Climatic Regions Of China Research Articles

Differences in functional niche hypervolume among four types of forest vegetation and their environmental determinants across various climatic regions in China.

Functional traits play an important role in studying the functional niche in plant communities. However, it remains unclear whether the functional niches of typical forest plant communities in different climatic regions based on functional traits are consistent. Here, we present data for 215 woody species, encompassing 11 functional traits related to three fundamental niche dimensions (leaf economy, mechanical support, and reproductive phenology). These data were collected from forests across four climatic zones in China (tropical, subtropical, warm-temperate, and cold-temperate) or sourced from the literature. We calculated the functional niche hypervolume, representing the range of changes in the multidimensional functional niche. This metric quantifies how many functional niche spaces are occupied by existing plants in the community. Subsequently, we analyzed differences in functional niche hypervolume and their associated environmental factors across different types of forest vegetation. The results indicate that the functional niche hypervolume and the degree of forest vegetation overlap decrease with increasing latitude (e.g., from tropical rainforest to cold temperate coniferous forest). The total explanatory power of both climate and soil factors on the variation in functional niche hypervolume was 50%, with climate factors exhibiting a higher explanatory power than soil factors. Functional niche hypervolume is positively correlated with climate factors (annual mean temperature and annual precipitation) and negatively correlated with soil factors (soil pH, soil organic matter content, soil total nitrogen content, and soil total phosphorus content). Among these factors, annual mean temperature, soil pH, and soil total nitrogen content most significantly affect the difference in functional niche hypervolume among forest vegetation. Our study emphasizes the significant variation in the functional niche hypervolume among typical forest vegetation in China.

Evaluation energy flow and analysis of energy economy for agricultural yield production in different geographical regions of China

Abstract Dry and humid climates have different potentials for providing soil moisture. Agricultural drought is a confirmed criterion for evaluating production potential in agriculture, which is discussed in this research. Therefore, this research aims to investigate drought using meteorological and agricultural drought indicator data in four climatic regions of China (humid, semi-humid, semi-arid and dry). For this purpose, climatic information was collected in the last 20 years, and the values of the standard precipitation index (SPI) and reconnaissance drought index (RDI) were determined. Examining the indicators indicates that the indicators are high in all the years under review in dry areas. In the semi-arid region, there was a significant decrease in the average value of the indices in July and August in the years 2017–2022. Drought indicators did not show a critical situation in humid and semi-humid areas, and there was sufficient moisture for plants throughout the year. The results showed that there was a high correlation between the SPI and the RDI in all the identified areas. In addition to rainfall, the RDI also includes transpiration and is more sensitive, especially in dry areas where transpiration is higher than rainfall.

Comparison of operation characteristics of a new spray cooling system based on PV/T and heat recovery in sow houses in five climate regions of China

Comparison of operation characteristics of a new spray cooling system based on PV/T and heat recovery in sow houses in five climate regions of China

A new perspective for assessing hydro-meteorological drought relationships at large scale based on causality analysis

Correlation analysis is the common method to evaluate the relationship between two variables; however, it may sometimes cause spurious correlations. Specifically, in the field of hydrometeorology, with the impacts of climate change and human activities, correlation analysis is difficult to identify the true relationship between variables, and thus, causality analysis should be adopted instead. This study analyzed the causal relationship between meteorological drought and hydrological drought in different climatic regions of China by using convergent cross mapping (CCM). We improved the identification of CCM convergence by using the coefficient of variation and applied it in the field of large-scale hydrometeorology. The results of correlation analysis were compared, and the applicability of causality analysis was explored. The results revealed that: In Southeast China, the correlation and causality between meteorological drought and hydrological drought were both large. In Northeast China and central Qinghai–Tibet Plateau, the correlation between meteorological drought and hydrological drought was small, but the causality was large. In view of the spurious correlation, introducing causality analysis can better explain the relationship between meteorological drought and hydrological drought, especially in areas with snowmelt runoff. Overall, CCM can provide valuable causal information from common time series in the field of large-scale hydrometeorology and has a wide range of application values. However, causality analysis cannot explain the positive or negative relationship between variables. Therefore, when analyzing the relationship between variables, the advantages of the two methods should be given full play.

