Different Climates In China Research Articles

Building Energy Efficiency Enhancement through Thermochromic Powder-Based Temperature-Adaptive Radiative Cooling Roofs

This paper proposes a temperature-adaptive radiative cooling (TARC) coating with simple preparation, cost effectiveness, and large-scale application based on a thermochromic powder. To determine the energy efficiency of the proposed TARC coating, the heat transfer on the surface of the TARC coating was analyzed. Then, a typical two-story residential building with a roof area of 258.43 m2 was modeled using EnergyPlus. Finally, the energy-saving potential and carbon emission reduction resulting from the application of the proposed TARC roof in buildings under different climates in China were discussed. The results showed that the average solar reflectivity under visible light wavelengths (0.38–0.78 μm) decreases from 0.71 to 0.37 when the TARC coating changes from cooling mode to heating mode. Furthermore, energy consumption can be reduced by approximately 17.8–43.0 MJ/m2 and 2.0–32.6 MJ/m2 for buildings with TARC roofs compared to those with asphalt shingle roofs and passive daytime radiative cooling (PDRC) roofs, respectively. This also leads to reductions in carbon emissions of 9.4–38.0 kgCO2/m2 and 1.0–28.9 kgCO2/m2 for the buildings located in the selected cities. To enhance building energy efficiency, TARC roofs and PDRC roofs are more suitable for use on buildings located in zones with high heating demands and high cooling demands, respectively.

Influence of self-shading building form on indoor solar gains and energy performance in different climates of China

Self-shading building form has a significant influence on indoor solar gains and energy performance. This paper presented a methodology for quantifying self-shading building forms, which categorized them into three types: inclined, cantilevered, and hybrid (inclined and cantilevered). The prediction model for the quantitative indexes and solar heat gains through windows was established through regression analysis, while the influence of proposed self-shading building forms on energy performance in different climatic regions of China was evaluated and compared. Results suggest that energy-saving potential of the cantilevered type is the highest, followed by the hybrid type. Compared to the south orientation, energy-saving potentials for east and west orientations are more pronounced. As building location changes from south to north, energy-saving potential is increasingly influenced by orientation. The proposed self-shading building forms have a maximum energy-saving potential of 14%, 23%, and 22% for Jinan, Shanghai, and Guangzhou respectively. Finally, corresponding design strategies were suggested to provide guidance and assistance for designers.

Optimal Sizing of Photovoltaic/Energy Storage Hybrid Power Systems: Considering Output Characteristics and Uncertainty Factors

The integration of PV and energy storage systems (ESS) into buildings is a recent trend. By optimizing the component sizes and operation modes of PV-ESS systems, the system can better mitigate the intermittent nature of PV output. Although various methods have been proposed to optimize component size and achieve online energy management in PV-ESS systems, the optimal interconnection between them has received less attention. In order to maximize the effectiveness of systems with limited component sizes and address the impact of uncertainty on the system, an optimization framework is proposed for determining the optimal size of the PV-ESS system. The proposed framework consists of five parts: determination of optimal size, analysis of component output characteristics, system state prediction, parameter calibration of energy management strategies, and update of system components output features, and it considers uncertain factors, including climate, different components, and battery degradation caused by irregular charging and discharging, to establish the model for energy saving. To validate the results, four different climates in a year were considered. The obtained results indicate that the proposed framework can effectively achieve the optimal working state of the system, realizing a matching degree of 94.55% between the offline size optimization and online management strategy. The proposed framework’s universality and effectiveness were demonstrated through simulation analysis across four cities with different climates in China.

Evaluating the energy-saving potential of earth-air heat exchanger (EAHX) for Passivhaus standard buildings in different climates in China

