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学者姓名：张伟荣
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Abstract ：
Predicting transient particle transport is crucial to address the risks posed to human health by indoor contaminants and to improve the design and control of ventilation systems. The Markov chain model with a coarse matrix has proven to be efficient in this regard, demonstrating faster speeds than traditional methods and higher computational accuracy when based on non-uniform states. However, its application under unsteady airflow conditions is limited, and discussions of how to determine non-uniform Markov states and their corresponding transient transition probabilities when the flow field changes are limited. Therefore, this study developed an application method of Markov chain model based on fixed non-uniform states in unsteady airflow fields. First, this model rapidly provides various Markov state schemes based on airflow field velocity and clustering methods. It then selects the optimal scheme through frequency and finally obtains the transition matrix through Lagrangian tracking for particle transport prediction. This model reduces the computational cost of transient particle transport when the wind speed changes while ensuring high computational accuracy. The proposed method was validated using published flow field experiments and simulation data. The computational speed of the proposed method was compared with computational fluid dynamics (CFD), and in the case studies considered in this research, the normalized root-mean-square deviation of the model was less than 10% and the total computation time was more than 75% faster than that of CFD.
Keyword ：
Periodic ventilation Periodic ventilation Computational fluid dynamics Computational fluid dynamics Non-uniform states Non-uniform states Contaminant transport Contaminant transport Markov chain model Markov chain model
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| GB/T 7714 | Ding, Xiaoxiao , Zhang, Haotian , Zhang, Weirong et al. Rapid prediction of transient particle transport under periodic ventilation using a non-uniform state Markov chain model [J]. | ENERGY AND BUILDINGS , 2024 , 321 . |
| MLA | Ding, Xiaoxiao et al. "Rapid prediction of transient particle transport under periodic ventilation using a non-uniform state Markov chain model" . | ENERGY AND BUILDINGS 321 (2024) . |
| APA | Ding, Xiaoxiao , Zhang, Haotian , Zhang, Weirong , Xuan, Yingli . Rapid prediction of transient particle transport under periodic ventilation using a non-uniform state Markov chain model . | ENERGY AND BUILDINGS , 2024 , 321 . |
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Abstract ：
Building flexible energy systems (BFES) can be enhanced by introducing storage batteries. Providing timely scheduling strategies for flexible resources can improve the system's energy utilization. BFES's scheduling strategies are often adjusted based on Intra-hour photovoltaic(PV) output. Intra-hour PV power generation can be predicted by analyzing cloud imagery data; however, this method does not meet the economic requirements of BFES due to its cost and instrumentation. Therefore, this study proposes a low-cost method for intra-hour PV power generation prediction (IHP) for BFES and explores the impact of integrating this approach into BFES on the rate of renewable energy consumption. This method combined low-quality sky images captured using fisheye cameras installed above buildings with historical electricity generation data and employed convolutional neural networks. The feasibility of the IHP method and the advantages of incorporating it into BFES were verified by applying it to a building equipped with PV devices in Changping, Beijing. The performance of the proposed model algorithm was compared with those of existing models. The proposed method achieved average prediction accuracy improvements of 25.1 and 12.5 % compared with existing models under sunny and cloudy conditions, respectively. Under clear conditions, the model could predict the PV power generation within the next 25 min, whereas under cloudy conditions, the model could predict the power generation within 10 min. In addition, integrating IHP into the scheduling strategy of BFES can improve the renewable energy consumption rate by 44.4 % on the original basis.
