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In recent decades, environmental problems have enforced designers to estimate the level of environmental emission of building design and reduce their environmental impact. On the premise of ensuring indoor comfort, the cost-effectiveness of solutions for reducing the building’s greenhouse gas has become a critical issue. Based on the Life Cycle Assessment (LCA), this paper establishes a building performance trade-off framework for indoor thermal comfort, economics, and environmental implication. This framework consists of four parts: the establishment of the optimization model; sensitivity analysis; obtain of Pareto frontier solutions, and decision-making analysis. Optimization variables involve envelope type and some envelope physical parameters. The “design variables-building performances” database is obtained by using building simulation software combined with the Latin hypercube sampling algorithm. Sensitivity analysis is used to extract the key factors affecting building performance. The designer can prioritize these key factors and it can reduce the uncertainty of building performance. A multi-objective optimization method coupling Gradient Boosted Decision Tree (GBDT) and non-dominated sorting genetic (NSGA-II) algorithm is proposed to seek the trade-off between three performances (obtain Pareto frontier solutions). The Pareto solution provides a more comprehensive reference for the preferences of different stakeholders, and the set of alternative solutions is further shrunk. Finally, take a specific residential building in China’s cold climate zone as a showcase of the trade-off framework. According to the obtained Pareto frontier solution, the solution set is shrunk to a certain range, and the distribution ranges of Life Cycle Costs, the greenhouse gas emissions, and the annual thermal discomfort hour ratio are 122.3–137.1 USD/m2, 15.6–44.8 kg CO2/m2, and 19.1–25.2%, respectively. The trade-off framework adopts the order of objective Pareto optimal and then subjective preference selection, narrowing the scope of alternatives for designers and saving time-cost of decision-making.

Phase change material (PCM) can be integrated into floor heating system to store excess heat, releasing it when needed, thus effectively reducing the mismatch between energy supply and demand. In this study, a double pipe PCM floor heating system with three independent heating modules was proposed for clean energy heating in rural China. A 26-day experimental study was conducted to study the operation effect of the system under four different strategies. Strategy 1: partial power operation strategy; Strategy 2: daytime energy storage strategy; Strategy 3: nocturnal energy storage strategy; and Strategy 4: phased energy storage strategy. In the early stage of heating, the average temperature of experiment room was 18.9 °C and the daily temperature fluctuation was controlled within 2.1 °C, which showed an effective guarantee of indoor comfort under Strategy 1. In the late stage of heating, the differences in indoor temperature fluctuations could be caused by changes in outdoor climate and operation strategies. And operating strategy had less impact on indoor temperature distribution. Moreover, the experiment results revealed that the system could provide a comfortable indoor thermal environment while adjusting operational strategies. And the 3 °C average daily temperature fluctuation confirmed the feasibility of creating a comfortable environment with gentle temperature fluctuations via the PCM floor intermittent heating.

In recent years, the systematic application of phase change materials (PCM) is continuously developing. In this paper, an innovative PCM ceiling coupled with earth-air heat exchanger (EAHE) cooling system was proposed for building cooling. The system aimed to combine the cooling capacity of soil and the energy storage capacity of PCM, thus improving the indoor thermal environment. Performance of the system was tested by experimental method while data analysis focused on the indoor side. To research the effect of cold storage time on the performance of the system, two different operation strategies were adopted for comparison: 8-h cold storage strategy and 12-h cold storage strategy. Moreover, a control group was set up to observe the performance of the system on indoor temperature under the same weather conditions. The result showed that the experimental room in which we installed this system could reduce peak temperature by 2.1 &deg, C under 8-h timed cold storage strategy and 2.7 &deg, C under 12-h timed cold storage strategy. What is more, under the two operation strategies, temperature and heat flux of the PCM ceiling had similar distribution characteristics. Different strategies mainly affected the sustainability of the system and phase transition efficiency of the PCM ceiling.