The significance of constructing low-carbon buildings has been on the rise because of the growing public concern about global warming and energy prices. The Passive House (or Passivhaus) standard is widely applied throughout Europe to reduce building energy consumption. However, a large percentage of the world’s population lives in regions with not only heating demand in winter but also high cooling and dehumidification energy consumption in the hot and humid summer, such as in several regions in China. The energy consumption of buildings in these regions could not be reduced by designing or retrofitting the buildings to Passivhaus standard only, but applying appropriate low energy cooling and dehumidification technologies are also necessary. The earth-air heat exchanger (EAHX) system is a suitable solution for utilizing low-temperature soil in summer, but the performance of the EAHX system in different climates was not previously evaluated. Thus, this research aims to evaluate the energy-saving potential of the EAHX system in a multi-storey Passivhaus standard building in different cities in China with different climatic conditions. A model of the Passivhaus building with the EAHX system was developed and verified using the Passive House Institute (PHI) prediction tool, PHPP and the commercial building energy simulation tool, IES Virtual Environment (VE). The results have shown that the EAHX system achieved an annual building energy load saving of up to 12.8 kWh/m2 in regions with hot and humid summers and cold winters, such as Beijing and Shanghai. The present study shows the potential of the addition of the EAHX in enhancing the building performance to satisfy the Passivhaus standard in hot and humid climates.

Analysis of Solar Chimney Application for Building Heating and Cooling in Different Climates in China

Solar chimneys have been applied widely to various buildings under different climate conditions because of their superiority in improving the indoor environment and saving energy. A series of comparative studies of the performance of solar chimneys used in 12 cities in China under different climatic conditions was carried out. A typical solar chimney in a representative building was studied using numerical simulation and analyzed using orthogonal experiment methods. Results show that the optimal structural configuration and the significance of influencing factors of the solar chimney have strong seasonal consistency. The window-to-wall ratio and wall-to-south wall ratio are the most significant influencing factors. Among the 12 cities, the highest average indoor temperature of the case room could be decreased by 5.7°C in summer and increased by 6.7°C in winter. The cooling and heating loads could be reduced by 30%–100% and 46%–56% respectively. The research results can provide practical guidance for future passive solar technology design.

Design of a photovoltaic electric bike battery-sharing system in public transit stations

Small off-grid solar photovoltaic (PV) systems installed in small urban public space or on the roofs of urban facilities can allow PV power stored in shared EB (electric bike) batteries for using anytime and anywhere, and make almost no side effects on original functions of the system installation locations, which is an effective and economic approach for promoting solar energy utilization in cities. However, the constraints such as limited available roof in public transit stations and limitation of EB-battery charging current, will lower the utilization efficiency of PV charging power. To address this issue, this study proposed a method to design a PV EB battery-sharing (PVEBBS) system in public transit stations. From field measurements, it was found that the charging mode of “one PV array unit to one battery” is energy-efficient in PV power utilization and simple in system operation control. The proposed design method is to calculate an optimal size of PV array unit which can provide a better energy-saving effect both in PV power and AC auxiliary charging, under the condition to ensure one battery fully-charged within 6 h using PV power from one PV array unit on a day throughout a year (when solar radiation is insufficient on a day, the unfully-charged battery will be charged to a fully-charged state from AC power). A southern city (Nanning) of China was used as a case study object to demonstrate how to design a PVEBBS and determine an optimal size of PV array unit. Analysis results show that the optimal sizes of one PV array unit are between 2 m2 and 6 m2 for the six cites (Lhasa, Kunming, Beijing, Shanghai, Nanning, Chongqing) with different climates in China.

Identification of Health Effects of Complex Air Pollution in China.

After the Chinese government introduced a series of policies to strengthen the control of air pollution, the concentration of particulate matter has decreased, but the concentration of ozone has increased, and the problem of complex air pollution still exists, posing a serious threat to public health. Therefore, disentangling the health effect of multi-pollutants has been a long-discussed challenge in China. To evaluate the adverse effects of complex air pollution, a generalized additive model was used to assess the health risks of different pollution types in eight metropolises in different climates in China from 2013 to 2016. Instead of directly introducing multiple pollutant concentrations, we integrated the concentration levels of PM2.5, NO2, and O3 into a set of predictors by grouping methods and divided air pollution into three high single-pollutant types and four high multi-pollutant types to calculate mortality risk in different types. The comprehensive results showed that the impact of high multi-pollutant types on mortality risk was greater than that of high single-pollutant types. Throughout the study period, the high multi-pollutant type with high PM2.5, NO2, and O3 and the high multi-pollutant type with high PM2.5 and NO2 were more associated with death, and the highest RRs were 1.129 (1.080, 1.181) and 1.089 (1.066, 1.113), respectively. In addition, the pollution types that most threaten people are different in different cities. These differences may be related to different pollution conditions, pollutant composition, and indoor–outdoor activity patterns in different cities. Seasonally, the risk of complex air pollution is greater in most cities in the warm season than in the cold season. This may be caused by the modifying effects of high temperature on pollutants in addition to different indoor–outdoor activity patterns in different seasons. The results also show that calculating the effect of individual air pollutants separately and adding them together may lead to an overestimation of the combined effect. It further highlights the urgency and need for air pollution health research to move towards a multi-pollutant approach that considers air pollution as a whole in the context of atmospheric abatement and global warming.