Keyword ：
Convolutional Neural Network Convolutional Neural Network PV PV Intra-hour PV Prediction Intra-hour PV Prediction Building Flexible Energy System Building Flexible Energy System
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| GB/T 7714 | Su, Yongyi , Zhang, Weirong , Deng, Gaofeng et al. An Intra-Hour photovoltaic power generation prediction method for flexible building energy systems and its application in operation scheduling strategy [J]. | SOLAR ENERGY , 2024 , 284 . |
| MLA | Su, Yongyi et al. "An Intra-Hour photovoltaic power generation prediction method for flexible building energy systems and its application in operation scheduling strategy" . | SOLAR ENERGY 284 (2024) . |
| APA | Su, Yongyi , Zhang, Weirong , Deng, Gaofeng , Wang, Zhichao . An Intra-Hour photovoltaic power generation prediction method for flexible building energy systems and its application in operation scheduling strategy . | SOLAR ENERGY , 2024 , 284 . |
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Abstract ：
Cities are the main contributors to carbon dioxide emissions, and communities are crucial for implementing urban low-carbon emissions. As a result of the controversies surrounding existing transformation technologies and the varying conditions faced by different targets for transformation, an effective method is required for application in specific target communities to achieve good carbon reduction effects. We propose a method that utilizes the knowledge graph and scenario analysis to systematically screen and evaluate community low-carbon technologies. First, a comprehensive knowledge map spanning multiple fields was constructed, and the correlations were analyzed using CiteSpace. Additionally, we categorized the transformation technologies into nine scenarios to assess their effectiveness and feasibility across different community contexts. Finally, the near-zero carbon emission community pilot area of Shenzhen City was selected as a case study, and the method was applied as a demonstration. The total carbon emissions of the pilot community were reduced by 43% after applying the proposed method. This method is helpful for technical screening at the low-carbon transformation stage and also provides a useful reference for the low-carbon construction stage of communities.
Keyword ：
knowledge graph knowledge graph community community Decision-making Decision-making Low-carbon transformation Low-carbon transformation scenario analysis scenario analysis
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| GB/T 7714 | Yu, Tingjia , Bai, Yifei , Zhou, Ning et al. Decision-making method for low-carbon transformation technology in existing residential areas based on knowledge graph and scenario analysis methods [J]. | INTERNATIONAL JOURNAL OF GREEN ENERGY , 2024 , 22 (1) : 35-49 . |
| MLA | Yu, Tingjia et al. "Decision-making method for low-carbon transformation technology in existing residential areas based on knowledge graph and scenario analysis methods" . | INTERNATIONAL JOURNAL OF GREEN ENERGY 22 . 1 (2024) : 35-49 . |
| APA | Yu, Tingjia , Bai, Yifei , Zhou, Ning , Zhang, Weirong , Yang, Xiu . Decision-making method for low-carbon transformation technology in existing residential areas based on knowledge graph and scenario analysis methods . | INTERNATIONAL JOURNAL OF GREEN ENERGY , 2024 , 22 (1) , 35-49 . |
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Abstract ：
In the context of indoor environmental control, users have varying preferences with respect to their ventilation demands. Currently, the design and control of ventilation schemes do not consider user preferences adequately. To optimize the ventilation strategies based on user demands, the weight coefficients of occupant ventilation demands were extracted in this study using questionnaire surveys. The distinctive characteristics of ventilation preferences among individuals were then analyzed. Computational fluid dynamics (CFD) was used to simulate ventilation schemes with multiple air supply parameters within an office environment, and a database of ventilation performance under different conditions was generated. A multi-index evaluation was conducted to determine the recommended air supply scheme. Finally, a backpropagation neural network was employed to establish a personalized recommendation model for the ventilation scheme. The model inputs include the environmental weight coefficients assigned by the occupants and objective environmental parameters. Its outputs include the ventilation performance and air supply parameters. The results demonstrate that the recommended model can align with user demands, providing suitable ventilation performance and air supply parameters. For user-oriented indoor environment construction, this method enables the rapid and accurate determination of recommended air supply parameters and performance, facilitating the creation of a healthy built environment.
