Energy Use In Residential Buildings Research Articles

Energy use in residential buildings for sustainable development: The fifth Solar Decathlon Europe revelations

This research focuses on achieving sustainable development in residential buildings with energy use. Under the influence of the energy crisis and related problems, research on residential buildings for less energy use has great potential. The literature review, according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses, and including VOSviewer analysis, shows the research is increasing and meaningful. Solar Decathlon buildings are used as the main objects in this research. The fifth Solar Decathlon Europe energy use technologies are examined through onsite investigation and online searching. The Analytic Hierarchy Process method for multi-criteria decision analysis is used for sustainability assessment. Moreover, the Ladybug and ClimateStudio plugins simulated respectively the annual solar radiation and the best angle for receiving it. The main findings show that 34 kinds of technologies used in these buildings can be classified into two categories in three directions. Passive technologies should be applied and prioritized, but generating renewable energy is also important. Some infrequently used technologies are not insignificant. The research shows that the combination of technologies decides sustainability performance, but the quantity used does not. Furthermore, energy use also needs to be balanced and coordinated in combination with architectural aesthetics. This research on energy use in residential buildings is beneficial for achieving sustainable development.

The impact of thermal comfort requirements on household heating energy consumption

Urban residential buildings consume a large amount of energy. In most homes, heating runs on a set schedule. Therefore, the installation continues to operate irrespective of whether the residents' thermal comfort requirements have been fulfilled. This process results in considerable wasted energy and CO2 emissions. To address this issue, in this study, data on heating energy use and carbon emissions from 2302 field surveys in the core, main, and suburban areas were quantified. Furthermore, the impact of different thermal comfort requirements on the carbon emissions of self-heating households was explored based on the PMV-PDD model. The study revealed that (1) Beijing's per capita household energy consumption is 613.80 kgce·a−1, and heating accounts for a significant portion of energy consumption and carbon emissions; (2) carbon emissions from household energy consumption differ among districts in Beijing, with suburban districts emitting approximately 1.6 times more than main urban areas; (3) suburbs have less need for thermal comfort, but they emit more carbon than other areas with similar heating costs due to limited heating infrastructure, poor access and the high cost of clean energy; and (4) warmer thermal comfort requirements elevate energy consumption and thus positively impact heating costs and carbon emissions, especially in the main urban area. The energy waste caused by heating is quantified, and the impact of thermal comfort requirements on carbon emissions is clarified. The findings are informative for optimizing energy use in residential buildings and proposing targeted low-carbon heating policies.

Examining behavioral aspect of urban context effect on residential building energy use in Seoul – linking urban walkability to energy

Recent studies show that urban contextual form can influence building energy use intensity (EUI). While many studies have focused on the thermal properties of buildings and their environment, human behavior, a key determinant of EUI, is also influenced by the contextual form. Urban walkability, a key measure of urban vitality, can encourage outdoor activity and thus alter occupant behavior and outsource energy demand. This study examines the behavioral aspect of the context effect on residential building EUI in Seoul. Summer electricity, winter gas, annual electricity, and annual gas EUIs are individually analyzed. Two sets of analyses are executed for each energy type. First, walkability is used as a simple explanatory variable in an ordinary least squares regression model. Second, walkability is used as a moderator for the socioeconomic parameters of occupant density, occupant age, and real estate price. The results suggest that an increase in walkability can significantly reduce residential electricity EUI, and walkability can modify the influence of occupant density and occupant age on electricity EUI. However, walkability is only a marginally significant predictor of residential gas EUI, and all interactions with socioeconomic variables are insignificant. These findings highlight the importance of walkability as a means of linking urban form and building energy.

A Hyper Parametrized Deep Learning Model for Analyzing Heating and Cooling Loads in Energy Efficient Buildings

The huge increase in energy consumption in recent decades, has made it cumbersome to anticipate energy usage in the residential sector. However, despite substantial advancements in computation and simulation, the modelling of residential building energy use is still in need of improvement for efficient and reliable solutions. To this end, the overarching objective of this research study is to construct a self-adaptive model (HBO-DL) for predicting the amounts of heating and cooling loads in residential buildings. The developed HBO-DL model is envisioned on coupling Bayesian optimization with deep learning neural network. Five statistical metrics of mean absolute percentage error (MAPE), root mean squared error (RMSE), root mean squared logarithmic error (RMSLE), mean absolute error (MAE) and normalized root mean squared error (NRMSE), are leveraged to measure and test the accuracies of the developed HBO-DL. Analytical results explicated that the developed HBO-DL model can endorse informed decision-making and foster energy conservation in built environment.