In China, relevant standards about building energy efficiency and green buildings have resulted in corresponding requirements for the envelope thermal performance. However, improvement of the thermal performance of the envelope is accompanied by an increase of the environmental impact and cost. This study quantitatively analyzed the thermal performance, environmental impact, and cost of the green building envelope in cold areas and established a common practice database, as well as a multi-index evaluation model. The results show that the best thermal performance improvements are 40% in residential buildings and 30% in public buildings, respectively, based on the relevant standards. The exterior walls and windows have the greatest impact on building heat consumption. There is no significant correlation between the heat consumption and the comprehensive heat transfer coefficient of 10 green buildings. Therefore, the comprehensive heat loss coefficient is corrected. The verification results show that all errors except project 10 are within 15%. Additionally, the projects with balanced thermal performance improvement of exterior walls and windows showed a better performance. Finally, the best combination of residential building envelopes in the cold area was selected, using the evaluation model and quantitative database to calculate the comprehensive score.

Due to the increasingly serious shortage of resources, new energy-using technologies and new energy sources have received widespread social attention. CCHP system, as an efficient and stable way of energy utilization, has wide application prospect, which can achieve energy ladder utilization and multi-energy complementary. In this study, a correlation model for configuration and operation optimization based on a bi-level model construction method is established for a CCHP coupled multi-energy system. A solution method based on sequence control configuration methods, sequential quadratic programming algorithm and feedback correction mechanism is proposed for the optimization model and a CCHP coupling multi-energy system optimization tool is developed using MATLAB. Then taking a commercial area in Tianjin as a specific case, after calculating, the optimized system has significant economic benefit, whose total annual operating cost can be reduced by 36.2% compared with the traditional sub-system. The study found an effective method for the design optimization of CCHP coupled multi-energy system, and gives a new idea for solving similar optimization problems.

With the gradual increase of building energy consumption in recent years, the application of phase change energy storage materials in buildings has become the focus of researches. Phase change material (PCM) embedded in the envelope can increase the building inertia to keep the temperature stable. It can also store energy and release energy when needed. In this paper, the system of PCM radiant floor combined with PCM wall attached with sunspace was established. The thermal characteristics and the energy consumption of the building are studied through analyzing the data from the experimental investigation including the temperature and the heat flux. Two different buildings were built between the PCM room and the conventional room with the same dimension and building. The main test conditions are divided into passive and active control stages by comparing the air temperatures, envelope temperatures, phase transition temperatures, heat fluxes and power consumptions between the two rooms. It is concluded that the average room temperature of the experimental room is 7.15 °C higher than that of the conventional room in the passive energy storage stage. While the average energy-saving rate of PCM room was 54.27% compared with the conventional room in the active control stage.

Due to the latent heat performance of the phase change materials during the phase change process, if they are used in a solar energy storage tank, the heat storage efficiency and the heat release capacity of the tank can be effectively improved. In this study, a water storage tank containing two types of phase change materials with different melting points was proposed. Then, based on simulation calculation, the tank with the highest performance of thermal storage was obtained by changing the thickness of the phase change materials. Finally, three water storage tanks with different contents of phase change materials, respectively equipped with a circulating water system were established; the time of storing and releasing heat as well as the total amount of heat release were measured at releasing heat temperature of 40 and 45 °C. Results show that the presence of the phase change materials with different melting points can significantly shorten the time of storing heat. An increase in the contents of phase change materials in the tank leads to evident growth of heat release. At 40 °C, the amount of heat release are 21.35, 27.01 and 29.78 MJ at the contents of 0, 11.12% and 19.17% respectively, which are larger than those at 45 °C. The storage tank can reduce the use of electric heating devices and can be used in solar hot water systems.

