Building Energy Model Research Articles

Sustainability and affordability of building electrification: A state-by-state holistic approach for multifamily buildings

This paper explores the evolving narrative of building electrification, considering its potential to solve climate change. Previous research has predominantly focused on hybrid renewable energy portfolios and increasing home pricing premiums, overlooking the perspectives of renters burdened by housing costs. Moreover, existing studies have primarily examined the effects of electrification on single-family homes and case studies, neglecting multifamily buildings and their relationship with the grid. This comprehensive study leverages calibrated building energy models to address these gaps and evaluates sustainability through carbon dioxide emissions and affordability through economic performance. The findings demonstrate significant progress in electrification since 2017, with nearly all states showing decreased energy usage in the electrified models. Environmentally, twenty-two states perform better or comparably with the electrified models in the most recent study. Economically, challenges persist, but a sensitivity analysis demonstrates how results could improve soon. Furthermore, the study discusses the outlook of electrified multifamily buildings, considering ongoing decarbonization plans for the electric grid. In three case studies, electrification demonstrates improvements in over half of the states by 2026. Overall, the research highlights the importance of expanding analysis to include multifamily buildings and emphasizes the positive impact of electrification as a viable energy, environmental, and economic solution.

Developing a systematic framework for integrating life cycle carbon emission assessment in urban building energy modeling

Extensive research has been conducted in recent years on the environmental evaluation of building lifecycles, spanning construction, usage, and end-of-life phases. This holistic approach shows promise in assisting cities to achieve decarbonization goals, given the significant carbon emissions attributed to buildings in urban areas. Prior efforts have successfully utilized Urban Building Energy Modeling (UBEM) tools to assess city-scale building energy consumption and explore energy efficiency scenarios. However, the integration of Life Cycle Assessment (LCA) with UBEM, a robust environmental assessment approach, remains underexplored. The combination of LCA and UBEM can provide a comprehensive understanding of a building's environmental impact, considering energy performance as well as broader considerations such as materials, construction processes, and waste management. Despite the evident benefits, there exists a research gap in fully harnessing the potential of integrating LCA and UBEM methodologies. Further investigation is thus warranted to unlock the synergies between these approaches, establishing an integrated framework that enhances sustainability assessments and informs decision-making in the built environment. A systematic approach is essential to address the challenges associated with the development of a city-scale environmental tool. This entails defining the challenges, identifying key components, establishing a structured framework, and testing and refining it through iterations. The defined framework should be implemented in modeling tools that offer a compatible environment.To address this need, this study proposes a systematic approach to the development of a framework for the integration of LCA into UBEM tools. Firstly, a structured procedure is defined to facilitate the systematic creation of the integrated framework. This procedure serves as a guide for developers to adopt a comprehensive and organized approach in addressing the challenges of combining LCA and UBEM methodologies. Subsequently, a workflow is designed to accommodate the implementation of any improvement practices within the defined systematic process of integrating LCA into UBEM tools.Applying the developed workflow in a real case study in Montreal, Canada, reveals that embodied carbon emissions can actually be higher than operational emissions, when the energy supply is renewable (hydropower). The outcomes of this research are expected to provide guidance for future studies on Urban Building Environmental Assessment (UBEA), facilitating a structured approach to advance and improve the field.

Finding the gaps in design strategies and technological advancements for net-zero energy buildings development in India

The climate-adaptive net-zero energy building design is an effective trend for achieving a carbon-neutral environment and reducing global energy demand, especially, in India where building energy consumption recorded substantial growth in the past decade. This review article focuses on the development of net-zero energy buildings in tropical climates through the analysis of 44 real case studies. This study investigates the building envelope parameters, advance technologies, and their effectiveness on the building energy models. In the first phase of this study, fourteen net-zero energy buildings or high-performance buildings in the USA, China, and India considering energy-efficient design features and their significant effect on energy consumption have been examined. Moreover, the role of building simulation tools in improving energy efficiency is discussed. The second phase investigates the thirty best practices of net-zero energy buildings in tropical climates worldwide based on envelope choices, heating, cooling, and lighting features. Furthermore, this review discusses the evolving definitions, challenges, and inconsistencies in terminologies of net-zero energy building and illustrates India's initiatives towards net-zero development. The objectives of this review study are to highlight the challenges in building material research, advanced lighting, heating, ventilation, and air conditioning technologies, and integration of renewable energy compared to developed nations. Additionally, the gaps that are the barriers to the development of net-zero energy building in India have been identified. The review eventually concludes by providing policy recommendations and suggesting areas for future research to facilitate net-zero energy-building development in India.

