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As an important public facility, the number, area, and scale of hospital buildings are growing rapidly. The efficiency of nurses' rounds to beds is an important indicator of the efficiency of nursing units in ward buildings. Ward buildings occupy a very important position in the overall energy consumption of hospital building complexes. The type and scale of nursing unit floorplans are some of the key factors affecting the energy consumption of ward buildings. In this paper, three typical floorplan layout types of hospital ward buildings in cold regions of China are selected. The relationships between rounding efficiency, building energy consumption, floorplan layout, and building size were quantified using Origin based on linear regression and non-linear regression. The study showed that at 60 beds, the efficiency of nurse rounds was 35.68% higher in the double-corridor layout compared to the single-corridor. At 44 beds, the difference in average bed energy consumption between the double-corridor type and the single-corridor type is the greatest, with a 9.02% saving in energy consumption. This result confirms that the layout and scale of the ward building has a significant impact on the efficiency of nursing unit rounds and building energy efficiency. [ABSTRACT FROM AUTHOR]

Taking current situation of energy regarding winter heating and study on heating as an example, this paper is based on field measurement, investigation and study, discussing the current energy consumption and usage of winter heating of rural areas in Shandong Province, China. The paper reveals that the most regions in Shandong are utilizing furnaces to keep warming which is a relatively primitive method. Most of villagers are satisfied with the indoor environment although this is not the case, which is contributed by the farmers’ strong passive adaptability. With the constant improvement in living standard of Shandong rural people, simultaneously, energy quality and quantity are required higher and higher. Traditional biomass energy cannot meet their needs. Therefore, more effective and cleaner energy are necessary for them to advance daily living quality and level. The heating measure that will save more energy should be adopted. [ABSTRACT FROM AUTHOR]

China building energy consumption (BEC) has been gradually derived and also sparked attention. However, something worth discussing is estimating the BEC based on the definition of life cycle or building operation. In addition, there exists discrepancy in the previous studies and the real status of BEC has not been known well. In order to settle the disputes over the calculation of BEC, this paper establishes an appropriate accounting method of building energy to present the BEC situation in China and lays the foundation for policymakers to develop appropriate energy saving policies. Meanwhile, this study attempts to clarify the BEC to help policymakers assess the effect of environmental policies. Using the method in this document, we find that the energy consumption of buildings just accounts for 15%–16% of the final total energy consumption in China; by contrast, the previous studies usually have double accounting through a top-down approach if central heat supply of buildings was taken into additional consideration. [ABSTRACT FROM AUTHOR]

Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]

Since decoration is an essential part of buildings, the carbon emissions generated by decoration work should not be ignored. In recent years, prefabricated decoration has attracted much attention as efforts are made to pursue green, low-carbon, and waste-reducing buildings. However, research on carbon emissions assessment of prefabricated buildings has focused mainly on the structural aspect of prefabricated buildings, with few studies having considered prefabricated decoration. This study therefore focuses on assessing the carbon emissions of prefabricated decoration from the life cycle perspective of a case study residential building and explores the potential for reducing carbon emissions by decorating buildings with prefabricated components. The results show that using prefabricated decoration in the case study building reduced carbon emissions by 29.08% at the building material production stage compared to traditional decoration, and using an optimized design of prefabricated decoration, the building's energy consumption over its design life could reduce carbon emissions by 1046 kgCO2/m2. These findings demonstrate the benefits of prefabrication decoration for reducing carbon emissions. This study provides decoration companies with robust data and insights to guide future decisions and practices, helping to transform and achieve the carbon neutrality goal for the building decoration industry. [ABSTRACT FROM AUTHOR]

The concentration of atmospheric greenhouse gases is being amplified by human activity. Building energy consumption, particularly for heating and cooling purposes, constitutes a significant proportion of overall energy demand. This research aims to establish a smart evaluation model to understand the thermal requirements of building occupants based on an open-access dataset. This model is beneficial for making reasonable adjustments to building thermal management, based on factors such as different regions and building user characteristics. Employing Bayesian-optimized LightGBM and SHAP (SHapley Additive exPlanations) methods, an explainable machine learning model was developed to evaluate the thermal comfort design of buildings in different areas and with different purpose. Our developed LightGBM model exhibited superior evaluation performance on the test set, outperforming other machine learning models, such as XGBoost and SVR (Support Vector Regression). The SHAP method further helps us to understand the interior evaluation mechanism of the model and the interactive effect among input features. An accurate thermal comfort design for buildings based on the evaluation model can benefit the carbon-neutral strategy. [ABSTRACT FROM AUTHOR]

