Urban Building Energy Consumption Research Articles

Gaussian-based plug load profile prediction in non-residential buildings archetype

This paper presents an innovative approach to addressing the prevalent challenge of simulation uncertainty in urban building energy modeling (UBEM), focusing on accurately determining occupant-related input parameters. Traditional UBEM methods typically rely on standard schedules to create archetype models, which often fail to reflect the variability observed in real-world scenarios. To overcome this limitation, this research introduces a novel framework for generating electricity use profiles in institutional building archetypes across various climate zones. This framework integrates k-means clustering with Gaussian processes, effectively incorporating uncertainties into the prediction models. The evaluation of this stochastic model suggests that the methodology can give acceptable predictions on the electricity consumption of institutional buildings. The model demonstrates robust predictive capabilities, achieving a CVRMSE as low as 11% on weekdays and 8.7% on weekends, reflecting its strong predictive performance. However, its performance varies among different clusters and time periods, with specific clusters displaying more significant predictive inaccuracies at particular times. These results emphasize the importance of fine-tuning models and offer opportunities for improvement in predicting urban building energy consumption. This can be achieved by incorporating sensor-derived data to develop more detailed building profiles that include variable electricity usage patterns. This methodology has been integrated into a UBEM tool, enabling the generation of more realistic electricity load profiles.

Urban 3D building morphology and energy consumption: empirical evidence from 53 cities in China

The impact of building morphology on building energy consumption has been extensively studied. However, research on how 3D building morphology affects energy consumption at a macroscopic scale is lacking. In this study, we measured the mean building height (BH), mean building volume (BV), and mean European nearest neighbor distance (MENN) of the city to quantify the 3D building morphology. We then used a spatial regression model to analyze the quantitative impact of urban 3D building morphology on per capita electricity consumption (PCEC). Results indicate that at the macroscopic scale of the city, the BH and the MENN have a significant positive impact on the PCEC, while the BV has a significant negative impact on the PCEC. Moreover, the inclusion of the 3D building morphology greatly improves the model’s ability to explain building energy efficiency, surpassing the impact of traditional economic factors. Considering the 3D building morphology indicators together, buildings with a lower height, a larger volume, and a more compact 3D morphology have greater potential for energy savings and are more conducive to electricity conservation. This study offers valuable insights for the energy-efficient arrangement of buildings.

Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating

In the pursuit of China’s ambitious carbon neutrality goals, optimizing carbon-emission efficiency within the construction sector, a significant emitter, becomes critical. This study employs a super-Slacks-Based Measure (SBM) model and a Tobit regression model to analyze buildings’ heating-related carbon emissions across China, considering urban population density, electricity usage, and building energy consumption and the influencing factors that cause differences in carbon-emission efficiency difference. The results of this study show that the average building carbon-emission efficiency of 30 provinces in China is 0.789; carbon-emission efficiency is 0.89 in the south, higher than 0.69 in the north. After excluding centralized heating emissions, the value of buildings’ carbon-emission efficiency in the northern provinces increases by 0.01, of which the buildings’ carbon-emission efficiency in Jilin Province and Ningxia Hui Autonomous Region shows positive growth, respectively, by 0.12 and 0.17. In terms of influencing factors, there is a significant positive correlation between the scientific and technological levels, the regional economic scale, and carbon-emission efficiency; however, government intervention in the economy has a negative correlation with carbon-emission efficiency. Renewable energy utilization and green-policy adoption emerge as pivotal in enhancing efficiency. The contribution of this study is to underscore the necessity of fostering renewable energy, refining energy-consumption structures, and implementing green strategies to augment buildings’ heating-related carbon-emission efficiency.