Study on the heat transfer of a concrete wall outfitted with phase change materials

This paper tested and simulated the thermal performance of a concrete wall outfitted with Phase change materials (PCMs) under 5 climate conditions in China. The PCMs were encapsulated in high-density polyethylene spherical shells in the concrete to form a PCMs-concrete layer. The daily thermal load change rate through the wall when the PCMs-concrete layer was placed in different positions in five typical climate regions in China were studied. The results show that to make the best use of the PCMs in summer, the phase transition temperature is suggested to be between the outer and inner surface temperatures. For the cases when the outer and inner surface temperatures are higher than the phase transition temperature, the PCMs should be installed in the middle of the wall; To make the best use of the PCMs in winter, the location of PCM should be adjusted according to the range of ambient temperature, when the ambient air is warm enough, the PCMs layer should be installed near to the inner surface, when the ambient air is tepid, the PCMs layer should be installed near to the outer surface, when the ambient air is cold, results do not suggest using PCMs.

Experimental research and energy saving analysis of an integrated data center cooling and waste heat recovery system

To better make use of the waste heat in distributed data centers, a new design of integrated waste heat recovery system that can meet different needs including heating for users and data center cooling is introduced. The system can switch among different operation modes including waste heat recovery mode, natural cooling mode, composite cooling mode and refrigeration mode based on the ambient temperature. The integrated waste heat recovery system is experimented in the enthalpy-difference laboratory, and the experimental results show well consistency compared with the modeling results. The operation strategies to switch among different modes are determined based on the experimental and modeling results. The paper introduced PUE (Power Usage Effectiveness) and ERE (Energy Reuse effectiveness) to comprehensively evaluate the cooling and energy reutilization effectiveness of the system. According to the evaluation result, the integrated waste heat recovery system has well performed energy effectiveness, environmental and economic effect. The ERE values of using the integrated waste heat recovery system in Chinese typical climate cities including Harbin, Beijing, Xi'an, Shanghai and Guangzhou are 0.87, 1.02, 1.04, 1.17 and 1.29 respectively. Using this system at 96 kW distributed data center instead of CRAC (computer room air conditioning) system can save 152.7 MWh electricity, 56.4 tons of standard coal, and 244.7 tons of CO2 emission in Beijing annually. The payback periods of this new system in different climate regions in China are all within 3 years.

Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China

The reference evapotranspiration (ETo) pertains to the evapotranspiration of cold-season grasses with an approximate height of 0.12 m or full-covered alfalfa with a height of 0.50 m. Accurate short-term ETo forecasts are indispensable for informed irrigation decisions by relevant departments and individuals. Four deep learning (DL) models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (Bi-LSTM), and Bidirectional GRU (Bi-GRU), as well as two calibrated empirical models (Hargreaves-Samani (HS) and reduced-set Penman–Monteith (RPM)), were used to evaluate the performance of the ETo forecast with a lead time of 1–7 d using temperature forecasts in different climates. The results reveal that the DL models and calibrated HS and RPM models exhibited comparable trends in the ETo forecasts for lead times of 1–7 d. Nonetheless, the DL models consistently outperformed the HS and RPM models across the diverse climatic regions in China. The DL models displayed an average root mean square error (RMSE) and mean absolute error (MAE) of less than 0.887 and 0.633 mm/d, respectively. Moreover, the mean correlation coefficient (R) and accuracy (ACC) exceeded 0.807% and 89.701%, respectively. Among the DL models, the LSTM model demonstrated slightly superior performance in short-term daily ETo forecasts in diverse climates. The LSTM model exhibited RMSE and MAE ranges of 0.563–0.875 mm/d and 0.418–0.626 mm/d, respectively, along with R and ACC ranges of 0.81–0.90 and 89.94–98.11%, respectively. Furthermore, even with an increase in lead time, the DL models continued to exhibit strong predictive capabilities, consistently surpassing the performance of the HS and RPM models. Overall, the trained DL models presented an exceptional ability to forecast the short-term daily ETo in various climatic regions of China. These models require only a few input variables and readily available data, making them highly advantageous for practical applications in ETo forecasting. Such models hold promise for significantly enhancing regional agricultural water-resource planning and management.

