Urban Building Energy Modeling Research Articles

District-level validation of a shoeboxing simplification algorithm to speed-up Urban Building Energy Modeling simulations

The “shoeboxing” algorithm is a simplification approach capable of converting a building of any shape into a representative shoebox, with the aim of speeding-up Urban Building Energy Modeling simulations. The procedure works towards accurately predicting both annual energy needs and hourly thermal loads, going beyond the preliminary assessment of buildings' thermal performance at city-scale. Furthermore, the simplification has been particularly developed to work with buildings of complex geometry considering adjacencies and obstructions. After a first validation performed for stand-alone buildings, in this paper, the capabilities of the algorithm are evaluated at district-level on fictional parametrically generated layouts and in different climatic conditions. As a whole, the shoeboxing algorithm properly predicted both heating and cooling needs at building-level in all the considered climatic conditions, yielding annual differences within ±10% and ± 20%, respectively for cooling and heating. Moreover, both annual and hourly deviations showed to be similar in the different climates considered, suggesting that the simplification can be reliably employed worldwide. Finally, the thermal simulation time has been reduced up to 36 times.

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

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

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

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

Automated energy performance certificate based urban building energy modelling approach for predicting heat load profiles of districts

Urban building energy modelling (UBEM) for analysing buildings in their spatial and functional context is an arising method. Only a few UBEM procedures use detailed building simulation tools, which are essential for high temporal and spatial resolution. This paper aims at developing a detailed automated physical bottom-up UBEM framework based on archetypes using Energy Performance Certificate data for predicting hourly heat load profiles of residential buildings. Simulation results are compared to and validated with measurements of two district heating networks and values from the TABULA typology. A comparison of the simulated hourly heat load profile for space heating and domestic hot water with measurement data results in a CV(RMSE) of 0.3, NMBE of 0.085, R2 of 0.85 and r of 0.94 for a sample size of 66 residential buildings, solely based on an estimation of the 3 classification criteria of the archetypes (building period, building condition and building type) and an estimation of the conditioned gross floor area for each measured building. Hence, the model can be declared as calibrated according to acceptance criteria in literature.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change.

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization. Urban building energy modeling (UBEM) is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations, supporting the implementation of carbon emission reduction policies. Currently, most studies focus on the energy performance of archetype buildings under climate change, which is hard to obtain refined results for individual buildings when scaling up to an urban area. Therefore, this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas, by taking two urban neighborhoods comprising 483 buildings in Geneva, Switzerland as case studies. In this regard, GIS datasets and Swiss building norms were collected to develop an archetype library. The building heating energy consumption was calculated by the UBEM tool-AutoBPS, which was then calibrated against annual metered data. A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%. The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results showed a decrease of 22%-31% and 21%-29% for heating energy consumption, an increase of 113%-173% and 95%-144% for cooling energy consumption in the two neighborhoods by 2050. The average annual heating intensity dropped from 81 kWh/m2 in the current typical climate to 57 kWh/m2 in the SSP5-8.5, while the cooling intensity rose from 12 kWh/m2 to 32 kWh/m2. The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7% and 18.6%, respectively, in the SSP scenarios. The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

Urban building energy modeling from geo-referenced energy performance certificate data: Development, calibration, and validation

Urban building energy models (UBEMs) are considered as applicable tools for urban energy planning. Model developers use different strategies to simulate urban building energy use appropriately, and yet they are often doing so in the absence of high-quality data. While data collection is challenging in many cases, in Sweden, the availability of national databases is relatively good and is expected to facilitate the modeling procedure considerably. This study aims to develop, calibrate and validate an UBEM using available national data, including GIS-based property maps and energy performance certificates (EPCs). The developed UBEM offers an automated framework for constructing simple building-level models from open data and conducting energy simulations in EnergyPlus. The developed UBEM was calibrated and validated for two case study cities in Sweden, Borlänge and Uppsala, where the mean absolute percentage error (MAPE) between simulated results and EPC data was 26% and 22%, respectively. Furthermore, a downward trend was observed in the MAPE with increasing spatial aggregation from building to district and city levels (from 26% to 21% and 10%), which highlights the performance of the UBEM in this study to support accurate urban-scale energy analyses for buildings.

TOM.D: Taking advantage of microclimate data for urban building energy modeling

TOM.D: Taking advantage of microclimate data for urban building energy modeling

A combined workflow to generate citywide building energy demand profiles from low-level datasets

Urban building energy models provide an efficient way to estimate the district's energy demand and have been vigorously developed over the years. However, due to the lack of crucial information about building's physical descriptions, existing datasets cannot always meet the significant data demand for modeling. In addition, it is challenging to balance simulation time while ensuring simulation accuracy. Therefore, in order to solve the above difficulties, this paper makes some improvements and optimizations based on reduced-order building modeling and experience-based archetypes and presents a novel two-step approach combining bottom-up and top-down methods. A case study in a suburban district in Germany was chosen as a sample test case to demonstrate these workflow automation tools. The proposed workflow achieved a mean absolute percentage error of less than 2% for citywide energy demand prediction using low-level public data, compared to an error of 15% in the scenario without public data. The results indicate that although there was a lack of specific input data, the presented workflow was capable of obtaining accurate results with reduced computation time. The presented methodology offers an efficient way to establish urban building energy models using low-level datasets and provide relatively accurate results on a district level.