Assessing the Energy Performance and Retrofit Potential of the 1980–1990s’ Residential Building Stock in China’s Jiangsu Province: A Simulation-Based Study

The building operation sector in China represents 22% of the national energy consumption and 22% of the carbon emission, of which urban residential buildings accounted for 24% in 2019. Such figures for the housing sector are projected to increase sharply in the near future, while China aims to peak CO2 emissions by 2030 and reach neutrality before 2060. To reduce the impacts of the urban housing sector and address the energy use and waste generated by large-scale demolition and reconstruction, the central government started promoting the energy retrofit of urban residential buildings, raising such policies to the national strategic level. Jiangsu Province is one of the most urbanised, with a rapid growth in the energy consumption of residential buildings. The Multi-Danyuan and Single-Danyuan Apartment built in 1980–1999 are the most representative residential types in its urban areas. While still adequate functionally, they were designed and built to low energy standards and show significant potential for energy retrofit. Nonetheless, their current performance and energy-saving potential are under-researched, while more detailed and reliable data would be critical to support retrofit design and policy making. This study investigates and characterises the typical use and energy performance of the two building types. Additionally, seven measures and six retrofit scenarios were identified based on the optimal energy reductions and regulations from selected countries. The simulations indicate that, without intervention, the energy consumption of the typical urban residential buildings can reach 122 kWh/m2 under the typical high-energy user scenario. By selecting a set of effective energy-saving measures, the operational energy use for heating and cooling can be reduced by up to 52.4%. Current local standards prove cost-efficient, although less effective in reducing energy use compared to international best practices, indicating potential improvements to the contribution of building retrofit towards achieving the national carbon reduction goals.

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error.

A framework for analyzing energy consumption in urban built-up areas based on single photonic radar and spatial big data

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide.

Urban Building Energy Modeling with Parameterized Geometry and Detailed Thermal Zones for Complex Building Types

Urban building energy modeling (UBEM) has attracted wide attention to the requirement for global carbon emission reduction. This paper presents a UBEM tool, AutoBPS-Param, to generate building energy models (BEMs) with parameterized geometry and detailed thermal zones, especially for complex building types, considering the shading effect from surrounding buildings simultaneously. Three building number scales and four scenarios were analyzed in the hotel-related buildings in Changsha, China. For the prototype modeling of Scenario 1, eighteen prototype building energy models for six building types in three vintages were created, and their simulation results were aggregated based on their representative floor areas. For AutoBPS-Param of Scenario 4, the method created one EnergyPlus (Version: 9.3.0) model for each building. The geometry of the prototype model was scaled and modified based on the target building’s length, width, and number of stories. The surrounding buildings were also added to the AutoBPS-Param simulation to better capture the urban dynamic impact. The results showed that the annual electricity and natural gas energy use intensity (EUI) of the pre-2005 HotelOffice prototype model was 172.25 and 140.45 kWh/m2. In contrast, with the AutoBPS-Param method, the annual electricity EUIs of 71 HotelOffice buildings constructed before 2005 ranged from 159.51 to 213.58 kWh/m2 with an average of 173.14 kWh/m2, and the annual gas EUIs ranged from 68.02 to 229.12 kWh/m2 with an average of 108.89 kWh/m2. The proposed method can better capture the diversity of urban building energy consumption.

Life cycle energy consumption prediction based on an extended system boundary with the Bi-LSTM model: An empirical study of China

Global warming and natural disasters have wreaked havoc on Earth these days. Energy conservation has been a priority under social development pressure, especially in the building sector. Energy consumption prediction serves as a vital tool for effective building energy management, guiding energy policy making and services distribution. However, despite the application of advanced technologies, achieving accurate energy consumption prediction still faces challenges due to potential influencing factors. This study aims to explore previously unidentified factors to enhance building energy consumption prediction accuracy. An extended life cycle energy boundary of buildings including embodied, operational, and mobile energies is proposed. To optimize the prediction processes, a hybrid Life Cycle Assessment (LCA) approach and a non-linear Bidirectional Long Short-Term Memory (Bi-LSTM) model are combined. An empirical study was conducted to predict the life cycle energy consumption of urban residential buildings in China by 2035. Results show: (1) life cycle energy consumption increased dramatically from 195.837 (±11.77) Mtce in 2000 to 1151.69 (±80.38) Mtce in 2022, but then achieved a slow decline to 796.998 ((±51.46)) Mtce by 2035; (2) there were four significant development stages in the residential field: rapid growth (2000–2010), sharp growth (2010–2018), slow growth (2018–2022), and stable fall (2022–2035); (3) mobile energy related to building location accounted for up to 28% of total energy consumption, and daily commuting is its largest emitter. However, calculation errors caused by diverse data sources and the fundamental model itself need to be addressed later. This study provides an original pathway of combining LCA with a non-linear model to improve energy consumption prediction and the completed primary historical data to help local governments’ energy decision-making.