• Obtaining the optimal interpolation method for adjacent sites. • Proposing the method for revising meteorological data at city scale. • Verifying the accuracy of the method by energy consumption simulation. In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs. [ABSTRACT FROM AUTHOR]

The rural residences of Northwest China are characterized by a state of high energy consumption and low comfort due to the limited economic level and awareness of energy-saving compared with the urban residences. To remedy this, appropriate passive design strategies should be adopted first, in order to provide a design mode with low energy consumption and low cost for rural residences under the premise of thermal comfort. In this paper, taking Hanzhong region (Shaanxi Province, China) as an example, we establish a benchmark model based on a field survey and develop an optimization process by combining EnergyPlus simulation software, the MOBO optimization engine, and weighted sum method. The action mechanisms of passive design parameters, including the building orientation, length–width ratio, building envelope parameters, external shading system, and window–wall ratio, on heating, cooling, and total energy consumption are analyzed, and the quantitative relationships between single-parameter and energy consumption are established. Then, the mutually restricted indices of total energy consumption and initial investment cost are taken as optimization objectives, and 17 design parameters are selected as the optimization variables. The NSGA-II algorithm is adopted to conduct the multi-parameter, multi-objective optimization design for rural houses in Hanzhong area, through coupling of the EnergyPlus and MOBO software. In this way, Pareto solutions are obtained and the value distributions of the multi-objectives and design parameters are analyzed. Based on the actual requirements of decision-makers and using the weight method, three design schemes focusing on different performance tendencies are proposed. The results indicate that by using the proposed optimization process, the building energy consumption can be significantly reduced while taking initial investment costs into account, where the energy-saving rate is in the range of 31.9–61.5%. When the EC/IC ratio is 1:1, 2:1, and 1:2, the energy-saving rate can reach 51.5%, 57.8%, and 43.5%, respectively. It can provide a beneficial pattern for the energy-saving design and renovation of rural residences in Hanzhong area of China. [ABSTRACT FROM AUTHOR]

In order to resolve the contradiction between the lack of building energy data and the need of more detailed information about energy performance, this paper presents a simplified building energy performance diagnosis method which can assess the energy performance from building level to system level with only limited information. Based on multiple-parameter regression between the whole building energy bills and outdoor weather data, the proposed diagnosis method uses the regression coefficients to identify the energy use of main systems (i.e., lighting-plug system and cooling/heating system), as well as other detailed information about physical properties of the building and cooling/heating system (i.e., cooling or heating load, the operation of cooling/heating system and the efficiency of cooling/heating system). One case study was conducted in an office building in China to test the application of this diagnosis method. In addition, the regression result of simulated energy consumption served as benchmark data to further diagnose the performance gap between the operation stage and the design stage and help locate the poor performance and key points of energy-saving. Finally, all diagnosis results have been verified by the performance data from advanced energy consumption monitoring system together with field surveys and measurements. [ABSTRACT FROM AUTHOR]

With the rapid urbanization of cities, the morphology of urban blocks develops from a single and low-density type to a diversified and high-density one, which has great influence on the underlying ground surface conditionings, ventilating performance and heat gain at a district scale, thus enhancing urban heat island effect and increasing building energy consumption. In this paper, the distinct urban attributes (greening rate, building density, and volume ratio) of ten districts around the county meteorological stations of Shanghai were collected to explore the relationships between the urban heat island effect and the urban morphology attributes. In addition, an energy model for a typical office building was setup to investigate the quantity effect of these distinct urban attributes on the energy performance of the buildings located around these ten county meteorological stations. The results show that the correlations between the urban island effect and distinct urban attributes exist. The heating load indexes of the buildings located in downtown are 1.5-5% less than those in the suburbs. As the heat island intensity raises by 1℃, the average heating energy consumption will decrease 5.04%. [ABSTRACT FROM AUTHOR]

Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China. [ABSTRACT FROM AUTHOR]

The large public building energy consumption (BEC) is the focus of Building Energy Saving, therefore, it is necessary to study the characteristics of BEC to find out the important factors affecting BEC. For this purpose, 27 office buildings in the west of Inner Mongolia Autonomous Region were studied, based on statistical analysis of the researched basic information and energy consumption bill of these buildings. This paper focused on the determination of the significant factors affecting the total and subentry energy consumption intensity (ECI) of office building, as well as the establishment of standardized linear regression models between these selected factors and total and subentry ECI. Firstly, eleven continuous variables, three independent categorical variables, and the climate factor were selected and analyzed the impact on total and subentry ECI by statistical software SPSS20.0, in order to find out the significant influencing factors. Then based on the results of curve fitting, standardized models of total and subentry ECI and their respective significant impact factors were established using multiple linear regression analysis. The regression results showed that the electricity use percentage of the total equivalent electricity consumption was an important factor affecting the total ECI of office building in the west of Inner Mongolia Autonomous Region. Finally, univariate analysis of variance (ANOVA) was conducted between the three independent categorical variables and the total and subentry ECI, and the results showed that these factors had no significant effect on the ECI of office building. Process of the regressive model establishment and the results of analysis of variance could both guide us to propose more targeted energy saving measures. [ABSTRACT FROM AUTHOR]

The impact of village morphology on habitats' daily spatiotemporal behavior and building energy consumption is still vague. This research aims to propose a method to reveal how and to what extent village morphology and residents' demographic structure influence farmhouses' occupancy rate and energy consumption. For this purpose, a combined time geography and building energy consumption method was proposed to disclose the relationship among residents' family roles, village morphology and household energy consumption. To explore the occupancy rate of farm-houses with different family members in different village morphology, resident types based on field research (retrospective interviews and aggregation behavior observation) were formulated. Residents with similar time-space behavior seemed as an "agent" and then a specified number of agents are combined into different families for ideal experiments. By placing different households in different villages for multi-factor cross-experiment, the occupancy rates of different households are obtained based on individual time-space behavior patterns, and then the energy consumption of each household under different scenarios is obtained by combining building energy consumption simulation. In addition, the relationship between resident's disposable personal time, local public space' quality, and the house occupancy rate was analyzed. Combined with a case study in Shanghai with 417 valid questionnaires, it can be seen that the method proposed in this paper is effective and villages with high quality public space have the potential to reduce household energy consumption from 2.17% to 38.82% for diverse families. The quantitative result indicated that the occupancy rate is mainly influenced by residents having more disposable personal time, and such space-time behavior is sensitive to village morphology. This study proposed a combined method and case evidence to systematically analyze the complex relationship between resident behavior, the village morphology, and household energy consumption, thus contributing to the sustainable plan and design of the community spatial system. [Display omitted] • Proposed systematic approach to reveal the relationship between morphology and energy. • Energy-saving effect of public space is revealed from villagers' behavior chain. • The influence of the village morphology on energy consumption is between 2.17% and 38.82%. • Alternative mode of villager's space usage in disposable personal time was proved. [ABSTRACT FROM AUTHOR]

Urban heat island (UHI) draws more attention as it affects not only the health of residents but also the energy consumption of buildings at the city scale. To achieve carbon neutrality goals, it is crucial to better understand the mechanism of the UHI influences on building energy consumption. The characteristics of urban heat island intensity (UHII) and the relationship between the UHII effect and building electricity and related coal consumption were analyzed, based on the long period of monitoring data with hourly weather data from 1 January to 31 December 2019. Results show that a strong correlation between the annual mean UHII and the median daily mean UHII exists. The synthetic diurnal UHII of most cities presents a U-shaped variation trend. In different building climate zones in China, namely, severe cold region (SCR), cold region (CR), hot summer cold winter region (HSCWR), hot summer and warm winter region (HSWWR), and mild region (MR), the influences of UHII on building energy consumption were analyzed. The existence of UHI reduces building energy consumption in 96.7% of SCR cities and 60.8% of CR cities, while in HSCWR, HSWWR, and MR cities, the percentage of cities where the building energy consumption is increased by UHI is 69.4%, 80%, and 63.6%, respectively. Urban climate strongly influences building energy consumption, indicating that it should be considered and analyzed in detail for making future urban development or carbon emission reduction strategies. [ABSTRACT FROM AUTHOR]