The unique geographical location of Hainan makes its climate characteristics different from inland areas in China. The thermal comfort of Hainan also owes its uniqueness to its tropical island climate. In the past decades, there have been very few studies on thermal comfort of the residents in tropical island areas in China. A thermal environment test for different types of buildings in Hainan and a thermal comfort field investigation of 1944 subjects were conducted over a period of about two months. The results of the survey data show that a high humidity environment did not have a significant impact on human comfort. The neutral temperature for the residents in tropical island areas was 26.1 °C, and the acceptable temperature range of thermal comfort was from 23.1 °C to 29.1 °C. Residents living in tropical island areas showed higher heat resistance capacity, but lower cold tolerance than predicted. The neutral temperature for females (26.3 °C) was higher than for males (25.8 °C). Additionally, females were more sensitive to air temperature than males. The research conclusions can play a guiding role in the thermal environment design of green buildings in Hainan Province.

Solar energy is clean and renewable energy; phase change material (PCM), used in buildings, can store the surplus heat and release when needed to save energy in winter. This paper establishes a new model of PCM floor coupled with solar water heating system (PFCSS). A module of PFCSS building was established in TRNSYS. The module was validated by a full-scale experiment. Two experiment buildings, a reference building and a PFCSS building, were set in Ninghe County of Tianjin, China. The experiment lasted for 15 days in February. Two mathematical methods, MRE (mean relative error) and Bland-Altman consistency analysis, are used to evaluate the accuracy of the model. In the comparison of the experiment data and simulation data of PFCSS building, MRE is 0.6% and the result of Bland-Altman consistency analysis is 95.1% (95% confidence interval −1.94–1.46). A model of the control building with ordinary radiant floor was set up to compare the differences in its temperature performance in relation to that of the PFCSS building. PFCSS building’s temperature fluctuation is smaller and the heating process is longer than control building. If the indoor temperature is maintained at 20 °C, the PFCSS building can save 5.87% on the energy consumption, when compared with the control building. Based on these two existing models, the influences of PCM’s thermal physical parameters on the indoor air temperature were analyzed by simulation.

Nowadays, the research and design of the DC-DC conversion system has become more and more popular in the application of photovoltaic generation into the medium voltage DC (MVDC)grids. A novel high power modular isolated DC-DC converter with high step-up ratio is proposed as a module of the DC-DC conversion system in this paper. Based on the input parallel and output series (IPOS)connections of the proposed modular converters, the system is easy to achieve high capacity and high voltage. A two-stage structure is employed in the proposed converter. The interleaved boost topology is applied to the front-stage, which realizes MPPT for PV arrays with low input current ripples. The full-bridge LLC topology is employed as the back-stage, which lowers power losses by the implementation of soft-switching. The voltage gain of the proposed converter is derived in detail. The theoretical analysis and control strategy are studied in depth. Simulation and experiment verify the feasibility and effectiveness of the proposed converter.

The use of exergy analysis provides theoretical guidance for the cascaded latent heat storage system (CLHSS). However, the exergy analysis of the CLHSS charging–discharging processes is imperfect with two problems to be solved. One is the lack of exergy flow analysis, the other is the inaccurate expressions of the overall charging–discharging processes exergy efficiency. This paper aims to solve the above two problems. First, the exergy flow of the CLHSS charging–discharging processes was revealed, composed of one or more of the three exergy flow paths. Second, the overall exergy efficiency was derived by determining exergy produced and consumed. Only by satisfying the constraint that the exergy change of each PCM is equal to zero, the product of exergy efficiencies of charging and discharging processes can represent the overall exergy efficiency. On this basis, previous models that used the product of exergy efficiencies were modified by adding constraints. Compared to models without constraints, the model with constraints has more accurate and complete optimization results, which is conducive to the CLHSS stable operation. Finally, the model with constraints was adopted to guide the application of the CLHSS in the solar power tower.