Stormwater retention performance of green roofs with various configurations in different climatic zones.

Green roof stormwater retention performance is fundamentally related to design configurations and climates. Efficient tools for assessing stormwater retention performance of green roofs with various configurations in different climates are highly desirable for practical applications. In this study, a hydrological model which can be used to simulate dynamic changes in moisture content and evapotranspiration of green roofs is developed and tested (with average Nash-Sutcliffe Efficiency of 0.8197 for calibration and 0.8252 for verification) using monitoring data (2018-2019) of four green roofs with various configurations. The model is applied to simulate long-term (1970-2018) moisture content, actual evapotranspiration, and retention performance of green roofs in eight cities across different climates of China. Green roofs built with engineered soil and Portulaca grandiflora show the largest evapotranspiration and thus provide the largest stormwater retention rates (Rr), while green roofs with light growing medium and Sedum lineare show the lowest evapotranspiration and Rr. Rr of green roofs increases as climate changes from humid to arid. Green roofs at Guangzhou (humid climate) provide the lowest Rr (28%±3%) caused by plenty of rainfall (1827mm), while green roofs at Urumqi (desert climate) show the lowest mean annual actual evapotranspiration (167-269mm) but provide the largest Rr (84%±5%) as a result of the lowest annual rainfall (282mm). The results highlight that stormwater retention performance of green roofs could be enhanced through configuration optimization. However, a limiting factor in improving green roofs water retention rates may be the peculiarity of local climatic conditions.

Performance analysis of different air conditioning systems in apartment buildings under different climates in China

Liquid desiccant air conditioning (LDAC) system and solid desiccant air conditioning (SDAC) system have strong dehumidification capability. To evaluate and compare their feasibility and applicability, performance analysis of these two air conditioning systems and the conventional split air conditioning (AC) system in apartment buildings was conducted under different climates in China. Results show that SDAC system has the best temperature control and LDAC system has the best relative humidity control with the same requirements. In hot and humid cities, 45% relative humidity can be reached and stably maintained by LDAC system in summer when the relative humidity set point is 50%. While for apartment building controlled by split AC system and SDAC system, indoor air relative humidity could even reach 65% in summer. LDAC system is most efficient in hot and humid cities. In cold and dry cities, split AC system performs better than other two systems. Moreover, a dividing line between areas where LDAC system is more efficient and areas where split AC system is more efficient is developed in this paper. Areas where LDAC system is more efficient are mainly distributed in the coastal areas, Yangtze River basin and south of the Yangtze River.

A method for the generation of typical meteorological year data using ensemble empirical mode decomposition for different climates of China and performance comparison analysis

The typical meteorological year (TMY) is one of the key factors to accurately estimate building energy consumption and analysis indoor environment. Therefore, high-performance TMY is crucial. A new TMY generation method based on empirical mode decomposition (EEMD) in this paper was put forward for different climates of China. The results show that the proposed method effectively improves the representation of TMY compared with traditional methods, especially for solar radiation and wind speed data. Finally, this paper analyzed the impact of TMY using EEMD on the results of building environmental analysis. The simulation result has showed that the TMY generated by the EEMD effectively reduced the relative average deviation for the estimation of the building energy consumption compared with other methods. This study further uses the method to obtain the daily weather database for selected cities in different climate regions of China.