A statistical quantitative analysis of the correlations between socio-demographic characteristics and household occupancy patterns in residential buildings in China

Occupancy patterns that describe occupant presence or absence can result in a significant impact in energy use of residential buildings; thus, they are critical to the energy modeling of residential buildings. Occupancy patterns currently embedded in building energy simulation tools or recommended in the standards are uniform, ignoring that different occupancy patterns exist for households with different socio-demographic characteristics. Consequently, this study objective is to extract the typical occupancy patterns of households and quantify the correlations between household occupancy patterns and socio-demographic characteristics based on statistical approaches. Clustering analysis was used to identify representative types of occupancy patterns based on the occupancy profiles of daily occupant presence data. Then, correlations between occupancy patterns and household characteristics were identified by investigating three statistical methods: nonparametric analysis, decision trees, and association rule mining. To illustrate the quantitative analysis, datasets collected by a large-scale questionnaire survey in two Chinese cities were used for the case study. The results revealed that socio-demographic characteristics, such as occupant age, employment status and household composition, have statistically significantly associated with occupancy patterns. The statistical quantitative analysis can be used to estimate household occupancy patterns to further reduce the performance gap between the simulation and actual energy consumption of residential buildings.

Scenario-based analysis of future life cycle energy trajectories in residential buildings-A case study of inner Melbourne

The residential building sector contributes significantly to energy use in Australia. However, future energy use in residential buildings is seldom evaluated from a life cycle perspective, and such analysis rarely provides details about potential energy savings by specific strategies. This study develops a scenario-based bottom-up approach to predict the future life cycle energy (LCE) trajectories in residential buildings to 2050 with different energy-saving strategies. The proposed approach is used to investigate the impacts of strategies on the future LCE of residential buildings in detail in four scenarios: high carbon (HC), business-as-usual (BAU), accelerated policy (AP), and net-zero emissions (NZE), for a case study of Inner Melbourne Cities. The results demonstrate the potential upward trajectory of operational energy (OE) in the HC scenario and a downward trend in the NZE scenario. The analysis also reveals how stringent energy-efficient strategies and increasing renewable energy supply could lead to future net-zero operational emissions in the NZE scenario by 2050. The most effective strategies for decarbonising the residential building sector include improving the building envelope efficiency, upgrading heating and hot water systems, and using renewable energy. Furthermore, embodied energy (EE) will contribute more to LCE after 2045 in AP and NZE scenarios with the decrease of OE. EE in the NZE scenario is higher than that in the other three scenarios, as energy-efficient dwellings usually use more insulation materials. Therefore, EE reduction is critical for achieving net-zero life cycle emissions in the residential building sector. Building stakeholders should synergistically promote demand-side, supply-side, and EE reduction strategies for achieving net-zero life cycle emissions. A detailed analysis of the future LCE development with different strategies shows the benefits of the proposed approach, which supports informed decision-making towards the 2050 net-zero emissions target.

Large-scale statistical analysis and modelling of real and regulatory total energy use in existing single-family houses in Flanders

ABSTRACT Large-scale statistical studies on the gap between the real and regulatory energy use in residential buildings in Europe have shown that the regulatory calculation overestimated the real energy use, inflated true energy savings and undermined national energy policy making. Using data from 122,680 Flemish existing single-family houses, this research builds further on existing studies by contributing results for Flanders. The study also examines to what extent available aggregated variables explain the real annual total building energy use using statistical linear models and addresses the problem of multicollinearity and the importance of bootstrapped confidence intervals for model quality control. The overestimation of the real total energy use (and potential energy savings) by the Flemish regulatory method is exceedingly large compared to studies from other EU countries. The Flemish labels prove very poor indicators of the real energy use. Statistical linear models explain up to 46.6% of all variability and indicate that a significant extent of multicollinearity had to be corrected. Half of the variability has been left unexplained and has to be attributed to variables that were not available and the fact that the data were insufficiently accurate. Future analysis will explore whether more complex models identify more evidence.