Evaluation of actual zero energy buildings (ZEBs) performance and identification of its regional characteristics are of great significance for similar future projects. Based on more than 400 cases in cold regions, this study compared the post-evaluation and drivers of ZEBs from China, the US and the European Union (EU). Results found that ZEB definition, energy drivers, standard, regional policies, technology adoption and their adoption ratio determine the energy performance of cases. Not all EU and China cases reach the net-zero energy target; however, most ZEBs in the US do. ZEBs in cold regions adopted multiple technologies to achieve high energy-efficiency. The adoption ratio of passive technologies is higher than that of active technologies, especially in China. The active technologies in cold regions are mainly seen in the application of advanced HVAC systems. Such application of renewable technologies exhibits strong regional characteristics; for example, the EU’s carbon emission reduction policies promoted the use of biomass-based technologies. The analysis also found that energy policies greatly enhance the development of ZEBs. Overall, achieving zero energy is a process, not an endpoint and many projects need further refinement after occupancy.

Accurate heating consumption prediction is the key to achieve on-demand heating in a heating system. A prediction model needs to go through the steps of data cleaning, model establishment and effect evaluation before being applied. Currently, there is no universal supplement method for missing data, and the evaluation method is based on the error between the predicted and the actual value, if the actual value is not on-demand value, the method cannot accurately evaluate the prediction model which may lead to the poor applicability of the model. In this paper, firstly, the distribution characteristics of heating consumption data are analyzed, and the mean method based on eighth fractile judgment is proposed to supplement the missing data. Secondly, the research method and empirical formula of solar radiation intensity and the percentage for heating consumption reduction is determined by analyzing the change of indoor temperature. Finally, the application effects of SVM, MLR and LR in heating consumption prediction are evaluated from the perspective of on-demand heating, for the daily prediction, LR, SVM and MLR can be well used when the average outdoor temperature fluctuation is small, and the applicability of SVM and MLR are poor when the fluctuation is large, for the hourly prediction, the applicability of SVM and MLR are poor due to influence of improper manual regulation, and the daily heating consumption distribution method is proposed. In summary, the methods of data cleaning, daily prediction, hourly prediction and model modification are proposed, which have a strong applicability and provide theoretical support for the practical application of heating consumption prediction in heating substation.

In the design stage of green buildings, there is a lack of a set of systematic performance-based evaluation methods for high-performance cold sources to evaluate whether the selected cold sources match the load characteristics of buildings, which leads to inappropriate selection of cold sources in the design stage. Due to this problem, this study proposes a multi-dimensional performance-based evaluation method. Data were collected from a public building in the cold region of China, and a set of calculation tools were developed to simulate the actual operation performance of cold source units. Considering its energy and economic efficiency, the multi-dimensional comprehensive evaluation index is put forward. Finally, taking a green building located in Tianjin, China as an example, the feasibility analysis of the multi-dimensional performance-based evaluation method is carried out. Results indicate that: 1) the prediction error of hourly performance coefficient prediction model based on Fuzzy clustering and Back-Propagation (BP) neural network algorithm is less than 10%, which can accurately predict the actual operation energy efficiency of cold source; 2) compared with the actual cold source, the improved cold source of the case building has the seasonal power consumption decreased by 34.0%, and the average load rate increased by 16.9%, which proves the feasibility of multidimensional performance-based evaluation method. The method proposed in this study can provide reference and help for the selection and performance evaluation of cold source units in green buildings at the design stage.

The adoption of natural ventilation is prevalent in semi-open hotel lobbies on the tropical island of Hainan, China. This paper presents the results of a thermal comfort study of such applications in ten luxury hotels in this region. Significant differences were found between the local residents and tourists in their thermal satisfaction and between the males and females in their thermal comfort. Moreover, the thermal satisfaction levels of the local residents and tourists showed significant correlations with the wind speed and air temperature considering the wind speed compensation, with regard to genders, the males’ thermal comfort levels showed significant correlations with the wind speed and air temperature considering the wind speed compensation, while the females’ thermal comfort levels showed significant correlations with air temperature, wind speed and the air temperature considering the wind speed compensation. And results of ordinal logistic regression show that the tourists are more sensitive to the wind speed and air temperature considering the wind speed compensation than the local residents, and the females are more sensitive to the air temperature, wind speed and air temperature considering the wind speed compensation than the males.