Historical peak situation of building carbon emissions in different climate regions in China: Causes of differences and peak challenges

The climatic conditions in different regions of China are complex and diverse, and the characteristics of building energy consumption in different climatic regions are quite different, leading to significant differences in the historical peak situation of building carbon emissions (BCE). Based on the statistical Mann–Kendall (MK) trend test method, this study evaluates the historical peak situation of BCE in different climate regions in China and discusses the reasons for the differences in the growth trends and peak situations of BCE in these regions. Furthermore, according to the characteristics of building energy consumption in different climate regions, the challenges faced by each climate region in promoting the peaking of BCE are highlighted. The research results show that owing to the continuous increase in the proportion of electrification and clean energy power generation, the electricity emission factor and carbon emissions per unit of energy consumption continue to decline, and the growth rates of BCE in the transitional and southern regions are significantly lower than the growth rate of building energy consumption, and the carbon emissions per unit floor area in the southern region has reached its peak. The main obstacles to promoting the peaking of BCE in the northern heating region are the high‑carbon heating energy structure and the unrestrained heating behavior of residents, while the challenges faced by the transitional and southern regions are the southward migration of the population and economic centers of gravity and the gradual increase in residents' requirements for living environment comfort. Government decision-makers should formulate differentiated BCE peaking strategies according to the characteristics of building energy consumption in different climate regions.

A novel regional drought monitoring method using GNSS-derived ZTD and precipitation

The drought monitoring indexes, such as SPEI and SPCI, have been proposed previously. However, these indexes are established at specific station and unsuitable to evaluate the regional drought, which becomes the focus of this study. A novel drought monitoring method using the zenith troposphere delay (ZTD) derived from global navigation satellite system (GNSS) and precipitation is proposed, and a new index, which is named regional precipitation and ZTD index (RPZI), is established. The station-based precipitation and ZTD index (PZI) is first proposed by considering the data selection, expression, multi-time scale and standardization. The RPZI is then calculated by using the Thiessen Polygon method to determine the weighting of each station in the located area. The proposed PZI is first validated in 8 climatic regions of China over the period of 2006–2018 at 801 metrological stations. The RPZI is further evaluated in arid, semiarid, humid, and semi humid areas of China over the period of 2006–2018 and Townsville, Queensland in Australia over the period of 1998–2018, respectively. Comparison results show that the proposed RPZI has a good consistency with the regional precipitation smoothing index (RPSI) when compared with station-based SPEI, station-based PZI, and regional SPEI (RSPEI), and the correlation coefficient is the highest between the RPZI and RPSI with values of 0.81, 0.90, 0.93, and 0.85 in four areas of China, and the corresponding value reaches 0.964 in Queensland, Australia. Such results show the good capacity of the proposed RPZI for regional drought monitoring, which is of great importance for global drought disaster prevention.

Predicting daily solar radiation using a novel hybrid long short-term memory network across four climate regions of China

Accurate daily solar radiation (Rs) data is crucial for the growth and development of crops, but its direct observation is relatively scarce in most regions around the world. Therefore, accurately estimating daily Rs is of great significance. Regarding the above practical requirement, this study developed a novel hybrid deep learning model (MEA-DBN-LSTM), which combined the Long Short-Term Memory model (LSTM), Deep Belief Network (DBN), and Mind Evolutionary Algorithm (MEA), to estimate the Rs across four different climate regions of China using five easily accessible meteorological input combination (sunshine-based, temperature-based and three complex-based). The results showed that MEA-DBN-LSTM was proved to be the most recommended Rs estimated model across four different climate regions of China, and the corresponding median R2, NSE, RMSE, MAE, and MAPE values ranged 0.805–0.999, 0.656–0.954, 1.069–4.289 MJ m−2 d–1, 0.889–3.532 MJ m−2 d–1, and 0.055–0.296 respectively. Correspondingly, compared to the empirical Rs model, the improvement values of MEA-DBN-LSTM were 12.33–52.13 % for R2, 14.80–69.38 % for NSE, 33.75–69.77 % for RMSE, 36.03–62.70 % for MAE, and 42.40–66.67 % for MAPE, respectively. Moreover, the MEA-DBN-LSTM exhibits improvements in R2, NSE, RMSE, MAE, and MAPE, ranging 1.40–13.50 %, 1.27–24.79 %, 1.81–61.18 %, 1.90–61.30 %, and 2.31–57.55 %, respectively, compared to other forms of LSTM-type models. Specifically, this study found the DBN modules significantly improved the complex-based single LSTM model, but without impact on sunshine-based and temperature-based single LSTM models. Regarding the MEA modules, it was effective in the hyperparameter optimization compared to DBN-LSTM. Furthermore, significant accuracy discrepancies exist among Rs models using different input combinations across four different climate regions, and the complex-based was generally superior to other input combinations. Overall, our findings can provide accurate daily Rs across four different climate regions of China using easily accessible meteorological data, which is of great significance for crop water consumption estimation, agricultural water resources management, crop growth conditions optimization, and sustainable agricultural development.