Impacts of climate change and building energy efficiency improvement on city-scale building energy consumption

Facing global warming, the construction industry, as one of the main sources of carbon emissions, is facing challenges. Assessing the changes in urban building energy consumption and the potential for energy savings is crucial. This paper evaluated the impacts of climate change and building energy conservation measures (ECMs) on the energy consumption of 59,332 urban buildings in Changsha, China. Firstly, the weather files in 2050 and 2080 of Changsha City under the future low-emission scenario are obtained using the Weather Morph. Secondly, the benchmark model for large office buildings was established by OpenStudio-Standards. Then the models of urban buildings in Changsha were created to expand the research scope, including 22 types of buildings using AutoBPS. Finally, four ECMs and rooftop photovoltaic (PV) systems were added according to the energy saving standards, including envelope, heating, ventilation and air conditioning (HVAC) system, lighting, and equipment. The results indicated that the annual total primary source energy use intensity (EUI) of large office buildings in the base year, 2050 and 2080 are 365.93, 372.99, and 374.89 kWh/m2, respectively. And for the urban buildings is 47,862.22, 49,387.34, and 50,043.90 GWh, with an increase of 3.19% and 4.56%, respectively. Taking the base year as an example, when combined with four ECMs and PV systems, the EUI of large office buildings can be reduced to 211.61 kWh/m2, achieving an energy saving rate of 46.67%. And when all four ECMs were applied, the EUI for the urban building model could be reduced by 22,278.56 GWh (46.55%). These results can provide valuable insights for urban energy planning to address future climate change and reduce carbon emissions.

Energy Consumption Forecasting of Urban Residential Buildings in Cold Regions of China

Long-term predictions of the energy consumption of a building can be a reference in formulating energy conservation policies to achieve carbon neutrality. However, existing research on the prediction of energy consumption of urban buildings mainly adopts top-down or bottom-up single models that do not consider the coupling effect of macro and micro factors. Therefore, a coupled top-down and bottom-up prediction model has been proposed in this paper. The applicability of the proposed method was investigated based on residential buildings in Beijing, China. First, based on a top-down methodology, the energy consumption data of residential buildings in Beijing were investigated using an urban statistical yearbook, and the energy consumption of residential buildings under different energy-saving policies was analyzed. Second, micro factors, such as envelope parameters, personnel behavior, air conditioning, and electrical usage of typical residential buildings, were investigated. Subsequently, a simulation model of residential building energy consumption was constructed according to the survey data. The actual and simulated values for 2017 were 0.264 GJ/m2 and 0.252 GJ/m2, respectively. Finally, pessimistic and optimistic scenarios are proposed using the Human Impact, Population, Affluence, Technology (IPAT) model. The energy consumption of residential buildings in Beijing for the next decade was predicted under different scenarios by adopting the gray prediction method and multiple regression analysis, which was verified using the back-propagation neural network algorithm. In the baseline scenario, the projected 2021 heating energy consumption value was 13.4 kgce/m2 with a relative error of 6.52%.