China is currently experiencing rapid urbanization. The building sector plays an important role in China's energy conservation and emission reduction. An increasing amount of energy will be steadily consumed in the building sector as the urbanization process accelerates. China's government attaches great importance to building energy efficiency (BEE). How does the urbanization process affect building energy consumption (BEC)? How much energy can be saved in the building sector through implementation of energy polices? These questions are critical for government development strategies and planning. Thus, in this paper, an econometric model (co-integration technique) is developed to analyze how China's BEC is affected by macroeconomic variables in the context of urbanization, and also forecast the BEC and BEE potential until 2020 under different scenarios. The research result shows that occupants’ lifestyles, residents’ living standards, and energy price have significant influences on BEC. The building sector is likely to contribute approximately 20% to China's energy conservation through electricity pricing reform. [ABSTRACT FROM AUTHOR]

Spectral selective absorption film (SSAF), a solar control film, has a special energy-saving mechanism. In the previous studies of SSAF coated glazing systems, thermal parameters (global thermal transmittance (U) and solar heat gain coefficient (SHGC)) calculated by traditional algorithms were not verified. In order to evaluate the energy-saving effect of SSAF coated glazing systems accurately, U and the SHGC were calibrated and then used for energy consumption simulation. Firstly, the simulation models of the heat transfer process of SSAF coated glazing systems were established by COMSOL Multiphysics, considering simplified linear attenuation of radiative transfer. After being validated, the simulation models were used for the calibration of U and SHGC by the Multiple Linear Regression (MLR) model. As a result, the calibration coefficients of U and SHGC are 1.126 and 1.689, respectively. Secondly, the thermal parameters of SSAF coated glazing systems calibrated by the calibration coefficients were used for a building energy consumption simulation case. The result showed that the inner surface is the best coating position for single glazing systems (SG), while the outer surface is the best coating position for double glazing systems (DG) in hot summer and cold winter zone, hot summer and warm winter zone and the moderate zone of China. [ABSTRACT FROM AUTHOR]

The contradiction between the shortage of urban land and the demand for urban green space needs to be solved reasonably. Different from urban parks, the cooling effect of small green space (SGs) has not been paid enough attention or fully evaluated. The major objective of this research was to explore the cooling and energy saving benefits of SGs with administrative units. The ENVI-met and EnergyPlus models were built based on field measurements on the green space of Xi'an Jiaotong University Qujiang Campus. Field measurement shows that the closer an area to the green space, the lower the air temperature and the higher the relative humidity, especially in the daytime. The ENVI-met results showed that the SGs can reduce the temperature by 0.66 °C within the range of 1260 m × 1350 m compared with the building scene at noon, the EnergyPlus simulation results showed that the building 100 m away from the SGs can reduce the air conditioning energy consumption by 2.1% compared with the distance of 150 m. The research can improve the planning and design standards of urban green space, clarify the cooling and energy saving benefits of SGs, and promote the ecological urban design. [ABSTRACT FROM AUTHOR]

Abstract: Hainan province is the only tropical and island province in China, with the unique climatic and geographical features. It has a different energy performance in public buildings from those in the inland areas. This paper investigated the current situation of energy consumption of the sampled public buildings, including 26 hotels, 24 office buildings and 7 department stores, through analyzing utility billing data, on-site survey and measurement, and site operational records. The breakdowns of energy consumption found out that the electricity dominated the energy consumption for all the sampled buildings and the air conditioning system played a key role in the energy consumption of major building services. By analyzing the main factors for building energy consumption, the annual average energy use indexes were calculated to be 141.63kWh/m2, 87.70kWh/m2, and 166.95kWh/m2 for hotels, office buildings and department stores, respectively. Besides, the comparison between Hainan province and other areas revealed that the building energy consumption of this tropical province was at a low level. Because indoor environment quality can influence the health and work efficiency of humans in buildings, field tests on the indoor environment quality were also carried out, and the performance of indoor environment quality was rated. [Copyright &y& Elsevier]