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.

Phase change materials (PCM) and cool materials used in construction field cannot only effectively reduce buildings energy consumption, but also improve indoor thermal comfort and mitigate the urban heat island (UHI) condition. A novel energy efficiency roof coupled with PCM and cool materials has been conducted a field test in Tianjin of China. A kind of PCM-eutectic mixture was prepared, which was encapsulated in the polyethylene of raised temperature resistance (PE-RT) pipe, for using in building roofs. Thermal performances such as the roof surface temperatures and heat fluxes have been studied. The results indicated that, compared with reference rooms, the novel energy efficiency roof has shown good effect on decreasing the peaks of temperature and heat flux, which means that the influence of the outdoor circumstance on indoor environment has been weakened. In conclusion, it has been proved that the novel roof has good thermal insulation effect and energy efficiency potential.

A prediction model, which can be established with a small number of operating data, is the key to realize the on-demand heating as soon as possible for new heating stations. The existing physics-based methods with high accuracy are based on detailed building thermal parameters, which are difficult to obtain and have poor applicability. Data-driven methods have high prediction accuracy, but they require a large amount of historical data for model training. In this paper, a novel prediction model based on building thermal inertia was proposed, called κ -model. Analysis and comparison were conducted between κ -model, support vector machine (SVM), extreme gradient boosting (XGBoost) and Multiple linear regression (MLR) with different conditions for secondary average temperature prediction. Results showed that κ -model can explore the heating characteristics from 1/2 days of operational data, and predict secondary average temperature with high precision by one-step or multi-step not only hourly but also daily. While XGBoost and MLR required the operational data of 15/60 days for the hourly/daily prediction model training, the accuracy of multi-step prediction was higher than that of one-step prediction for XGBoost, and MLR was not suitable for daily multi-step prediction due to error accumulation. SVM required the training data of 15/75 days for the hourly/daily prediction, and one-step prediction had higher accuracy than multi-step prediction. The above mean that in practical engineering, only κ -model can be applied for hourly prediction from November 16 to November 29, and for daily prediction from November 17 to January 16 at the beginning of the heating season.

Climatic zoning is an important tool in building energy policy and regulations, smaller and more homogeneous climate zones could help enhance the rationality of energy policymaking. In most countries, climate zoning is based on simplified climate models, and it is difficult to accurately represent the energy demand characteristics of buildings. Therefore, this paper proposes a novel climate subdivision method that tackles the complex relations between climate and building energy-efficient, which move from traditional weather-based approaches to a performance-based approach. Integrate the uncertainty analysis method and the building dynamic performance simulation to extract a novel statistically significant index as the basis for classification. The K-means clustering method and discriminant analysis method are used to obtain and verify the clustering results, respectively. Furthermore, the comparison with clustering results based on degree days confirms the superiority of the new method proposed in this paper in identifying building energy demand. The method is applied to the cold climate zone of China as a showcase. The results show significant differences in building energy demand and the order of key features affecting building performance between each region in the climatic zones. The zone can be further sub-divided into four groups considering the climate factor and building feature uncertainty, and clustering results have been verified by several evaluation indicators.

Adjustment strategies including window ventilation and shading have important improvements in energy consumption, thermal and light environments, furthermore, the upper limit for improvement is affected by design parameters. However, studies incorporating adjustment strategies in the building design process are very limited. To address this research gap, we explore the effects of window ventilation and shading on building design performance from uncertainty analysis, sensitivity analysis, and multi-objective optimization. Furthermore, China’s typical climate zones are compared given climate effects. Results indicate that (1) the uncertainty of total energy demand in the severe cold climate is most affected with the uncertainty increase rate being 32.0%, the uncertainty of thermal comfort ratio in the hot summer and cold winter climate and the hot summer and warm winter climate is most affected with the uncertainty increase rate being 16.3% and 14.0%, respectively. (2) the sensitivity analysis of the thermal comfort ratio is more sensitive to adjustment strategies than to total energy demand. The severe cold climate is more vulnerable than in other climates. (3) when multi-objective optimization is performed with maximum thermal comfort and minimum total energy demand when considering adjustment strategies, the severe cold climate has the greatest energy-saving potential (38.1%) and the hot summer and cold winter climate has the largest potential to improve thermal comfort (17.6%). More importantly, the light environment is within the comfort range from the daylight glare index, the illuminance, and illuminance uniformity ratios.