Study on Building Energy Saving Effect and Thermal Response of Roof Greening in Extremely Hot Weather

As the main living space and carrier of the population, cities and buildings have become more seriously affected by changes in global environment in recent years. The deteriorating living environment has brought about the quality of survival of the population, the government’s administrative management, and the sustainable development of the environment. This article has found that green roofs on urban environmental management and building energy consumption have found that green roofs can have an important impact on urban environmental management. In extreme weather events, green roof has a maximum cooling effect of 26℃ in the daytime, and its warming effect at night has been suppressed to a certain extent; the heat preservation time of the green roof can be up to 17 hours after the superposition of precipitation weather incidents, and its heat preservation effect is positively related to the temperature in the daytime. The green roof can reduce the energy consumption of the building by 106W/ ㎡/d in the extreme weather incident, while its energy -saving effect is reduced to 45W/m2/d after superimposing the precipitation meteorological incident.

Multi-Agent-Based Model for the Urban Macro-Level Impact Factors of Building Energy Consumption on Different Types of Land

Urban researchers pay more and more attention to building energy consumption from different perspectives to obtain the results of urban overall energy conservation. The research at the micro level has yielded abundant accomplishments, but the macro-level research that can support urban planning decision making is still in the exploration stage. In this study, a multi-agent-based model, including the main panel, building agent, resident agent, and household appliance agent, is established by using Anylogic software. The model integrates Harbin urban macro-level impact factors of building energy consumption by designing and linking three sub-models: an urban morphology sub-model, climate sub-model, and energy use behavior sub-model. In the end, this study explored the building energy-saving potential of different types of land under the impact of variable factors through urban morphology and climate simulation scenarios and discussed the related energy-saving strategies. Findings and suggestions include: (1) The impact of urban morphology on overall urban building energy consumption is mainly reflected in residential and commercial land. The land development intensity (building density, floor area ratio, and building height) control and the coordination of land type layout and configuration can help to reduce the building energy consumption. (2) The energy-saving potential of residential land is more evident under climate impact, and ecological means should be used to adjust the climate to reduce the building energy consumption on different lands. (3) From the methodology perspective, this model can well realize the integration of multiple impact factors at the macro-level of the city and the dynamic simulation of energy consumption. The research results are expected to provide quantitative support for creating a sustainable built environment for the city.

Semi-supervised learning based framework for urban level building electricity consumption prediction

The spatial feature of building energy consumption in a city is essential for urban level energy planning and policy making. With the increasing availability of urban level building energy benchmarking datasets, machine learning has shown a powerful capability of making data-driven predictions on urban level building energy consumption. However, the building energy benchmarking datasets usually only cover large buildings, which are not sufficient representations of all buildings in a city. Besides building energy benchmarking datasets, many other urban level open datasets are also valuable to building energy prediction, but they do not contain building energy use data, in other words, they are unlabeled. This study proposes a novel framework based on semi-supervised learning to make effective use of the unlabeled datasets to develop more generic urban level data-driven building energy prediction models, and energy mapping with higher space resolution. The framework consists of preliminary labeling, selection of pseudo labeled samples and predictive modelling. Several machine learning algorithms are proposed and compared for generating pseudo labels of building electricity consumption for unlabeled datasets of small and medium-sized buildings. A selection process consisting of convergence testing and screening is designed to select pseudo labeled samples with high credibility to enlarge the labeled dataset. A novel two-level performance evaluation method is proposed to evaluate the performance of the framework at both urban level and district level to enhance the spatial resolution of the predictions. The framework is implemented to model and map the individual electricity consumptions of all buildings in two years in the districts of New York City using multiple open datasets. The results show significant improvement in terms of prediction accuracy at both levels. In addition, the applicability of the model to various buildings in the city is remarkably enhanced.