Keyword ：
Demand-oriented control Demand-oriented control BP neural network BP neural network Personalized recommendation Personalized recommendation Ventilation method Ventilation method CFD CFD
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| GB/T 7714 | Zhang, Weijia , Zhang, Weirong , Bai, Yifei et al. Enhancing indoor environmental quality: Personalized recommendation method for demand-oriented indoor ventilation strategy [J]. | SUSTAINABLE CITIES AND SOCIETY , 2023 , 101 . |
| MLA | Zhang, Weijia et al. "Enhancing indoor environmental quality: Personalized recommendation method for demand-oriented indoor ventilation strategy" . | SUSTAINABLE CITIES AND SOCIETY 101 (2023) . |
| APA | Zhang, Weijia , Zhang, Weirong , Bai, Yifei , Wen, Shuqing . Enhancing indoor environmental quality: Personalized recommendation method for demand-oriented indoor ventilation strategy . | SUSTAINABLE CITIES AND SOCIETY , 2023 , 101 . |
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Abstract ：
As infectious respiratory diseases are highly transmissible through the air, researchers have improved traditional total volume air distribution systems to reduce infection risk. Multi-vent module-based adaptive ventilation (MAV) is a novel ventilation type that facilitates the switching of inlets and outlets to suit different indoor scenarios without changing ductwork layout. However, little research has evaluated MAV module sizing and air velocity selection, both related to MAV system efficiency in removing contaminants and the corresponding level of protection for occupants in the ventilated room. Therefore, the module-source offset ratio (MSOR) is proposed, based on the MAV module size and its distance from an infected occupant, to inform selection of optimal MAV module parameters. Computational fluid dynamics simulations illustrated contaminant distribution in a two-person MAV equipped office. Discrete phase particles modelled respiratory contaminants from the infected occupant, and contaminant concentration distributions were compared under four MAV air distribution layouts, three air velocities, and three module sizes considered using the MSOR. Results indicate that lower air velocities favour rising contaminant levels, provided the ventilation rate is met. Optimal contaminant discharge can be achieved when the line of outlets is located directly above the infected occupant. Using this parameter to guide MAV system design, 85.7% of contaminants may be rendered harmless to the human body within 120 s using the default air vent layout. A more appropriate supply air velocity and air vent layout increases this value to 91.4%. These results are expected to inform the deployment of MAV systems to reduce airborne infection risk.
Keyword ：
computational fluid dynamics computational fluid dynamics infection control infection control indoor air quality indoor air quality multi-vent module-based adaptive ventilation multi-vent module-based adaptive ventilation
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| GB/T 7714 | Zhang, Haotian , Ding, Xiaoxiao , Zhang, Weirong et al. Optimising multi-vent module-based adaptive ventilation using a novel parameter for improved indoor air quality and health protection [J]. | BUILDING SIMULATION , 2023 , 17 (1) : 113-130 . |
| MLA | Zhang, Haotian et al. "Optimising multi-vent module-based adaptive ventilation using a novel parameter for improved indoor air quality and health protection" . | BUILDING SIMULATION 17 . 1 (2023) : 113-130 . |
| APA | Zhang, Haotian , Ding, Xiaoxiao , Zhang, Weirong , Zhang, Weijia , Xuan, Yingli . Optimising multi-vent module-based adaptive ventilation using a novel parameter for improved indoor air quality and health protection . | BUILDING SIMULATION , 2023 , 17 (1) , 113-130 . |
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Abstract ：
Multi-energy supply systems for office buildings have been rapidly developed. Although some combinations of distributed energy sources have been applied in office buildings, there is a lack of research on the collaborative matching of office building multi-energy supply systems that integrate random electric vehicles (EVs), stationary battery (STB), photovoltaic (PV), and the power grid (PG). Therefore, in this study, we propose a method for the collaborative matching of supply and demand for multi-energy systems integrated with random EVs, STB, PV, and PG in office buildings to minimize the net operating cost of the system. To this end, we first constructed an objective function, including system operating cost and operating revenue. We then modeled the supply and demand sides of the system. On the supply side, the travel parameters of EVs around the office building were investigated, and the randomness of EVs was quantified using the Monte Carlo algorithm. On this basis, the charging and discharging capacity of EVs was determined. Additionally, an STB charging and discharging model and PV power generation model were established. On the demand side, a prediction model of office building power consumption was constructed using the Random forest (RF) algorithm. Next, the energy supply and demand of office buildings for the following 24 h were matched collaboratively using nonlinear programming. Finally, the proposed collaborative matching method was applied to a real case study. The results showed that the matching method can reduce the net operating cost of the system by up to 92% compared with the Basic scenario.