Performance gap analysis for Korean building energy efficiency certification

Performance gap between predicted and measured building energy use can be caused by simplifications and assumptions introduced in the building energy modeling process, uncertainties in building operation such as heating and cooling setpoint temperature, operational hours, occupant behavior, heat generated from lights and equipment, etc. In South Korea, the building energy efficiency certification system (BEEC) evaluates annual energy use intensity (EUI, kWh/m2/yr) and assigns ten certificate levels using ECO2, a EUI calculation tool developed by the Korean government that is based on ISO 52016 and DIN V 18599. Firstly, we collected information on 158 non-residential BEEC-certified buildings and their actual energy usage data. Subsequently, we analyzed the performance gap between predicted and measured EUI for 158 non-residential buildings in terms of building types, heating and cooling degree days, chiller types, gross floor area, window-wall ratio, and envelope U-value. The study reveals that the performance gap is significant, showing mean absolute error ranging from 21.3 to 417.4 kWh/m2/yr, mean biased error from −3.2 to 73.4 %, and the coefficient of variance of the root mean squared error from 57.0 to 304.9 %, respectively. It is also noteworthy that even individual building’s annual consumption during eight years (2014–2021) is annually fluctuating, indicating that even the performance gap is a moving target. This study reassures that building energy certification in the design stage should be understood as a rating, not for a prediction because of many unpredictable and stochastic behavior of building energy systems and occupants.

Urban microclimate prediction based on weather station data and artificial neural network

Urban microclimate has a significant impact on building energy consumption. Building energy modeling (BEM) requires accurate local weather conditions near a target building, whereas Typical Meteorological Year (TMY) weather inputs often use remote airport weather data. An artificial neural network (ANN) model is presented in this study to predict urban microclimates based on long-term measurements from local weather stations near urban buildings and their significance in analyzing building energy consumption. By utilizing only a few months of data, the ANN model could connect local and remote meteorological parameters for a whole year. The 20-year historical weather data at the airport was then used to generate a local TMY. Based on the original and local TMYs, this study compared building heating and cooling loads. This method was evaluated for five weather stations within the city of Montreal to assess the impact of the local microclimate on the energy consumption of buildings. Based on locations, urban microclimate contributed to an additional 2 % to 14 % cooling energy consumption and a reduction of 1 % to 10 % winter heating energy consumption.

Office building energy consumption forecast: Adaptive long short term memory networks driven by improved beluga whale optimization algorithm

With the development of urbanization, buildings have become a major source of energy consumption. This research uses a data-driven approach to achieve accurate building energy consumption prediction by analyzing and modeling building energy consumption data. The model proposed in this paper uses improved beluga whale optimization algorithm (IBWO) to optimize long short-term memory networks (LSTM) for accurate energy consumption prediction. In order to enhance the ability of BWO in global search and local exploitation, a new method of dynamic adjustment of step factor as well as strategies such as nonlinear decreasing are introduced to improve BWO. For the first time, it is proposed to explore the accuracy of the number of hyper-parameters of LSTM on the prediction of energy consumption, and the improved beluga whale optimization algorithm is used to optimize the two, three, and four hyper-parameters of LSTM respectively. Then short-term prediction of historical energy consumption data of an office building in Xi'a is performed. Experiments show that the optimization of the four hyperparameters of LSTM using the IBWO of this paper can reduce the mean absolute error (MAE) of the pre-improvement model from 830.71 KW to 128.28 KW, the mean absolute percentage error (MAPE) from 12.32 % to 1.38 %, and the coefficient of variation (CV) from 7.5 % to 1.2 %.