The impact of urban vegetation morphology on urban building energy consumption during summer and winter seasons in Nanjing, China

Studies have shown that urban vegetation can be an effective strategy for reducing energy consumption in urban buildings by regulating the microclimate and shading solar radiation on building surfaces. However, an understanding of the potential energy savings of vegetation morphological planning at the urban scale is still lacking, particularly regarding the quantitative correlation between urban vegetation morphology and its impact on urban building energy use. The morphology of the metropolitan area in Nanjing, a typical hot summer/cold winter city in eastern China, was statistically analyzed, and 40 urban building-vegetation morphological prototypes were extracted. Using the proposed co-simulation technique for urban microclimate and urban building energy, the summer and winter building energy consumption of the prototypes were simulated. A quantitative analysis was conducted on the relationship between urban vegetation morphology indexes and building energy consumption. The results indicate that strategically planned urban vegetation morphology can significantly reduce urban building energy consumption. In the summer, vegetation close to the geometric center of the site, uniformly distributed and highly mixed with buildings, can significantly reduce the building energy consumption; in the winter, the opposite is true. The presented findings provide designers and planners with strategies for incorporating urban vegetation morphology design into the construction of energy efficient cities.

Optimization of COVID-19 prevention and control with low building energy consumption

COVID-19 is a global threat. Non-pharmaceutical interventions were commonly adopted for COVID-19 prevention and control. However, during stable periods of the pandemic, energy would be inevitably wasted if all interventions were implemented. The study aims to reduce the building energy consumption when meet the demands of epidemic prevention and control under the stable period of COVID-19. Based on the improved Wells-Riley model considering dynamic quanta generation and pulmonary ventilation rate, we established the infection risk - equivalent fresh air volume - energy consumption model to analyze the infection risk and building energy consumption during different seasons and optimized the urban building energy consumption according to the spatio-temporal population distribution. Shopping centers and restaurants contributed the most in urban energy consumption, and if they are closed during the pandemic, the total infection risk would be reduced by 25%–40% and 15%–25% respectively and the urban energy consumption would be reduced by 30%–40% and 13%–20% respectively. If people wore masks in all public indoor environments (exclude restaurants and KTV), the infection risk could be reduced by 60%–70% and the energy consumption could be reduced by 20%–60%. Gyms pose the highest risk for COVID-19 transmission. If the energy consumption kept the same with the current value, after the optimization, infection risk in winter, summer and the transition season could be reduced by 65%, 53% and 60%, respectively. After the optimization, under the condition of Rt < 1, the energy consumption in winter, summer, and the transition season could be reduced by 72%, 64%, and 68% respectively.

A practical methodology for predicting electricity consumption of urban residential buildings

AbstractIt is a challenging task for determining the emissions and energy‐saving options for counties and countries. In order to overcome this issue, two new concepts are introduced in this study. These are residential base electricity consumption and residential base natural gas consumption. A new model that estimates the total energy consumption of urban residential buildings from the available electricity consumption data is developed. The model employs heating degree days (HDD) and cooling degree days (CDD) parameters to predict the energy consumption for heating and cooling purposes. A month with zero or almost zero values of both HDD and CDD is considered as “base month.” These months when there is neither heating nor cooling, plays a key role in this study.The proposed model is applied to a county with a population of over 62,000 located in Southern Turkey, and the total electricity consumption is calculated with 1.24% accuracy. The validity and accuracy of the model are tested by theoretical electricity calculations, coal, and wood consumption. Accordingly, while annual electrical heating is equal to 2618 kWh/year for an average house of 96 m2, for the coal and wood burning households, the annual energy consumption values are 2190 and 2616 kWh/year, respectively.

An evidence-based framework for designing urban green infrastructure morphology to reduce urban building energy use in a hot-humid climate

Urban green infrastructures (UGIs) play an essential role in reducing urban building energy consumption by cooling surrounding areas and shielding buildings from direct solar radiation heat gain. Although the energy-saving effect of UGIs is widely agreed upon, there is a lack of systematic knowledge about how UGI morphology affects urban building energy consumption. Consequently, an effective method to design UGI morphology to reduce urban building energy consumption in urban design is not available. A grading system was proposed to evaluate the strength of evidence for the findings and conclusions in studies that pertain to the cooling and energy-saving effects of UGI. The evidence-based literature review found that strategically designing the morphology of UGI, including its size, shape, location, connectivity, spatial pattern, and configuration, can effectively reduce the air temperature surrounding the UGI area and building energy demand. This study formulated a morphological design framework for UGI as part of the urban design process to reduce urban building energy consumption. Based on the evidence-based answers to the key questions in the framework, design strategies are provided as visual charts and technical principles. The study includes an urban design case in Nanjing, China, which applied this framework to achieve the goal of urban energy savings.