Facial Expressions of Urban Forest Visitors Jointly Exposed to Air Pollution and Regional Climate

Urban forests have important functions to alleviate air pollution, adjust the regional climate, and promote visitors’ mental health and well-being. Humans’ perceptions towards both atmospheric particulate matter (PM) and meteorological factors can be a gauge for assessing the functional services of urban forests. The geographical locations of host cities experiencing urbanization could take part in effects on emotional states of forest visitors. In this study, a total of 6309 facial photos of urban forest visitors were obtained from social networks in 42 cities of mainland China. Happy, sad, and neutral emotions were rated as percent scores in 2020, when the same-day air pollutants, meteorological factors, and socioeconomic indicators were recorded. The positive emotional index (PEI) was calculated as the difference between happy scores and sad scores. The results reveal that severe air pollutants (jointly PM2.5 > 75 μg/m3, PM10 > 150 μg/m3, and AQI > 150) were more frequently found in cities in the northeastern and northern areas of China. The forest visitors in the northeastern cities showed higher happiness scores compared to the visitors in other regions. The Quasi-Poisson regression suggested that high scores of happiness were frequently disclosed in weathers with low PM10. High scores of sadness were regressed on exposure to cities with a low GDP per capita and low total retail sales but with a high GDP at low-longitudinal and high-latitudinal locations with low levels of PM2.5 pollution, relative humidity, and wind velocity and a high temperature. The happiness score and PEI showed high-value aggregations in mega-sized cities (population over 10 million), such as Beijing and Zhengzhou, and in a metro-sized city (population of 5–10 million in Langfang) from climate regions of China.

Allelic variation in transcription factor PtoWRKY68 contributes to drought tolerance in Populus.

Drought stress limits woody species productivity and influences tree distribution. However, dissecting the molecular mechanisms that underpin drought responses in forest trees can be challenging due to trait complexity. Here, using a panel of 300 Chinese white poplar (Populus tomentosa) accessions collected from different geographical climatic regions in China, we performed a genome-wide association study (GWAS) on seven drought-related traits and identified PtoWRKY68 as a candidate gene involved in the response to drought stress. A 12-bp insertion and/or deletion and three nonsynonymous variants in the PtoWRKY68 coding sequence categorized natural populations of P. tomentosa into two haplotype groups, PtoWRKY68hap1 and PtoWRKY68hap2. The allelic variation in these two PtoWRKY68 haplotypes conferred differential transcriptional regulatory activities and binding to the promoters of downstream abscisic acid (ABA) efflux and signaling genes. Overexpression of PtoWRKY68hap1 and PtoWRKY68hap2 in Arabidopsis (Arabidopsis thaliana) ameliorated the drought tolerance of two transgenic lines and increased ABA content by 42.7% and 14.3% compared to wild-type plants, respectively. Notably, PtoWRKY68hap1 (associated with drought tolerance) is ubiquitous in accessions in water-deficient environments, whereas the drought-sensitive allele PtoWRKY68hap2 is widely distributed in well-watered regions, consistent with the trends in local precipitation, suggesting that these alleles correspond to geographical adaptation in Populus. Moreover, quantitative trait loci analysis and an electrophoretic mobility shift assay showed that SHORT VEGETATIVE PHASE (PtoSVP.3) positively regulates the expression of PtoWRKY68 under drought stress. We propose a drought tolerance regulatory module in which PtoWRKY68 modulates ABA signaling and accumulation, providing insight into the genetic basis of drought tolerance in trees. Our findings will facilitate molecular breeding to improve the drought tolerance of forest trees.