Relationships between building characteristics, urban form and building energy use in different local climate zone contexts: An empirical study in Seoul

The influence of building context on the relationship between building and its energy use has been attracting attention. However, most studies simply defined building contextual areas as within an arbitrary distance and measured their form with commonly used planning and design indicators. Such an approach often suffers from the lack of justifications on the context area size, confounding effects between indicators, and weak connection between those indicators and urban energy policy. This study uses the local climate zone (LCZ) as buildings’ thermal context area, and investigates the relationship between building characteristics, urban form, and residential building energy use in different LCZ contexts in Seoul, South Korea. A linear mixed model is employed to examine this relationship. Results show that LCZs as different contextual forms not only significantly influence building energy, but also impact the effects of building characteristics on building energy use. The results are further compared to another model with zoning classes, a widely used development control tool in planning, as contextual areas, and find the latter has much smaller influences. The findings suggest a promising application of the LCZ framework in understanding the form-energy relationship in complex urban environments and in energy-oriented urban planning.

Mixed-Mode Ventilation in HVAC System for Energy and Economic Benefits in Residential Buildings

In the U.S., approximately 47% of the total electricity use comes from residential buildings. Most of the residential buildings use HVAC system to ventilate, cool, or heat the indoor environmental spaces mechanically, rather than using natural outdoor air in transition seasons, even though the outdoor environmental conditions are favorable for indoor thermal comfort. In this case, an HVAC system using mixed-mode ventilation with an appropriate ratio of using indoor air and outdoor air could decrease the energy use in residential buildings. This research uses high-granular HVAC electricity use data with indoor thermostat data and outdoor weather data from residential buildings in Austin, Texas, to evaluate the benefits of energy and economics when using HVAC mixed-mode ventilation in spring and fall transition seasons. The results demonstrate that the household owners could save approximately 150.79 kWh of total HVAC energy use and 24.41% of HVAC cost in spring transition months (April/May), and similarly, 143.86 kWh of energy use and 27.2% cost savings in fall transition months (October/November). The results could support further study to use automatically operated windows for natural ventilation to reduce energy use in residential buildings toward sustainable development.

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.

Characterizing the theory of energy transition in Luxembourg—Part three—In the residential sector

AbstractIncreasing consciousness to address future concerns is a challenging subject, noticed considerably by policymakers and investors. Investigation of energy use in residential buildings in terms of quantity and sources of energy was the main focus of this study. Indeed, the paper was aimed to study the role of contextual factors on two points: the energy demand and the spreading of renewable technologies in the residential sector. It was theory‐driven research to highlight some future concerns. The first theory was to reveal how the growth of single‐family houses versus multi‐family houses could affect the growth of energy consumption and the application of renewable technologies. The second theory was built to highlight that spreading energy measures to increase efficiency or introducing a new energy carrier should be embedded in the context of new construction activities or in a template of renovation projects. The third theory was to emphasize the priority of thinking about managing the changes in residential buildings because of the lasting impact of the decision about the type of building. Finally, the purpose of the ongoing transition was argued to highlight that the growth of renewable technologies has not been necessarily aligned with getting away from fossil fuels.