Toward Improved Urban Building Energy Modeling Using a Place-Based Approach

Urban building energy models present a valuable tool for promoting energy efficiency in building design and control, as well as for managing urban energy systems. However, the current models often overlook the importance of site-specific characteristics, as well as the spatial attributes and variations within a specific area of a city. This methodological paper moves beyond state-of-the-art urban building energy modeling and urban-scale energy models by incorporating an improved place-based approach to address this research gap. This approach allows for a more in-depth understanding of the interactions behind spatial patterns and an increase in the number and quality of energy-related variables. The paper outlines a detailed description of the steps required to create urban energy models and presents sample application results for each model. The pre-modeling phase is highlighted as a critical step in which the geo-database used to create the models is collected, corrected, and integrated. We also discuss the use of spatial auto-correlation within the geo-database, which introduces new spatial-temporal relationships that describe the territorial clusters of complex urban environment systems. This study identifies and redefines three primary types of urban energy modeling, including process-driven, data-driven, and hybrid models, in the context of place-based approaches. The challenges associated with each type are highlighted, with emphasis on data requirements and availability concerns. The study concludes that a place-based approach is crucial to achieving energy self-sufficiency in districts or cities in urban-scale building energy-modeling studies.

Toward a Fast but Reliable Energy Performance Evaluation Method for Existing Residential Building Stock

Building a reliable energy model for old residential buildings with insufficient documentation and user assistance is a challenging and time-consuming task. Nevertheless, the ambitious European decarbonization targets require this building stock to be renovated, making energy assessment a key priority. In line with this goal, the following study explores a more simplified and automatic framework to generate a residential building energy model (BEM). The paper’s approach is based on the concept of urban building energy modelling (UBEM) archetypes or building prototypes and is customized according to the principles of dynamic simulations performed in the existing BEM software, Integrated Environmental Solutions Virtual Environment IES VE, and Solemma Open Studio. Therefore, based on three real starting inputs, a prototype database (DB) of assigned inputs is generated, i.e., an input matrix, using Google Maps as a geometry source. Other data are drawn from tabular DB. The proposed approach is evaluated by benchmarking the simulation results with precise models and monitoring the data that come from the Horizon2020 project REZBUILD. Nevertheless, a level of simplification is introduced that creates less accurate results for total or system-level energy consumption; this is compensated for using a set of simple calibration steps. The approach gives promising results for daily indoor temperature, making it a suitable indicator for evaluating further retrofitting alternatives.

Framework for Quantifying Energy Impacts of Rehabilitation of Derelict Buildings: Assessment in Lisbon, Portugal

Cities are currently responsible for an important part of energy consumption and greenhouse gas emissions, justifying the need to develop measures to help them become more sustainable. One of those measures can be to address under-utilized assets in cities, such as derelict buildings with high potential for rehabilitation, and the establishment of new residence hubs within cities. Consequently, this work establishes a novel framework for evaluating the impact of rehabilitating these buildings in an urban area in Lisbon, considering the energy consumption associated with the usage of the dwelling as well as the impact on mobility, since it was considered that these buildings will be occupied by people who currently work nearby but live in the outskirts of Lisbon, favouring an urban planning of proximity between home and work. To this extent, a methodology was developed for selecting the buildings to be analysed and the commuting movements to be replaced. Then, buildings were simulated in an urban building energy modelling (UBEM) tool, considering three rehabilitation scenarios, and the required primary energy, CO2 emissions, and costs were calculated. Regarding mobility, three new scenarios were compared with the current scenario. The results obtained confirm the high potential savings from the rehabilitation of derelict buildings and in the best-case scenario—corresponding to the rehabilitation considering envelope insulation, the installation of efficient windows, and the adoption of a heat pump together with a mobility standard targeting 15 min cities—reductions of 76% in primary energy and 84% in CO2 emissions were achieved.