In-depth knowledge of the customers and a better understanding of their heat use is the basis of accurate heating load prediction model, it is also the cornerstone for realizing the intelligent and multi-energy development of the heating system. Most of existing heating load prediction models were established mainly based on outdoor meteorological parameter, while few of them took indoor temperature and user behavior into account. This paper took the wireless on-off control system as an example, and the heat user behavior was analyzed by the open valve number. Different heat users have different heat behaviors in different time periods, which are reflected in the change of open valve number. The number of open valve was first time introduced into the heating load prediction model (Model-2) in this paper. The results showed that the prediction accuracy of Model-2 was significantly improved compared with the traditional model (Model-1) including multiple linear regression (MLR) and support vector machine (SVM). K-means algorithm was used to identify and cluster the heat user behavior. Comparing the heating load prediction accuracy of MLR and SVM before and after clustering (Model-2 and Model-3), the results showed that Model-3 had a much higher accuracy. The rank of the models was Model-3, Model-2 and Model-1 in turn, in terms of the performance coefficients for model training and testing. The Model-3 proposed in this paper provides a reliable heating load prediction method for wireless on-off control system.

In this study, a new two-dimensional model of CPRF was developed in TRNSYS. Its accuracy was validated by a full-scale experiment. The effects of key parameters of CPRF coupled with solar water heating system on indoor temperature was analyzed. The single factor analysis revealed that reducing tube pitch and solidification temperature, increasing PCM layer thickness and solar collector area could increase indoor temperature. The orthogonal test analysis indicated that tube pitch has the greatest impact on the average indoor temperature. The CPRF heating system coupled to solar energy and an electric boiler operating on valley electricity (CSSEBV) was proposed to ensure stable heating. It was optimized by GENOPT. The optimal parameters are that the tube pitch is 0.28 m, the PCM layer thickness is 0.02 m, the solar collector area is 12.5 m2 and the operating time of the electric boiler is 2h. The economy, energy saving property and environmental benefits of four systems were also compared and analyzed.

Due to reducing the reliance of buildings on fossil fuels, Passive House (PH) is receiving more and more attention. It is important that integrated optimization of passive performance by considering energy demand, cost and thermal comfort. This paper proposed a set three-stage multi-objective optimization method that combines redundancy analysis (RDA), Gradient Boosted Decision Trees (GBDT) and Non-dominated sorting genetic algorithm (NSGA-II) for PH design. The method has strong engineering applicability, by reducing the model complexity and improving efficiency. Among then, the GBDT algorithm was first applied to the passive performance optimization of buildings, which is used to build meta-models of building performance. Compared with the commonly used meta-model, the proposed models demonstrate superior robustness with the standard deviation at 0.048. The optimization results show that the energy-saving rate is about 88.2% and the improvement of thermal comfort is about 37.8% as compared to the base-case building. The economic analysis, the payback period were used to integrate initial investment and operating costs, the minimum payback period and uncomfortable level of Pareto frontier solution are 0.48 years and 13.1%, respectively. This study provides the architects rich and valuable information about the effects of the parameters on the different building performance.