县域城镇空间形态与居住建筑能耗关联性的连续尺度研究——以浙江长兴、福建连江为例

PDF HTML阅读 XML下载 导出引用 引用提醒 县域城镇空间形态与居住建筑能耗关联性的连续尺度研究——以浙江长兴、福建连江为例 DOI: 10.5846/stxb202103280807 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: “十三五”国家重点研发计划课题"（2018YFC0704705）；福建省中青年教师教育科研项目（JAT190034）；福州大学引进人才科研启动项目（511005-GXRC-21024） Correlation between urban spatial form and residential building energy use of towns on a continuous scale: A case of Changxing, Zhejiang and Lianjiang County, Fujian Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:居住建筑能耗受建筑周边空间形态的直接影响，确定空间形态低碳效应及其最佳尺度将有助于实现县域城镇低碳转型。以浙江省长兴县、福建省连江县为例，共计选取49个具有代表性的居住建筑样本，采用GIS分析与数理统计相结合的方式，在居住建筑1800m半径范围内，以200m为间隔建立缓冲区，开展全年、最冷月份能耗期及最热月份能耗期建筑能耗与周边空间形态关联性的连续尺度研究。结果表明：①道路密度、开发密度、容积率均与建筑能耗呈正相关，但各指标对应的能耗时期、尺度范围有较大差异。②水面率、土地利用混合度是与建筑能耗相关的共性指标，但在不同地区的正负效应及尺度范围不同；③影响浙闽地区县域城镇最热月份能耗期能耗的关键形态因子为1000m-道路密度、800m-水面率、1600m-开放空间率；影响连江最冷月份能耗期能耗的关键形态因子为800m-水面率。④就控碳单元设置而言，长兴县可重点关注1000-1200m半径范围，连江县可重点关注800-1000m、1400m半径范围。⑤浙闽地区县域城镇特性指标为开放空间率，与最热月份能耗期建筑能呈负相关；长兴县特性指标为容积率、水岸密度，分别与全年、最冷月份能耗期及最热月份能耗期建筑能耗呈正、负相关。据此提出3个低碳城镇规划建议，为实现我国浙闽地区县域城镇低碳转型、居住建筑节能减排提供理论依据。 Abstract:Since the energy consumption of residential buildings is directly affected by the spatial form surrounding the buildings, determining the low-carbon effect of the spatial shape and its optimal scale contributes to realizing the low-carbon transformation of towns and township.Taking Changxing County of Zhejiang Province and Lianjiang County of Fujian Province for example, 49 representative samples of residential buildings are selected in total and included in this research. Based on both GIS analysis and the method of mathematical statistics, with buffer zones built at the interval of 200m within a radius of 1800m around the residential buildings, a continuous-scale research is conducted on the correlation between building energy consumption and the spatial pattern around throughout the year, in the consumption period of the coldest month and in the consumption period of the hottest month.The results showed that:①road density, development density, and floor area ratio were all positively correlated with building energy consumption, but the energy consumption period and scale range corresponding to each index were quite different. ②Water surface ratio and land use mixing degree were common indicators related to building energy consumption, but their positive and negative effects and scale ranges vary in different regions. ③The key form factors that affect the energy consumption of towns in Zhejiang and Fujian during the monthly energy consumption period with the highest temperature were 1000 m- road density, 800 m- water surface ratio, and 1600 m- open space rate; The key form factor that affects the energy consumption during the energy consumption period of the lowest temperature month in Lianjiang is 800 m-water surface ratio. ④In the selection of carbon control unit, it is necessary for Changxing County to focus on the radius range of 1000 -1200 m, and Lianjiang County to focus on the radius range of 800 -1000 m and 1400 m.⑤ Characteristic indexes of towns in counties in Zhejiang and Fujian were the rate of open space, which was negatively correlated to building energy consumption in the consumption period of the hottest month; characteristic indexes of Changxing County are floor area ratio and waterfront density, which were positively and negatively correlated to building energy consumption throughout the year, in the consumption period of the coldest month and in the consumption period of the hottest month respectively. On such a basis, 3 proposals concerning low carbon urban planning are made herein, which may serve as a theoretical basis for the low carbon transition of county towns and energy saving and emission reduction in residential buildings in Zhejiang Province and Fujian Province. 参考文献 相似文献 引证文献

Statistical analysis of greenhouse gas emissions of South Korean residential buildings

This study analyzed the status of greenhouse gas (GHG) emissions of residential buildings in South Korea from 2015 to 2019, based on energy consumption data for all residential buildings in the country from the Korea National Building Energy Database. The analysis revealed that new residential buildings had a lower GHG emission intensity than old residential buildings and that strengthening the Building Energy Conservation Code (BECC) contributes to the reduction of GHG emissions from heating energy use in residential buildings. The GHG emission intensity for heating energy use of apartments to which the latest standard (0.21 W/m2·K U-value of exterior walls) was applied was 35.8% lower than that of apartments to which the insulation standard was not applied. However, despite the strengthening of the BECC, the GHG emission intensity for baseload and cooling energy use of apartments increased. This means that the BECC does not contribute to reducing GHG emissions from baseload and cooling energy use. Moreover, as the number of residential buildings continues to increase, their consolidated GHG emissions also increase. Since heating energy consumption is significantly influenced by the weather, GHG emissions from heating energy use can vary depending on the weather. For instance, GHG emissions from heating energy use can increase in extremely cold winter despite the strengthening of the BECC. Therefore, a policy is needed for strengthening the BECC as well as for enhancing building occupants' understanding of GHG emissions reduction in buildings and for promoting the occupants’ behavior change.