Spatio-temporal heterogeneity analysis of energy use in residential buildings

Conventional building energy dynamic analysis relies on climatic and geographical statistics of buildings. A large number of studies focus on investigating the major influencing factors and the energy use with engineering methods and statistical methods to predict buildings' energy use. However, due to the lack of temporal statistics when analyzing buildings' energy use intensity (EUI), very few studies concerned the energy consumption from spatial and temporal heterogeneity simultaneously. This study extends the traditional regression model by introducing the temporal variations into the geographically weighted regression (GWR) model and developed a geographically and temporally weighted regression (GTWR) model. A case study, which is based on four years’ urban building energy consumption (from 2015 to 2018) in the city of Seattle, is used to validate the performance of the proposed regression model. Meanwhile, the ordinary least square (OLS), GWR, and temporally weighted regression (TWR) models are developed for making cross-comparison of the goodness-of-fit. The results show the GTWR and TWR models reduce the mean absolute error by 13% and 7%, respectively, relative to the GWR model. Moreover, the GTWR model has a substantial improvement of goodness-of-fit, which can be referred from the value of R2 increasing by 49% in OLS, 16% in GWR, and 6% in TWR. Two driving forces, the number of floors (NF) and energy stars (ES), are identified to have the most significant coefficients in evaluating the site EUI. The novelty of this research lies in achieving better reliability and accuracy in building energy modeling by GTWR, which provides an assisting tool for government and local authorities to make the master plan and sustainability strategies for the new buildings in the city.

Investigation on household energy consumption of urban residential buildings in major cities of Indonesia during COVID-19 pandemic

The implementation of the movement control order (MCO) to curb the spread of the 2019 novel corona virus disease (COVID-19) have influenced household energy consumption patterns around the world. This study aims to investigate household energy consumption of urban residential buildings in major cities of Indonesia during COVID-19 pandemic. Three representative major cities of Indonesia were selected to investigate detailed information about household appliances and gas consumption through face-to-face interviews in 2021 (n = 311). The factors affecting household energy consumption were investigated by multiple regression analysis. The results showed that, overall, the average annual energy consumption of all samples during pandemic was approximately 23.5 GJ, 3.0 GJ larger than before pandemic. The difference was primarily attributed to the use of air conditioning and cooking. The statistical analysis clearly indicated that the increase in household income (low-to high-cost houses), which would increase household size and number of appliances including air conditioning, thus increased total household energy consumption. We recommended the following potential energy-saving strategies for urban houses in Indonesia: (a) control the number of family members, (b) use more energy efficiency standards for electrical appliances and (c) encourage energy-saving lifestyles, particularly to younger adults by adopting passive cooling techniques (window opening) whereever possible.

Review of Urban Heat Island and Building Energy Modeling Approaches

Abstract In this paper, a review of the current literature in modeling urban heat island (UHI) phenomena including its main causes and effects is summarized. The main goal of the review is to assess the current modeling capabilities to accurately determine the impacts of UHI on outdoor comfort levels and urban building energy demands. In particular, the analysis techniques and modeling approaches are overviewed to estimate the mutual thermal interactions between urban atmosphere and buildings. In addition, the applications and the limitations of various modeling methods are discussed to predict outdoor thermal comfort and urban building energy consumption. The specific capabilities of the reviewed modeling approaches are highlighted to assess the effectiveness of mitigation strategies of the UHI effects. As part of the review analysis, recommendations are outlined to improve current modeling approaches to predict more accurately the impacts of UHI phenomena on urban building energy performance.