In recent years, since DC grids provide higher power supply reliability, higher equipment redundancy, more adaptable power supply mode and more secure power flow control, the medium voltage DC (MVDC) grid is becoming a strategic industry that is widely developed and focused in the world. Particularly, power generation based on large-capacity photovoltaic (PV) is growing rapidly, and a corresponding power conversion system is critical for integrating large PV systems to medium voltage dc grid. In order to improve the reliability and efficiency of PV generation system, the high voltage gain and high efficiency for large capacity dc conversion are required. However, researches on this issue have been relatively few and limited so far. An in-depth analysis and study about high step-up ratio conversion system for MVDC grid connected with PV system is carried out in this paper. Specifically, the characteristics suitable for high step-up ratio conversion system in structure are discussed. According to these characteristics, the conversion systems fall into four categories by series-parallel connection scheme of power modules. Then, three conversion system solutions are identified and compared in terms of input/output performances, conversion efficiency, modulation method, control complexity, power density, reliability and hardware cost. Through thorough analysis, how to select the most suitable conversion system in various applications is also discussed.

The development of green building is an important way to solve the environmental problems of China’s construction industry. Energy conservation and energy utilization are important for the green building evaluation criteria (GBEC). The objective of this study is to evaluate the quantitative relationship between building shape parameter, envelope parameters, shading system, courtyard and the energy consumption (EC) as well as the impact on indoor thermal comfort of rural residential buildings in the hot summer and warm winter zone (HWWZ). Taking Quanzhou (Fujian Province of China) as an example, based on the field investigation, EnergyPlus is used to build the building performance model. In addition, the classical particle swarm optimization algorithm in GenOpt software is used to optimize the various factors affecting the EC. Single-objective optimization has provided guidance to the multi-dimensional optimization and regression analysis is used to find the effects of a single input variable on an output variable. Results shows that the energy saving rate of an optimized rural residence is about 26–30% corresponding to the existing rural residence. Moreover, the payback period is about 20 years. A simple case study is used to demonstrate the accuracy of the proposed optimization analysis. The optimization can be used to guide the design of new rural construction in the area and the energy saving transformation of the existing rural houses, which can help to achieve the purpose of energy saving and comfort.

Due to energy crisis and phenomenon of global warming, green buildings have been undergoing vigorous development in China, and people are paying increasing attention to the actual operational effect of green buildings rather than the design. In this paper, 12 high-star green buildings (3 LEED Gold buildings, 1 operation Two-Star building, 1 operation Three-Star building, 1 design Two-Star building, and 6 design Three-Star buildings) in the cold region and hot summer and warm winter region (HSWW region) were taken as the research objects to evaluate their indoor thermal comfort. First, from December 2016 to March 2018, this paper conducted a continuous collection of indoor objective thermal environment data of green buildings to obtain the actual thermal environment distribution, and proposed new indexes to evaluate indoor thermal environment of green buildings, namely, OTCD (Over Thermal Comfort Distance) and OTCP (Over Thermal Comfort Percentage). It was found that indoor temperature and humidity of most buildings didn't reach the standard. The new evaluation indexes clearly showed the percentage of the indoor temperature and humidity of each green building away from the comfort zone under different standards in mathematical form, which is convenient for comparison. Second, a questionnaire survey on people's subjective satisfaction of indoor thermal comfort in green buildings was carried out at the same time, wherein 1400 valid questionnaires were recovered. Based on the different evaluation results of subjective and objective thermal comfort, the operative comfort temperature/humidity and thermal/humid comfort zone of different building climate zones were obtained by cluster and linear regression analysis. The results showed that there are differences between thermal/humid comfort zone of different building climate zones and it is helpful for the research on thermal comfort of green buildings in China and the development of relevant codes.