Adaptive opaque façades and their potential to reduce thermal energy use in residential buildings: A simulation-based evaluation

Adaptive façades are a promising choice to achieve comfortable low-energy buildings. Their effective performance is highly dependent on the local boundary conditions of each application and on the way the dynamic properties are controlled. The evaluation of whole building performance through building performance simulation can be useful to understand the potential of different Adaptive opaque façades (AOF) in a specific context. This paper evaluates through dynamic simulations promising design solutions of AOF for a residential building use in six different climates. It quantifies the total delivered thermal energy of 15 typologies of AOFs which consist of alternative adaptation strategies: (i) variation of solar absorptance of the cladding, (ii) variation of the convective heat transfer of air cavities and (iii) adaptive insulation strategies. For the first time, it also quantifies the performance of AOF which combine more than one adaptation strategy. The results show that the variation of the heat transfer by means of Adaptive Insulation components has the most significant impact on the reduction of the thermal energy use. The variation of the solar absorptance has also a significant positive impact when reducing heating consumption, but only if this adaptation strategy is actively controlled and combined with Adaptive Insulation components.

Impacts of COVID-19 on residential building energy use and performance

Following the declaration of the COVID-19 pandemic and the rise in cases across the United States, the typical daily routines of millions were disrupted as the country attempted to control the spread of the virus. As a result, homes became makeshift offices, classrooms, restaurants, and entertainment centers. With these changes in how residential buildings are used, surveys and grid-level studies have been conducted to understand how energy use has shifted due to the pandemic. However, there are limited efforts that review the impact of energy use at the household level. In this study, high-resolution, disaggregated data is analyzed to measure the shifts in electricity use related to HVAC loads, non-HVAC loads, and whole-home loads in a comparison of 225 housing units over the years of 2018–2020. Key findings from the analyses indicated increased electricity use during periods that occupants would usually be away from home. The most percent increases in non-HVAC residential loads occurred between 10 a.m. and 5 p.m.; HVAC loads increasing in total daily consumption compared to the same average daily temperatures of previous years. Additionally, dividing the data by household income, the lowest income and higher income households experienced the larger increases in consumption, while the middle income groups experience smaller shifts.

Forecasting Time-Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency.

Building energy efficiency is important because buildings consume a significant energy amount. The study proposed additive artificial neural networks (AANNs) for predicting energy use in residential buildings. A dataset in hourly resolution was used to evaluate the AANNs model, which was collected from a residential building with a solar photovoltaic system. The proposed AANNs model achieved good predictive accuracy with 14.04% in mean absolute percentage error (MAPE) and 111.98 Watt-hour in the mean absolute error (MAE). Compared to the support vector regression (SVR), the AANNs model can significantly improve the accuracy which was 103.75% in MAPE. Compared to the ANNs model, accuracy improvement percentage by the AANNs model was 4.6% in MAPE. The AANNs model was the most effective forecasting model among the investigated models in predicting energy consumption, which provides building managers with a useful tool to improve energy efficiency in buildings.

Rethinking the concept of building energy rating system in Australia: a pathway to life-cycle net-zero energy building design

Over the last decades, Australia has taken several measures to tackle the increasing trend of energy use in residential buildings. Recently, the Trajectory for Low Energy Buildings has been endorsed aiming to reduce energy usage in residential buildings. However, the primary focus of this trajectory is on decreasing operational energy without considering the embodied energy of the building and systems. This paper aims to address one primary question; ‘can the continued exclusion of embodied energy from the energy efficiency regulations effectively lead to reducing energy consumption in Australian residential buildings?’. The findings indicate that embodied energy becomes a dominating factor as buildings’ thermal performances increase according to the Australian energy efficiency regulations. In transitioning from a standard 6.0-star building to a highly energy-efficient 8.7-star building, the proportion of embodied energy significantly increases from 20–40% to 50–75%. This study recommends establishing minimum mandatory requirements for buildings’ embodied performance.

Predicting U.S. Residential Building Energy Use and Indoor Pollutant Exposures in the Mid-21st Century.