Evaluation and comparison of precipitation estimates and hydrologic utility of CHIRPS, TRMM 3B42 V7 and PERSIANN-CDR products in various climate regimes

Evaluation of satellite-based quantitative precipitation estimates (QPEs) with reliable and independent ground-based measurements is important for both product developers and users. Here, we present a comprehensive evaluation on 3 high-resolution QPEs, namely, the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), the latest non-real-time post-processing version of Tropical Rainfall Measuring Mission (TRMM 3B42 V7), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), in 3 basins with different climates in China. The accuracy of 3 QPEs in reproducing the spatial extent of daily and monthly precipitation (PR) as well as extreme PR indices was evaluated. Two simulation scenarios were utilized to evaluate the efficiency of hydrologic events forecasting of these 3 QPEs quantitatively. The results indicated that the 3 QPEs generally show high accuracy in estimating monthly PR in 3 basins, among which TRMM 3B42 V7 performs best (coefficient of determination R2 < 0.94) followed by CHIRPS (R2 < 0.91). However, all QPEs tend to overestimate daily PR of 3 basins, resulting in low accuracy at the daily scale (R2 < 0.35). For estimation of the extreme PR indices, the 3 QPEs show large differences in the spatio-temporal accuracy, but all with better performance in humid (R2 < 0.86) than arid (R2 < 0.7) basins. Similarly, all 3 QPEs show better performance in simulating streamflow in humid than arid basins. TRMM 3B42 V7 (Nash-Sutcliffe coefficient of efficiency, NSCE < 0.96) and CHIRPS (NSCE < 0.9) perform better in simulating streamflow in humid basins than PERSIANN-CDR (NSCE < 0.88), while PERSIANN-CDR performs best in arid basins (NSCE < 0.67). However, 3 QPEs mostly underestimate peak flow and overestimate soil moisture in all basins, suggesting that the necessity of improving hydrologic efficiency for all of them.

Meta-analysis of Microbial Communities in the Activated Sludge of Wastewater Treatment Plants Under Different Climate Types

Microbial communities in wastewater treatment plants(WWTPs) are very important for water purification in the context of public drinking water safety and environmental health. Therefore, it is necessary to explore the trends in microbial community structure and diversity in sewage treatment plants and their main environmental impact factors under different climates in China. Based on high-throughput sequencing techniques, a meta-analysis was conducted to screen the 16S rRNA genes in an open database. We analyzed the trends in microbial community structure and diversity in WWTPs under three climate types(Dwa, Cfa, and Cwa) in China. We then constructed cohesion models to examine the core microbial taxa and their interactions within the communities. We also used a piecewise structural equation model(PSEM) to examine the effects of different climate types on microbial community structure. The three climate types significantly affected the structure and diversity of the microbial communities, with patterns correlated with influent pH, mixed liquid temperature, conductivity, and nitrogen concentrations(P<0.05). Based on the PSEM analysis, the β-diversity of the microbial communities was directly correlated with latitude, while α-diversity was indirectly correlated with latitude through conductivity and water temperature. Based on the cohesion modeling, microbial community stability was the highest under Dwa climate followed by the Cfa climate. This could be explained by a small subset of highly connected taxa capable of withstanding disturbance, indicating an important stability role. In contrast, the stability of the microbial communities under the Cwa climate was low, and no species with strong negative cohesion were observed. Overall, the structure, diversity, and stability of microbial community in WWTPs were found to be sensitive to climate, and the responsive mechanisms of α-diversity and β-diversity with respect to latitude were distinct.

Extreme learning machine for reference crop evapotranspiration estimation: Model optimization and spatiotemporal assessment across different climates in China

Accurate estimation of reference crop evapotranspiration (ETo) is critical for quantifying crop water requirements and irrigation schedule design. This study proposed two optimization methods, i.e., particle swarm optimization (PSO) and genetic algorithm (GA), to determine the input weights and hidden biases of extreme learning machine (ELM), and developed two novel hybrid GA-ELM and PSO-ELM models for ETo estimations with limited input data. A temporal and spatial scanning strategy that can avoid misleading or partially valid results was applied to train and test the models, using daily climatic data during 1994–2016 from 96 meteorological stations across various climates of China. The results revealed that GA-ELM and PSO-ELM could quantify ETo on daily, monthly, and annual time scales, with GA-ELM (with model efficiency ranging from 0.80 to 0.97) performing better than PSO-ELM (with model efficiency ranging from 0.70 to 0.93) in all climates. The temperature-based GA-ELM only with air temperature data as forcing input has relatively accurate ETo estimates across different environments, and can be recommended as an alternative method when radiation data are not available. Spatial assessment reveals that the machine learning models trained by external data also offer accurate ETo estimates, confirming the applicability of models for ETo estimations elsewhere without the model training or when local data for model training are unavailable. Overall, the GA-ELM model provides accurate ETo estimates on various time scales and exhibits superior performance than the PSO-ELM model, and thus can be recommended for ETo estimations using limited data as model forcing in different climates of China. Our work presents a novel method for accurate ETo estimations with limited data, which has practical implications in regional agricultural water management.