Assessing and Comparing Reference Evapotranspiration across Different Climatic Regions of China Using Reanalysis Products

This study aims to assess the accuracy of the reference evapotranspiration (ET0) estimated by CLDAS, ERA5 reanalysis products, and the quality of reanalysis weather variables required to calculate PM-ET0. For this purpose, the applicability of surface meteorological elements from the ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), and the second-generation China Meteorological Administration Land Data Assimilation System (CLDASV2.0) datasets are evaluated in China by comparison with local observations from 689 stations reported by the Chinese Meteorological Administration (CMA). Statistics including percent bias (PBias), coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) are used to check the accuracy. The results show the highest correlation between reanalysis temperature and observations, with a mean R2 of 0.96, 0.90 for the CLDAS maximum and minimum air temperatures, and 0.87, 0.84 for ERA5. For the reanalysis of solar radiation (Rs) and relative humidity (RH), an overestimation trend is shown for Rs and an underestimation trend is shown for RH. For reanalysis of wind speed, a relatively low accuracy is shown. The accuracy of ET0 estimated by the two reanalysis products is acceptable in China, but the spatial and temporal consistency between the CLDAS estimates and site observations is higher, with a mean RMSE R2 of 0.91, 0.82 for CLDAS and 1.42, 0.70 for ERA5, respectively. Moreover, CLDAS reanalysis products are more effective in describing the boundary details of the study area.

On The Profiling of Air Leakage Infiltration Pattern across Chinese Vernacular Buildings

The purpose of this paper is to provide understanding of the seasonal pattern of air leakage (infiltration) in Chinese vernacular buildings across China’s five climate regions. In achieving the set purpose, a grand extensive literature survey was conducted and supported with data drawn from established Meteonorm V6.1 on sensible heat and psychrometric variables. Numerical computations for normalized and specific infiltration from stack effects followed the Gowri method in line with ASHRAE reference 2004. Solar energy admittance into building followed Bouger’s model form Angstrom properties. From the distribution of vernacular buildings across five climate regions of China, evidence from computational and numerical values showed symmetries in terms of minimums and maximums times of occurrence. Further, a reciprocal pattern exists between solar radiative admittance and region’s temperature profile. Knowing that Chinese vernacular building heritage extended to further Asia, this research became limited to only the Chinese region. It became difficult to report if the construction culture away from China has correlation with infiltration and energy admittance value. Earlier works on Chinese climate and vernacular dwellings reported a climate responsive dwelling designed by passive cooling strategy; a gap was closed by extending the previous work to specific infiltration pattern and energy admittance level. Chinese vernacular buildings by virtue of research outcomes are and should be adoptable to modern housing needs for cultural integration.

Development of a novel daily-scale compound dry and hot index and its application across seven climatic regions of China

Development of a novel daily-scale compound dry and hot index and its application across seven climatic regions of China

Future changes of dry‐wet climate regions and its contributing climatic factors in China based on CMIP6 models

AbstractUnderstanding future dry and wet changes under global climate change is of great regional significance in China. Based on six Global Climate Models (GCM) from the Coupled Model Intercomparison Project phase 6 (CMIP6), we project the changes of dry‐wet climate regions in China in the 21st century under the three SSP‐RCP (shared socioeconomic pathway‐representative concentration pathway) scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), and further identify the key climatic factors that lead to the transition of dry‐wet climate regions. The results show the area of humid and subhumid regions in China will show an upward trend in the future, while the area of semiarid and arid regions will show a downward trend. Especially under the SSP5‐8.5 scenario, the area of the subhumid region has the largest increase trend (0.042% per year), and the area of the arid region has the largest decline trend (−0.048% per year). The three main transfer directions of dry‐wet climate regions are from arid to semiarid regions, from semiarid to subhumid regions, and from subhumid to humid regions in the future. Due to the future extensive wetting trend in the north, the future dry‐wet climate regions will shift to wetter types in Tibet, North China, the Qinling‐Huaihe regions and the northeast. In addition, the key climatic factors for the transition of dry‐wet climate regions are mainly precipitation, maximum temperature and solar radiation. The increase of precipitation plays a dominant role for areas projected to change to wetter types, and the increase of potential evapotranspiration (PET) plays a dominant role for areas projected to change to drier types. The PET changes in changed areas of dry‐wet climate regions are mainly dominated by maximum temperature and solar radiation. Our research has important implications for future water resource management and reconsideration of climate mitigation and adaptation policies.

Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions

Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region.