A novel acceleration approach to shadow calculation based on sunlight channel for urban building energy modeling

Shadow effect among buildings has great impacts on the building energy consumption and the performance of building photovoltaic, and calculating shadows on building surfaces in urban building energy modeling (UBEM) faces challenges of inefficiency, especially for dense urban areas. In this study, a novel shadow calculation approach based on sunlight channel is proposed that can streamline the surrounding environment and accelerate the shadow calculation process. The sunlight-channel algorithm can further accelerate the shadow calculation process by dynamically predetermining the shading surfaces according to the actual solar position. In a real urban context, the proposed approach can accelerate the computation process by over 10 times over the baseline and over 34 times over the non-accelerated method, with a mean absolute percentage error (MAPE) of 1.13% for the total solar radiation. The proposed approach copes well with both large-scale urban models and the complexity of building structures, particularly for urban models with complex changes in building heights. This approach can significantly enhance the computational efficiency in complex urban environments, facilitating an accurate and rapid analysis of the energy consumption and solar potential of buildings in dense cities.

Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy model

Quantifying energy consumption of database operations is the foundation of building energy-efficient database systems. Existing approaches only focused on stand-alone database servers, while we are interested in modeling energy consumption of database operations in the distributed environments. In this study, we aim at providing an accurate energy consumption model for queries executed in distributed database systems, profiling energy consumption characteristics of both the individual queries and the system as a whole, to guide the design of green database systems and to reveal opportunities for energy-efficient computing. As the execution of a distributed query is a combination of a set of subqueries that decomposed from it, we start from building energy models for individual subqueries by extracting basic operations that can effectively reflect their energy consumption. Then we use a bottom-up measuring and modeling method as the basis to provide a comprehensive energy estimation model for the entire distributed query. To validate the accuracy of the model, we use queries with a variety complexity that generated from three standard benchmarks (TPC-H, SSB, and Sysbench) on real distributed databases. Extensive experimental results show that our solution can achieve a high average accuracy of 94.63% for distributed queries. More importantly, based on these results we further explored energy consumption patterns for distributed queries and presented several important implications. And finally, a significant role of distributed joins has been discovered in saving both idle and dynamic energy cost of the system. We hope that taking advantage of our observations can help readers who wish to substantially improve energy efficiency for distributed database systems.

Analysis of Uncertainties and Associated Convective Processes in Simulations of Extreme Precipitation Over Cities With a Regional Earth System Model: A Case Study

AbstractThis study utilizes the Weather Research and Forecasting model coupled with an atmospheric chemistry model, a multi‐layer urban canopy model (UCM), and a building energy model to simulate the extreme rainfall event influencing the Guangzhou city in South China on 7 May 2017. By employing small variations in the longwave emissivity of buildings within the UCM, 11 convective‐permitting experiments are conducted. The maximal 18‐hr and hourly rainfall accumulation vary from a 2‐year return period to as high as a 20 or 40‐year return period with notable spatial differences. Comparisons between the more accurate group (GOOD) and less accurate group (POOR) simulations highlight that some minor differences in the near‐surface air thermodynamic conditions in urban area could lead to substantial differences in local convection and its impacts on subsequent convective systems. With persistent transportation of warm, moist airflows from the northern South China Sea, formation of a slow‐moving mesoscale outflow boundary to the north of the urban agglomeration leads to the development of a quasi‐stationary, compactly structured meso‐γ‐scale rainstorm in the GOOD simulations. The stronger low‐level to near‐surface convergence and mid‐level cyclonic shear within this system substantially enhance low‐level updrafts, leading to increased microphysical production and stronger horizontal advection of rainwater within the system. These findings offer some process‐based understanding about the uncertainties in simulating urban extreme rainfall and underscore the need to develop ensemble forecasting methods for convection‐permitting numerical models that incorporate increasingly complicated representations of anthropogenic influences.

Calibration of simulation model to analyze hospital building energy performance

This research paper addresses the calibration of an energy simulation model for a naturally ventilated hospital building located in Mangalore, Karnataka, India, in a warm, humid climatic zone. The study aims to enhance the accuracy of energy consumption calculations through a calibration process by developing the energy model of a reference hospital building using EnergyPlus software. The required architectural design data, construction details, occupancy and schedule data and services are collected through documents, energy audits and documentation, measured drawings, and semi-structured interviews. Calibration parameters are systematically identified and adjusted through an iterative process. The monthly electricity bill is used to validate the simulation model. The simulation model reached during the validation process has an excellent Coefficient of Variation-Root Mean Squared Error of 4.24% and a Normalized Mean Bias Error of −4.42%, both of which meet the ASHRAE-approved accuracy standards. The paper also discusses the challenges and emphasizes the need for a nuanced approach to modeling healthcare facilities. Notwithstanding the challenges faced, the study offers insightful information about how to calibrate simulation models for hospital energy usage with the significant influence of scheduling for artificial lighting and equipment usage. By pioneering a manual calibration approach tailored to hospital simulation models in the warm humid climate of India, this paper offers a novel and practical solution to address the challenges of energy performance analysis in resource-constrained environments. The calibrated simulation model presented in this study is a valuable tool for assessing and improving the energy performance of naturally ventilated healthcare facilities. By creating alternative space layouts, the research findings aim to foster the development of hospital infrastructure that is both ecologically friendly and energy-efficient in a specific climatic context. This research is assured to make a substantial contribution to the advancement of sustainable hospital design in warm-humid climates, with implications for both academia and industry.