With rapid development of artificial intelligence, data-driven prediction models play an important role in energy prediction, fault detection, and diagnosis. This paper proposes an ensemble approach using random forest (RF) for hourly performance predictions of GSHP system. Two years of in situ data were collected in an educational building situated in severe cold area in China. Prediction models were established for performance indicators, and results indicate that the average error for COPs, COPu, EERs and EERu were all controlled within 5%. The model established by small amount of data can accurately predict long-term performance, thereby reducing time and difficulty of data collection. RF models, trained with different parameter settings were compared, results indicate that model accuracy was not very sensitive to variables numbers. The impact of input variables on prediction performance was analyzed, and importance ranking changed with period and performance indicators. By comparing the variable importance list, it was possible to establish which parameters were abnormal and lists of different periods can reflect whether the energy structure of building has changed. The overall superiority of RF was verified by comparing with back propagation neural network (BPNN) from robustness, interpretability, and efficiency. First, since GSHP system involving multiple indicators, the robustness, measured by average accuracy, was used to evaluate the accuracy level. According to CV-RMSE, robustness of RF is approximately 3.3% higher than that of BPNN. Second, RF is highly interpretive but BPNN is typical black box model. Finally, modeling complexity and training time of BPNN were much greater than RF.

The phase change material (PCM) using in buildings, a significant technology for the global warming solution, has received considerable attention over the last decade. PCM depending on the phase state change can passively store the solar energy or excess heats as latent heat and release the heats to the indoor environment within a specific temperature range, leading to building energy consumption reducing, and indoor thermal comfort enhancing by smoothing indoor temperature fluctuations. They can also be coupled using with active building energy supply systems to increase the system efficiency and shift peak loads. This study has reviewed the state-of-the-art PCM applications in buildings found on the researches and markets. First, PCM classification, PCM thermal properties, PCM study, and selection methods have been introduced. Second, PCM passively used in building envelops, inclusive of PCM wall, PCM roof, PCM floor, PCM concrete, PCM gypsum board, PCM window, and so on, have been detailedly analyzed. Thirdly, PCM coupled with active system, such as PCM and solar energy hot water/air systems, PCM and floor heating system, PCM and GSHP system, and PCM and air condition and ventilation system, etc., have been extendedly reviewed. Lastly, the potential further researched area for PCM used in buildings has also been presented in conclusion.

Rating systems for green buildings often give assessments from the perspective of the overall performance of a single building or architecture complex but rarely target specific green building technologies. As some of the rating systems are scored according to whether the technologies are used or not, some developers tend to pile up energy-saving technologies blindly just for the sake of certifications without considering their suitability for the application. Such behavior may lead to the failure of achieving the energy goals for green buildings. To solve this problem, a system that pre-evaluates the suitability of green building energy-saving technologies is devised based on modified TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method, SA (simulated annealing) algorithm and unascertained theory-based data analysis method. By setting indices from technology performance, economy, human satisfaction aspects and by using the building prior information and measured database of technology usage, this system can make a quantifiable and multi-dimensional grading assessment for the target green building energy-saving technologies in the design stage. The system aims at helping the designer choose technologies in the design phase that best enhance the performance of the finished green building. It also helps prevent the sub-optimal performance of unsuitable technologies caused by the “pile up” behavior mentioned earlier. To verify this evaluation system, two building designs which use energy-recovery technology are evaluated, and the predicted performance for both designs matched the actual operation of the technology in the buildings themselves well.

The aim of this paper is to optimize the building shape parameters and envelope parameters influencing the rural building energy consumption in cold winter and hot summer climate. Several typical models are established and optimized by integrated TRNSYS and GenOpt. Single-objective optimization has provided guidance to the multi-dimensional optimization. Building shape and envelope parameters are considered simultaneously by multi-dimensional optimization. Results of the optimization showed significant reduction in terms of EC (energy consumption). When O (building orientation) was SW (south by west) 10°, LWR (length-width ratio) was 1.1, WWRS (window-wall ratio in south) with the range of 0.6–0.8, ITE (insulation thickness of exterior wall) and ITR (insulation thickness of roof) was 0.05 m and 0.08 m respectively, the building had minimal energy consumption. The results also indicated that the optimal EWT (exterior window type) was plastic single-frame Low-E insulating glazing filled with inert gas, and the optimal shape of building is Re (rectangle). An effective method was provided to optimize the design of the rural building for the purpose of reducing building energy consumption in cold winter and hot summer climate.