The Visualization Study on Research Progress of Thermal Comfort for Indoor Environment Based on CiteSpace

The papers published from 2000 to 2019 in CNKI database were collected. CiteSpace software was used as visualization tool to analyze the research progress of indoor thermal comfort in China based on those papers. The results showed that the period, from 2000 to 2004, was the initial developing stage of researches on thermal comfort in China. From 2005 to 2019, there were two research fields developed of thermal comfort. One was to study the adaptive thermal comfort in different climates in China and try to promote the prediction model according to different climate characteristics. Another research keyword was to expand the field of thermal comfort research, e.g. studying the thermal comfort of the elderly and children, and studying rural houses and tall space buildings. Literature analysis also disclosed that “thermal sensation” and “thermal environment” were the core keywords in the field of thermal comfort from 2000 to 2019. According to research methods, “numerical simulation”, “field research” and “questionnaire” were commonly used. Recently, Thermal comfort under natural ventilation became the hot keyword in thermal comfort research. From 2011 to 2019, thermal comfort of rural housing had become a hot topic.

不同气候条件下桫椤在中国的潜在适生区分布

不同气候条件下桫椤在中国的潜在适生区分布

Analysis of the degradation of encapsulant materials used in photovoltaic modules exposed to different climates in China

This work investigates the degradation mechanisms of polymeric encapsulants used in photovoltaic modules. Two groups of photovoltaic modules, which were exposed to two typical climates, namely a hot-humid climate and a hot-dry climate, were investigated. Both types of modules were produced by Siemens Solar in 1992 and exposed to outdoor sunlight and the climate for 18 years. The results show that, depending on the climate conditions, the polymeric encapsulant materials exhibit different degradation mechanisms regarding the optical, chemical and morphological properties. The aging of polymeric encapsulant materials is, on the one hand, related to the working environment (moisture, sunlight intensity, UV content and temperature) and, on the other hand, also the conditions are not uniform across the modules: moisture ingress is more important at the edge of the module, oxygen diffusion is more important between cells than at the center of a cell, the module temperature is not uniform.

Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China

Day of the year-based (DYB) models can achieve great accuracy in daily global solar radiation estimation without specific meteorological elements. Many empirical models (EMs) and machine learning (ML) methods have been proposed for DYB models. However, the number of their comparative studies based on diverse climates is limited. In this study, a grand total of 14 DYB models are established to estimate daily global solar radiation based on measured data from 1994 to 2015 at 35 meteorological stations in six climate zones of China. Detailed tasks are as follows: (1) Seven EMs and seven ML models are trained for solar radiation estimation. (2) A new EM and two novel ML models are proposed, i.e. hybrid 3rd order polynomial and sine wave model, adaptive neuro-fuzzy inference system (ANFIS) optimized by chaotic firefly algorithm (CFA) and ANFIS optimized by whale optimization algorithm with simulated annealing and roulette wheel selection (WOASAR). (3) Four statistical indicators are utilized to compare those models, and the best model for each station is decided. (4) We discuss the model parameters and climate variances of six specific stations in different climate zones. The comparison results demonstrate superb estimation precision and climate adaptability of the newly proposed models.

Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China

Accurate estimation of reference evapotranspiration (ET0) is of great importance for the regional water resources planning and irrigation scheduling design. The FAO-56 Penman-Monteith model is recommended as the reference model to predict ET0, but its application is commonly restricted by lack of complete meteorological data at many worldwide locations. This study evaluated the potential of machine learning models, particularly four relatively simple tree-based assemble algorithms (i.e. random forest (RF), M5 model tree (M5Tree), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost)), for estimating daily ET0 with limited meteorological data using a K-fold cross-validation method. For assessment of the tree-based models in terms of prediction accuracy, stability and computational costs, these models were further compared with their corresponding support vector machine (SVM) and extreme learning machine (ELM) models. Four input combinations of daily maximum and maximum temperature (Tmax and Tmin), relative humidity (Hr), wind speed (U2), global and extra-terrestrial solar radiation (Rs and Ra) with Tmax, Tmin and Ra as the base dataset were considered using meteorological data during 1961–2010 from eight representative weather stations in different climates of China. The results showed that, when lack of complete meteorological data, the machine learning models using Tmax, Tmin, Hr, U2 and Ra obtained satisfactory ET0 estimates in the temperate continental, mountain plateau and temperate monsoon zones of China (RMSE < 0.5 mm d−1). However, models with three input parameters of Tmax, Tmin and Rs were superior for daily ET0 prediction in the tropical and subtropical zones. The ELM and SVM models offered the best combination of prediction accuracy and stability. The simple tree-based XGBoost and GBDT models showed comparable accuracy and stability to the SVM and ELM models, but exhibited much less computational costs. Considering the complexity level, prediction accuracy, stability and computational costs of the studied models, the XGBoost and GBDT models have been recommended for daily ET0 estimation in different climatic zones of China and maybe elsewhere with similar climates around the world.

Optimal Tilt Angle and Orientation of Photovoltaic Modules Using HS Algorithm in Different Climates of China

Solar energy technologies play an important role in shaping a sustainable energy future, and generating clean, renewable, and widely distributed energy sources. This paper determines the optimum tilt angle and optimum azimuth angle of photovoltaic (PV) panels, employing the harmony search (HS) meta-heuristic algorithm. In this study, the ergodic method is first conducted to obtain the optimum tilt angle and the optimum azimuth angle in several cities of China based on the model of Julian dating. Next, the HS algorithm is applied to search for the optimum solution. The purpose of this research is to maximize the extraterrestrial radiation on the collector surface for a specific period. The sun’s position is predicted by the proposed model at different times, and then solar radiation is obtained on various inclined planes with different orientations in each city. The performance of the HS method is compared with that of the ergodic method and other optimization algorithms. The results demonstrate that the tilt angle should be changed once a month, and the best orientation is usually due south in the selected cities. In addition, the HS algorithm is a practical and reliable alternative for estimating the optimum tilt angle and optimum azimuth angle of PV panels.

Comparison of energy performance between PV double skin facades and PV insulating glass units

Building-integrated photovoltaic (BIPV) windows provide the benefits of generating electricity, reducing building cooling and heating energy consumption, and efficiently utilizing daylight simultaneously. In this paper, the overall energy performance of a PV double skin façade (PV-DSF) and a PV insulating glass unit (PV-IGU) is studied through comparative experiments on a test rig in Hong Kong. The PV-DSF means ventilated PV-DSF by default, if not special mentioned. It is found that the average solar heat gain coefficients (SHGCs) of the PV-DSF and the PV-IGU are 0.152 and 0.238, while the U-values are 2.535W/m2K and 2.281W/m2K. The results indicate that the PV-DSF has better performance than PV-IGU in reducing solar heat gains, while it has worse performance regarding thermal insulation. With a lower PV module temperature, the energy conversion efficiency of PV-DSF is 1.8% better than PV-IGU. Simulation models for the PV-DSF and the PV-IGU are developed and validated against experimental data. Using the validated models, the overall energy performances of PV-DSF and PV-IGU in five different climates of China are investigated. The results show that the average energy saving potential of the PV-DSF and the PV-IGU are 28.4% and 30%, respectively, compared to the commonly used insulating glass window in five different climates. On average, the performance of PV-IGU was 2% better performance than the ventilated PV-DSF in the five representative cities. However, if an appropriate ventilation control scheme was adopted, PV-DSF can have a much better performance than the PV-IGU. The models developed in this study can be used for selecting suitable PV windows in the design process, and the results achieved can be used as a guideline for utilizing PV windows in different climates.