Evaluating energy retrofits of historic buildings in a university campus using an urban building energy model that considers uncertainties

Urban building energy model (UBEM) is a powerful tool to simulate performance and evaluate efficiency of upgrades for a group of buildings under the urban context. However, the larger the scale/number of buildings, more parameters must be collected to create energy models that cover each individual building, causing more uncertainties. To reveal this, this study created a UBEM for the mixed modern and historic buildings at a campus in China. The calibrated set of UBEMs with the modeled results meeting the 20% error range, were then used to evaluate uncertainties of energy-savings of four building energy retrofit (BER) measures. The first measure, BER 1, was to preserve the historic values of buildings; BER 2 to meet green building design standard; BER 3 to achieve 20% more savings than BER 2; and BER 4 to utilize renewable photovoltaic energy. For BER 1, BER 2, and BER 3, the energy savings of buildings of different ages varied within 10%–44%. For BER 4, the energy savings of buildings varied within 49%–505%, respectively, where the reason for higher than 100% is energy production is much higher than energy demand. Similar results can be concluded for building functions, for BER 1, BER 2, and BER 3, the energy-saving potentials varied within 6%– 45%, while 97%–492% to BER 4. This study can provide an important and significant reference to apply UBEM in evaluating energy-efficient retrofits as well as other energy-related studies that consider uncertainties.

A large scale artificial dataset to evaluate the impact of on-board monitoring set-ups on the Heat Transfer Coefficient assessment

At the current state, the majority of residential buildings are underperforming in terms of energy. To assess the current state of the building sector and bridge the gap between the designed and operative energy demand, quantifying the as-built envelope behaviour is essential. For this purpose the Heat Transfer Coefficient is used as a stationary performance indicator, however, prevailing assessment methods are impractical as they are performed on individual buildings. To extend the assessment range to urban scale statistics-based modelling techniques can be applied to limited and on-board collected data. In this step, where the accuracy of statistical methods and different input scenarios are investigated, simulated data is more suitable since there is no influence of measurement inaccuracy and the expected outcome is known. As part of this research, a comprehensive dataset to assess the HTC estimation methodology is created. The artificial dataset is generated using urban building energy models representative of the Belgian residential building stock. The generated artificial datasets resemble ideal measurement campaigns where inputs can be systematically selected which supports the aim of determining the impact of available monitoring setups. In this work, black-box AutoRegressive with eXogenous input statistical models were applied and the results show that, considering the assumed modelling limitations, with ideal monitoring setups 85% of the estimates fall within 10% of the target.

Data-driven urban building energy models for the platform of Toronto

Data-driven urban building energy models for the platform of Toronto

A comprehensive survey of the urban building energy modeling (UBEM) process and approaches

Fossil fuels increase the emission values of greenhouse gases such as CO2 in the atmosphere and cause global warming and climate change. At the same time, fossil fuel reserves are facing depletion in the near future, and energy supply also has an important dimension such as national security and foreign dependency. All these show that turning to renewable energy sources and developing solutions and policies for energy saving has become a necessity both globally and locally. For such reasons, modeling of urban structures, which have a great contribution to energy consumption, and simulating the energy demand on an urban scale are of great importance for the effective use of energy. Research on this has shown that UBEM (Urban Building Energy Modeling) is an effective solution to these problems. However, UBEM contains different technical problems for implementation. Due to its versatility, various concepts related to this field lead to complexity. With this increasing complexity, there is a growing need to compile concepts from a holistic perspective. In this study, it is aimed to create a solution to these challenges. For this purpose, a comprehensive and up-to-date research of various modeling approaches and model creation process used in urban building energy modeling has been conducted. Studies on these approaches are summarized and a systematic review of the literature is made. At the same time, the study is in the nature of guiding and forming the general knowledge level with the basic concepts that should be known to those who will work on UBEM.

Investigating the impact of urban microclimate on building thermal performance: A case study of dense urban areas in Hong Kong

Urban microclimate conditions could be an important factor influencing building thermal performance. However, most studies on urban building energy modeling (UBEM) use the typical meteorological year (TMY), often developed from observations of exposed/ rural sites, as input weather data. This study aims to assess the impacts of urban heat on building thermal performance by coupling a high-resolution urban climate simulation with UBEM. The simulated building thermal performance was compared with and without urban microclimate considerations, using weather data from the TMY, weather observations from an urban oasis, and outputs from the urban climate model. The physical parameters of typical building archetypes used in the UBEM were calibrated against indoor-measured data during summer days. Without considering urban microclimate, there was an underestimation of up to 140% of the average overheating risks of urban buildings. Furthermore, neighboring urban contexts and building ventilation rates considerably affected the thermal performance of individual buildings within high-density urban areas. The study reveals that neglecting the influence of the urban microclimate can result in a notable error in the building thermal performance assessment even at the urban level.