The building envelope performance significantly influences the building energy consumption, and the major influencing factors of the building envelope include the external wall U-value, external wall solar absorptance, roof U-value, roof solar absorptance, external window U-value, and window-to-wall ratio. This paper proposed a framework that integrates Building Information Modeling (BIM) with LSSVM (least square support vector machine (LSSVM) and non-dominated sorting genetic algorithm-II (NSGA-II) to study the influence of building envelope parameters on building energy consumption and discover the best design. The DesignBuilder (DB) is used to perform energy consumption simulation and orthogonal experiment to obtain the sample data set. The LSSVM is utilized to learn the data set to establish a prediction model between the building envelope and energy consumption, which is used as the fitness function of the building energy consumption in NSGA-II. With the objective of minimizing building energy consumption and maximizing the indoor thermal comfort (measured by predicted mean vote or PMV), an NSGA-II multi-objective optimization model is established. The Pareto front is obtained through calculation, and the optimal combination of building envelope parameters is discovered using the ideal point method. A case of a proposed school building in China is used to demonstrate the feasibility and effectiveness of the developed framework, the results show that: (1) The LSSVM has great ability to predict building energy consumption, achieving an accurate prediction with a goodness-of-fit of 0.9549 and an RMSE of 0.0273; (2) The optimized envelope design parameters of the proposed building are presented, including the External wall U-value is 0.2, the External wall solar absorptance is 0.55, the Roof U-value is 0.2, the External window U-value is 0.4166, the External window U-value is 1, the Window-wall ratio is 0.215. (3) The developed LSSVM-NSGA II hybrid approach can effectively improve the building's energy consumption and thermal comfort with the building energy consumption reduced by 10.6% and the thermal comfort increased by 32.2% than the initial values, respectively. The proposed LSSVM-NSGA II framework is able to optimize building envelope design parameters than its precedent, which can provide effective thinking for similar problems. • LSSVM-NSGA-II hybrid method can predict and optimize the building energy consumption. • The energy consumption prediction surrogate model trained by LSSVM is an efficient NSGA-II fitness function. • NSGA can effectively reduce building energy consumption and improve thermal comfort at the same time. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the comprehensive percentage influence of input parameters on building energy and comfort performance by a new approach of sensitivity analysis (SA) and explore the most reliable and neutral sampling and sensitivity assessment method. The research combined 7 sampling methods with 13 SA methods to comprehensively integrate the percentage influence of 25 input parameters on building energy and comfort performance in 24 coastal cities of China. The results have found that the percentage influence of many important input parameters is affected by geographical position. Considering both energy and comfort performance of the building, the key parameters are heating setpoint, infiltration rate, cooling setpoint, roof U value, roof solar absorptance, window solar heat gain coefficient, equipment, and occupant density, all of which could comprehensively impact 70% of energy demand and comfort performance along the Chinese coastline. This is of great significance for policymakers to formulate relative building regulations. After comparing the F-test and the exceed percentage test, we recommended the Pearson with Quasi-random sampling method as the most reliable SA assessment method in building simulation, followed by the standardized regression coefficient in random sampling and Latin hypercube sampling methods, which can achieve data closest to the average value. [ABSTRACT FROM AUTHOR]

China's regular energy statistics does not include the building sector, and data on building energy demand is included in other types of energy consumption in the Energy Balance Sheet (EBS). Therefore data on building energy demand is not collected based on statistics, but rather calculated or estimated by various approaches in China. This study aims at developing and testing China's building energy statistics by applying an adapted EBS. The advantage of the adapted EBS is that statistical data is from the regular statistical system and no additional statistical efforts are needed. The research result shows that the adapted EBS can be included in China regular energy statistical system and can be standardized in a transparent way. Testing of the adapted EBS shows that China's building energy demand has shown an annual increase of 7.6% since 2001, and a lower contribution to the total energy demand as compared to the developed world. There is also a close link to lifestyle and living standard while industrial energy demand is mainly driven by economy and decoupling of building energy demand with increasing of building floor area, this is due to a considerable improvement of building energy efficiency. The adapted EBS creates a method for China conducting statistics of building energy consumption at the sector level in a uniform way and serves as the basis for any sound building energy efficiency policy decisions. [ABSTRACT FROM AUTHOR]