The extent to which climate change and other factors will influence building energy use and population exposures to indoor pollutants is not well understood. Here, we develop and apply nationally representative residential energy and indoor pollutant model sets to estimate energy use, indoor pollutant concentrations, and associated chronic health outcomes across the U.S. residential building stock in the mid-21st century. The models incorporate expected changes in meteorological and ambient air quality conditions associated with IPCC RCP 8.5 and assumptions for changes in housing characteristics and population movements while keeping other less predictable factors constant. Site and source energy consumption for residential space-conditioning are predicted to decrease by ∼37-43 and ∼20-31%, respectively, in the 2050s compared to those in a 2010s reference scenario. Population-average indoor concentrations of pollutants of ambient origin are expected to decrease, except for O3. Holding indoor emission factors constant, indoor concentrations of pollutants with intermittent indoor sources are expected to decrease by <5% (PM2.5) to >30% (NO2); indoor concentrations of pollutants with persistent indoor sources (e.g., volatile organic compounds (VOCs)) are predicted to increase by ∼15-45%. We estimate negligible changes in disability-adjusted life-years (DALYs) lost associated with residential indoor pollutant exposures, well within uncertainty, although the attribution among pollutants is predicted to vary.

Heating and cooling energy consumption prediction model for high-rise apartment buildings considering design parameters

As lots of the total energy was used by buildings, the number of residential buildings has dramatically increased in South Korea. Thus, it is imperative to pay more attention to energy consumption by residential buildings. In addition, it is important to predict energy consumption in residential buildings accurately. While several studies have currently focused on the data-driven method to predict energy consumption, it requires much information for multivariate data. The present study developed a predictive model for energy consumption for residential buildings by using the statistical method. Using the response surface methodology, the relationships between design factors, and heating and cooling energy use in residential buildings were outlined. The response values were calculated by using the simplified geometries in the energy simulation tool. The relationship has confirmed the dependencies of the energy consumption on various design variables of envelope systems in residential buildings in that the predictive model for the heating and cooling energy consumption was developed. The developed model was compared with the data obtained in the apartment buildings in two cities in South Korea. As a result, a coefficient of variation of the root mean squared error (Cv (RMSE)) was ranged from - 0.3% to 15% and all the comparisons were within the acceptable range. Moreover, heating and cooling energy consumption was predicted by varying the values of design variables such as thermal transmittance, solar heat gain coefficient (SHGC), and air infiltration rates. Among the variables, the largest heating energy was required as with the increase in the air infiltration rates, while the largest cooling energy was consumed as the SHGC was increased for both apartment buildings. Moreover, the increase in thermal transmittance values resulted in about 27% - 29% of the increase in the heating energy consumption. For cooling, 8% to 26% of the energy consumption was decreased when the thermal transmittance was increased. As can be shown, the developed model can offer a rapid energy prediction for apartment buildings with simple information on design variables. Furthermore, it can easily figure out the most important design factor to make a more energy-efficient residential building design.

What leads to variations in the results of life-cycle energy assessment? An evidence-based framework for residential buildings

• Sources of variations in LCEAs were investigated • 157 cases of residential buildings that were analysed by LCEA were examined • Four major categories of parameters affecting LCEA results were identified • A conceptual framework was proposed to standardize LCEA in residential buildings Residential buildings are one of the major contributors to climate change due to their significant impacts on global energy consumption. Hence, most countries have introduced regulations to minimize energy use in residential buildings. To date, the focus of these regulations has mainly been on operational energy while excluding embodied energy. In recent years, extensive studies have highlighted the necessity of minimizing both embodied energy and operational energy by applying the life-cycle energy assessment (LCEA) approach. However, the absence of a standardized framework and calculation methodology for the analysis of embodied energy has reportedly led to variations in the LCEA results. Retrospective research endeavoured to explore the causes of variations, with a limited focus on calculating embodied impacts. Despite the undertaken attempts, there is still a need to investigate the key parameters causing variations in LCEA results by examining methodological approaches of the current studies toward quantifications of embodied and operational energies. This paper aims to address three primary questions: ‘what is the current trend of methodological approach for applying LCEA in residential buildings?’; ‘what are the key parameters causing variations in LCEA results?’; and ‘how can the continued variations in the application of LCEA in residential buildings be overcome?’. To this end, 40 LCEA studies representing 157 cases of residential buildings across 16 countries have been critically reviewed. The findings reveal four principal categories of parameters that potentially contribute to the varying results of LCEAs: system boundary definition, calculation methods, geographical context, and interpretation of results. This paper also proposes a conceptual framework to minimize variations in LCEA studies by standardizing the process of conducting LCEAs.