Power/thermal-to-hydrogen energy storage applied to natural-gas distributed energy system in different climate regions of China

This paper presents a novel distributed energy system (DES) with hybrid-hydrogen energy storage, in which the proton exchange membrane electrolyzer (PEMEC) and steam reforming of methanol (SRM) are used to convert surplus power and waste heat into hydrogen. To identify the feasibility of the proposed DES in different climate regions, a case study is performed to choose a rehabilitation hospital in Harbin, Jinan, Wuhan and Guangzhou of China to explore the DES optimal configurations. The mixed integer linear programming model is established and the multi-objective optimization considering annual total cost (ATC) and annual carbon emissions (ACE) is conducted by improved version of augmented ε-constraint (AUGMECON2) method for system optimal configurations. After selecting four cases among Pareto frontiers for each city, the two methods of Analytic Hierarchy Process/Shannon entropy combined with Gray Relation Analysis are performed to determine the most suitable locations. The results show that adding the SRM to DES as a hybrid-hydrogen energy storage system can improve the configuration flexibility, environmental performance and primary energy ratio compared with only-electrochemical hydrogen storage DES. The Pareto frontiers for four cities demonstrate a trade-off between ATC and ACE, and the ACE is decreased by 6.3% with ATC increased by 1.4% in Jinan. Generally, Guangzhou is the best suitable city, and Jinan and Wuhan rank second by two evaluation methods with 0.727 and 0.643 average scores, respectively. The current electricity and natural-gas prices are verified to be beneficial to Jinan to deploy the proposed DES, and Guangzhou and Wuhan present a better economic performance with the minimum ATC decreased by 11.4% and 6.58% under ideal energy prices. The results can provide useful information for hybrid-hydrogen energy storage application to DES in different climate regions.

Investigation on Minimum Ventilation, Heating, and Energy Consumption of Pig Buildings in China during Winter

Ventilation and heating can be necessary for pig production during winter in China. However, it is challenging to balance the ventilation rate and heat loss due to the ventilation. Therefore, it is essential to design the minimum ventilation and heating load properly in order to reduce energy loss. In this paper, a VBA (Visual Basic for Applications) model based on energy balance is established. Meteorological data, pig body masses, outdoor temperatures, feeding densities, and building envelope thermal insulance factors were involved in the model. A model pig house with a length and width of 110 m × 15 m was used to investigate the ventilation, heating time, load, and power consumption in different climate zones, i.e., Changchun, Beijing, Nanning, Wuhan, and Guiyang, representing five major climate regions in China. Based on the simulation results, the models of minimum ventilation and heating load were fitted. The results showed that there is a logarithmic relationship between the minimum ventilation volume and body mass, R2 = 0.9673. The R2 of heating load models for nursery pigs and fattening pigs were 0.966 and 0.963, respectively, considering the feeding area, the outside temperature, the body masses of the nursery and fattening pigs, and the thermal insulance factor of the enclosure. The heating requirements of commercial pig houses within the same building envelope followed the trend in Changchun > Beijing > Guiyang > Wuhan > Nanning. Increasing the building envelope’s thermal insulance factor or using precision heating could reduce the pig house’s power consumption. The analysis of the heating load and energy consumption of winter pig houses in various climate regions provided a reference for precise environmental control and the selection of building thermal insulance factors in China.

Multi-objective optimization of building envelope in different climate zones in China based on BP-NSGA-Ⅱ under the future climate

Global warming has an impact on building performance, and it is very important to explore the optimization of building performance under future climate change conditions. The study generates 2050s typical meteorological year (TMY) data of different cities (Harbin, Beijing, Shanghai, Shenzhen) representing the future climate. Taking energy consumption, thermal comfort, and initial investment cost as the objective function, the Back Propagation (BP) neural network and non-dominated sorting genetic algorithm (NSGA-Ⅱ) were used to optimize the key parameters of the building envelope of representative cities in different climate regions of China and to obtain the Pareto curve. The final solution is obtained by the weighted sum method (WSM). The results show that, except for the type of windows, the optimal configuration of the building envelope in each city is different. Compared with the results of reference buildings, the final results of each city reduces energy consumption by 14.5~24.0 % and improves thermal comfort by 23.8~34 % when the initial investment cost increases by 27.0~35.3 %. The method proposed in this paper has reference significance for the optimization of building envelope in different climatic regions of China under the future climate.