Census-based urban building energy modeling to evaluate the effectiveness of retrofit programs

Housing retrofits are essential for meeting societal decarbonization goals, alongside addressing energy insecurity, improving public health, and creating new jobs. Yet, despite their multiple benefits and comprehensive government efforts to incentivize retrofits, adoption rates across the world remain low, usually less than 1% per year. Barriers to adoption among homeowners include lack of knowledge of what combination of energy retrofitting upgrades are most cost effective for their situation given available incentive programs. Similarly, cities lack urban-level analysis tools to optimize uptake of and predict carbon emissions reduction from existing incentive programs. To address the latter gap, we present a census-based Urban Building Energy Modeling framework that combines a technical energy saving potential analysis with a socioeconomic model that includes occupant demographics, local building regulations, and incentive eligibility criteria. We use the framework to evaluate the effectiveness of retrofit programs in two Boston neighborhoods with median incomes of $110,00 and $42,000. Results reveal that for the higher income, neighborhood predicted and actual adoption rates between 2014 and 2017 are comparable. In the lower income neighborhood, the proportion of households that would financially benefit from incentive offerings is higher. However, current participation rates do not reflect this difference suggesting that many viable projects do not happen for reasons that are not yet captured by the model. Urban planners, energy policy designers, and community advocates seeking to plan and evaluate energy incentive programs can use this framework to understand the breakdown of opportunities and barriers for different socio-demographic groups and geographic locations.

AutoBPS: A tool for urban building energy modeling to support energy efficiency improvement at city-scale

China has promised to peak carbon emissions by 2030 and become carbon neutral before 2060. Achieving this target requires actions from all sectors, especially the building sector, which accounts for 33 % of the total energy consumption in China. Within this scenario, this paper introduces a new urban building energy modeling tool, Automated Building Performance Simulation (AutoBPS), to calculate urban building energy demands and to analyze energy retrofit and rooftop photovoltaic (PV) potential. AutoBPS uses a building-by-building approach to automatically generate multi-zone (one zone per floor) models based on the city building dataset, considering the shading effect by surrounding buildings. The tool simulates building energy use and PV generation via EnergyPlus. A downtown district of 3633 buildings in Changsha, China, was selected as a case study to demonstrate the features of the tool. Firstly, annual baseline energy uses of 1793 GWh were simulated by inputting Geographic Information System (GIS) data. Then, different energy conservation measures (covering envelope, lighting, cooling system upgrade, etc.) and roof PV generation systems were adopted to explore energy saving potential. The results showed that the window upgrade was the most energy-efficient individual measure for residential buildings, while the lighting upgrade was that one for commercial buildings. Altogether three measures could save 2.7 ∼ 30.7 % of annual site energy use intensity per building. The total energy demand could be reduced by 18.5 % by combining all measures and saved by 38.6 % when PV installation was added. AutoBPS can be easily applied to other cities, supporting urban energy efficiency plans for carbon reduction goal implementation.

Using urban building energy modeling data to assess energy communities’ potential

The decentralized solar deployment will play a crucial role in the energy transition, especially in urban areas where high electricity consumption density is found. By implementing local generation systems, such as photovoltaic panels in buildings’ roofs, energy communities (EC) arise as an innovative and cooperative strategy to share these decentralized energy resources.This work develops a modeling framework to assess the potential of EC creation, by combining Urban Buildings Energy Modelling (UBEM) capabilities and building’s rooftops potential for solar generation. Hence, three EC case studies, with multiple building typologies, have been simulated and analyzed for three energy sharing scenarios: individual self-consumption outside an EC and collective self-consumption without and with a central battery storage, both inside an EC.The case study results demonstrate that self-sufficiency in buildings increases when going from individual self-consumption to collective self-consumption, having the best results when combining diverse demand profiles. Self-sufficiencies achieved at a community level range from 16% to 34%. Moreover, results show that when considering battery storage systems, self-sufficiency increases 16 percentual points, however decreasing the economic viability.By considering UBEM outputs the developed model allowed a valuable assessment of EC performance, constituting a valuable step in enhancing its implementation.

An agile heating and cooling energy demand model for residential buildings. Case study in a mediterranean city residential sector

Climate change will affect people's health, especially in cities. Hence, energy planning will play a key role in the development of sustainable and resilient cities. Urban building energy models facilitate energy planning as heating and cooling demand becomes known, and the consequences of different planning actions can be modelled. This research presents an agile heating and cooling demand model. It combines a European standard's methodology, geometric information of buildings collected from cadastral and altimetric datasets using GIS-based technologies, solar irradiation analysis and the degree-days method. The model is validated with various case studies and then applied to several buildings in different environments.The model shows the strong influence of the building's age (design and materials), the building surface-to-volume ratio on the energy demand and the importance of the solar irradiation analysis. Furthermore, the model can predict the effects of the temperature rise on energy demand and prioritise the buildings to be retrofitted. Indeed, one of the conclusions obtained from the model is that advanced retrofitting of 17% of the most energy demanding buildings would obtain a 50% decrease in thermal demand; if the percentage was 50%, an 85% reduction could be reached. In conclusion, the energy planning tool hereby presented is a useful tool to viably foresee the energy demand of residential buildings and districts and the effects of climate change on their energy demand, as well as the consequences of countermeasures like retrofitting.