Unlock city-scale energy saving and peak load shaving potential of green roofs by GIS-informed urban building energy modelling

Urban green roofs have emerged as a significant trend in urban architecture worldwide, offering numerous benefits, including enhanced energy performance, improved urban microclimate, and public health. This study proposes a holistic framework for assessing the green roofs' energy-saving potential at the city scale, achieved by scaling up building-scale energy simulations to city-scale energy demands. Firstly, massive building information is collected using Geometric Information System (GIS) technologies. Subsequently, prototype buildings are generated to accurately represent the geometric characteristics of buildings at the city scale. Building performance simulation is further conducted considering three types of plants on roofs. A case study in Xiamen, China, demonstrates the effectiveness of the proposed framework to efficiently quantify the city-scale energy-saving potential of green roofs. By implementing green roofs in Xiamen, energy savings of 1.62–1.83% and peak load shaving of 1.10–1.63% can be achieved for the whole city. Overall, the proposed framework has the potential for widespread application in other cities with minor adjustments to accommodate variations in climate and building parameters.

A novel shadow calculation approach based on multithreaded parallel computing

Solar radiation significantly affects the thermal processes of buildings; thus, various building energy modeling programs have established corresponding shadow calculation modules. However, the large number of projection surfaces in a building requires extensive calculations, making it difficult to rapidly analyze the building shadow while ensuring hourly calculations. This study proposes an accelerated calculation framework for building shadows based on multithreaded parallel computing for an accurate building energy consumption analysis. Based on the basic principle of shadow projection calculation, the embedded, integrated development of a shadow calculation module is proposed, and a full-performance simulation platform kernel based on a multithreaded structure is constructed. Results show that the multithreaded shadow calculation framework can significantly shorten the calculation time. Based on case studies, a typical complex single building’s shadow calculation speed can be increased by nearly 2.4 times, whereas for city-scale building shadow simulations, it is only 5.4% of the original duration.

Futures for electrochromic windows on high performance houses in arid, cold climates

This study investigates high performance electrochromic windows used on a passive house and residential dwelling to IECC 2021 (i.e., IECC dwelling). In the lab, the electrochromic film switches transmitted solar heat gain coefficient (SHGC) from 0.09 to 0.7 and visible transmittance from 0.15 to 0.82 with power consumption of 1.23 W/m2 during switching times less than 3 minutes. We extrapolate these results to a window assembly. Building energy models of the houses were evaluated in Santa Fe, New Mexico. A Monte Carlo analysis for 2020, 2040, 2060, and 2080 was conducted for Shared Socioeconomic Pathways 2-4.5, 3-7.0, and 5-8.5. Cases with and without the electrochromic windows and with and without electricity were used to determine energy use intensity and hours beyond thermal safety thresholds.The passive house showed 1.3-3.1% mean energy savings and the IECC dwelling 4.4-5.1% with electrochromic efficiency benefits growing into the future for both cases. Even so, overall savings decrease into the future for the passive house, due to growth in cooling load being dominant, conversely overall energy savings increase into the future for the IECC dwelling due to heating loads being dominant. For thermal resilience, the passive house exhibited a mean percent decrease of 0.02-0.31% hours in the extreme caution (i.e., > 32.2∘C, ≤ 39.4∘C) range while the IECC dwelling exhibited 0.38-4.38%. The study therefore shows that electrochromic windows will have smaller benefits for the passive house in comparison to the IECC dwelling. The relationship between electrochromic windows is shown to have a complex relationship between house efficiency and climate change by these results.

Comparative Validation of a Building Energy Model Calibration Methodology with a Focus on Residential Buildings

Comparative Validation of a Building Energy Model Calibration Methodology with a Focus on Residential Buildings

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.