial analysis techniques to develop an energy demand data inventory enabling assessment of the impacts of climate change on building energy consumption in Shanghai, China. Our results suggest that all types of buildings exhibit a net increase in their annual energy demand under the projected future (2050) climate conditions, with the highest increase in energy demand attributed to Heating, Ventilation, and Cooling (HVAC) systems. Variations in building energy demand are found across building types. Due to the large number of residential buildings, they are the main contributor to the increases in energy demand and associated CO2 emissions. The hourly residential building energy demand on a typical hot summer day (29 July) under the 2050 climate condition at 1 p.m. is found to increase by more than 40%, indicating a risk of energy supply shortage if no actions are taken. The spatial pattern of total annual building energy demand at the individual building level exhibited high spatial heterogeneity with some hotspots. This study provides an alternative method to develop a building energy demand inventory with high temporal resolution at the individual building scale for cities lacking energy use monitoring data, supporting the assessment of building energy and GHG emissions under both current and future climate scenarios at minimal cost.

Occupant-Detection-Based Individual Control of Four-Way Air Conditioner for Sustainable Building Energy Management

In this study, individual control of a four-way air conditioner was developed based on the distribution of occupants to prevent unnecessary energy consumption during room-wide control. An occupancy detection algorithm was created in Python using YOLOv5 object recognition technology to identify the occupants’ distribution in space. Recorded video data were used to test the algorithm. A simulation case study for a building energy model was conducted, assuming that this algorithm was applied using surveillance cameras in commercial buildings, such as cafés and restaurants. A grey-box model was established based on measurements in a thermal zone, dividing one space into two zones. The temperature data for the two zones were collected by individually turning on the air conditioner for each zone in turns for a specific period. Manual closure was applied to each supply blade using a tape to provide cooling to the target zone. Finally, through energy simulations, the decreased rates in energy consumption between the proposed individual control and existing room-wide controls were compared. Different scenarios for the occupants’ schedules were considered, and average rates in energy savings of 21–22% were observed, demonstrating the significance of individual control in terms of energy consumption. However, marginal comfort violations were observed, which is inevitable. The developed control method is expected to contribute to sustainable energy management in buildings.

Dynamic predictions for the composition and efficiency of heating, ventilation and air conditioning systems in urban building energy modeling

The composition and efficiency of heating, ventilation and air conditioning (HVAC) systems are crucial inputs for urban building energy modeling (UBEM). However, in current studies, the heterogeneity of HVAC systems is often ignored, leading to huge modeling errors. To address these issues, this study developed a three-step prediction approach that can dynamically forecast the composition of HVAC systems (SystemID), the efficiency of heat sources and the efficiency of entire systems at the urban scale. The performance of the prediction models was evaluated through the ten-fold cross-validation. The results showed that the accuracy in predicting the composition could reach 0.87, and the coefficient of determination (R2) was greater than 0.93 in efficiency forecasts. The accuracy of the developed approach was further evaluated through the testing set in two situations, i.e., with the predicted and actual SystemID data. In the first situation, the overall accuracy in predicting the composition reached 78.2 %, and the R2 for forecasting the system efficiencies was over 0.6. Then, with the SystemID assumed to be accurate, the R2 increased to more than 0.87. To analyze the performance of the developed method in energy use predictions, two case regions in Changzhou and Nanjing were used, respectively. The simulation results were compared with the scenario in which the system efficiencies were determined based on the national standard. The results showed that the developed method could enhance the accuracy of total energy use predictions by approximately 10 % and HVAC energy use predictions by roughly 40 % at both urban and building scales. This helps policy-makers craft energy-saving strategies more reasonably.

CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors

The significant energy consumption associated with the built environment demands comprehensive energy prediction modelling. Leveraging their ability to capture intricate patterns without extensive domain knowledge, supervised data-driven approaches present a marked advantage in adaptability over traditional physical-based building energy models. This study employs various machine learning models to predict energy consumption for an office building in Berkeley, California. To enhance the accuracy of these predictions, different feature selection techniques, including principal component analysis (PCA), decision tree regression (DTR), and Pearson correlation analysis, were adopted to identify key attributes of energy consumption and address collinearity. The analyses yielded nine influential attributes: heating, ventilation, and air conditioning (HVAC) system operating parameters, indoor and outdoor environmental parameters, and occupancy. To overcome missing occupancy data in the datasets, we investigated the possibility of occupancy-based Wi-Fi prediction using different machine learning algorithms. The results of the occupancy prediction modelling indicate that Wi-Fi can be used with acceptable accuracy in predicting occupancy count, which can be leveraged to analyze occupant comfort and enhance the accuracy of building energy models. Six machine learning models were tested for energy prediction using two different datasets: one before and one after occupancy prediction. Using a 10-fold cross-validation with an 8:2 training-to-testing ratio, the Random Forest algorithm emerged superior, exhibiting the highest R2 value of 0.92 and the lowest RMSE of 3.78 when occupancy data were included. Additionally, an error propagation analysis was conducted to assess the impact of the occupancy-based Wi-Fi prediction model’s error on the energy prediction model. The results indicated that Wi-Fi-based occupancy prediction can improve the data inputs for building energy models, leading to more accurate energy consumption predictions. The findings underscore the potential of integrating the developed energy prediction models with fault detection systems, model predictive controllers, and energy load shape analysis, ultimately enhancing energy management practices.

Enhancing the building resilience in a changing climate through a passive cooling roof: A case study in Camas (Seville, Spain)

Renovating buildings is a crucial mission for the coming decades to combat energy inefficiencies and produce a resilient building stock. The inefficient thermal envelope has significant implications for occupants, especially for families lacking the financial resources to maintain a healthy and comfortable indoor environment. In scenarios where households cannot afford or utilize HVAC systems and high temperatures are more frequent, building retrofits should prioritize reducing heat loss in winter and passively lowering indoor temperatures in the cooling season. This work introduces a new solution to enhance the liveability of a social housing building in southern Spain by integrating a double-skin roof with a conventional retrofit strategy, which incorporates passive cooling techniques – night ventilation and an evaporative cooling system – for effective heat dissipation based on available commercial solutions. An extensive monitoring campaign was conducted over three years to evaluate the thermal comfort of occupants and the risk of overheating in naturally conditioned buildings before and after renovation following two impact assessment methods: real-time data-based and simulation-based. The real-time data-based method compared the indoor conditions of a dwelling with a conventionally retrofitted roof to one with a double-skin roof, indicating that the dynamic roof maintains a ceiling temperature lower than the air temperature throughout the summer while the conventional roof acts as a heating surface. On the other side, the simulation-based method compared the observed indoor conditions with a double-skin roof enabled during 2023 to the initial stage and a double-skin roof disabled scenario, using calibrated building energy models. The natural cooling roof solution almost eliminates the overheating events, leading to a reduction of 94.1% and 76.9% in discomfort degree-hours compared to the initial stage according to the ASHRAE adaptative and Fanger comfort models, respectively. Additionally, the results indicate that conventional retrofits can increase the risk of overheating when a cooling system is not considered. Integrating a cool roof solution requires only an extra implementation cost of 31€/m2 compared to a conventional solution, having an operational cost that represents less than 3% of the minimum monthly income in the social housing district.