Keyword ：
Electric vehicles Electric vehicles Collaborative matching method Collaborative matching method Randomness Randomness Multi-energy supply system Multi-energy supply system Photovoltaic Photovoltaic Random forest Random forest
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| GB/T 7714 | Bai, Yifei , Zhang, Weirong , Hu, Xu . A collaborative matching method for multi-energy supply systems in office buildings considering the random characteristics of electric vehicles [J]. | ENERGY AND BUILDINGS , 2023 , 303 . |
| MLA | Bai, Yifei et al. "A collaborative matching method for multi-energy supply systems in office buildings considering the random characteristics of electric vehicles" . | ENERGY AND BUILDINGS 303 (2023) . |
| APA | Bai, Yifei , Zhang, Weirong , Hu, Xu . A collaborative matching method for multi-energy supply systems in office buildings considering the random characteristics of electric vehicles . | ENERGY AND BUILDINGS , 2023 , 303 . |
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Abstract ：
Infectious respiratory diseases are known to have high levels of airborne transmissibility. However, traditional ventilation methods based on perfect mixing often lead to the diffusion of airborne pathogens. A ventilation method that can reduce air mixing is necessary. The ventilation system should also be able to adapt to changes in the use scenario of a given room. For this reason, a new type of ventilation method, referred to as multi-vent module-based adaptive ventilation (MAV), is proposed, and its performance in contaminant diffusion control is evaluated in this study. Computational fluid dynamics (CFD) is applied to investigate the contaminant distribution in a two-desk office with MAV. Tracer gas (CO2) is used to simulate coughed contaminants from an infected person. For three different MAV air distributions (vertical, parallel, and cross mode) and traditional mixing ventilation (MV), the contaminant concentration distributions and contaminant variations in the oronasal areas of the occupants are compared. The results show that MAV results in a smaller contaminant diffusion area and lower contaminant diffusion speed. Furthermore, MAV can reduce the peak contaminant concentrations at the oronasal areas of the occupants to 12% of that of MV. The levels of performance of the three MAV modes are different because of the different airflow patterns that they create. For the tested location of the infected person, the vertical mode has the best performance. The average cumulative inhalation of contaminants in the vertical mode is 23.7% lower than that in the cross mode and 36.5% lower than that in the parallel mode.
Keyword ：
Computational fluid dynamics (CFD) Computational fluid dynamics (CFD) Indoor contaminants Indoor contaminants Cross-infection Cross-infection Probable cumulative inhalation Probable cumulative inhalation Multi-vent module-based adaptive ventilation (MAV) Multi-vent module-based adaptive ventilation (MAV)
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| GB/T 7714 | Zhang, Haotian , Zhang, Weirong , Zhang, Weijia et al. Multi-vent module-based adaptive ventilation to reduce cross-contamination among indoor occupants [J]. | BUILDING AND ENVIRONMENT , 2022 , 212 . |
| MLA | Zhang, Haotian et al. "Multi-vent module-based adaptive ventilation to reduce cross-contamination among indoor occupants" . | BUILDING AND ENVIRONMENT 212 (2022) . |
| APA | Zhang, Haotian , Zhang, Weirong , Zhang, Weijia , Xuan, Yingli , Yue, Yaqi . Multi-vent module-based adaptive ventilation to reduce cross-contamination among indoor occupants . | BUILDING AND ENVIRONMENT , 2022 , 212 . |
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Abstract ：
Cities comprise many residential districts, facing environment and energy problems. Therefore, the retrofit of these existing residential districts is the main part of city renewal. Some retrofit strategies may affect the thermal microclimate environment and lead to changes in the energy demand of buildings. Few studies had evaluated these retrofit effects, which has brought the motivation for this study, to determine the microclimate changes surrounding buildings due to residential district retrofits and the corresponding impact on the cooling load of buildings. The most general retrofit strategies were selected, including greening rate, ground pavement materials, wall materials and roof materials. They were applied to a prediction model for the selected residential district in the simulation tool. The mean reduction of air temperature around the building has been used to evaluate the impact of retrofit strategies on the microclimate in the district. The results showed that greening had the greatest impact on the mean air temperature around buildings, followed by the pavement material, the roof and the wall material. Ground greening, wall material and roof material decreased the total cooling load, while pavement material increased it.
Keyword ：
Retrofit Retrofit Residential district Residential district ENVI-met ENVI-met Strategies Strategies Energy demand Energy demand
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| GB/T 7714 | Li, Ruixu , Zhang, Weirong , Qiu, Luo et al. A numerical study on changes in air temperature around buildings due to retrofits in existing residential districts [J]. | INDOOR AND BUILT ENVIRONMENT , 2022 , 31 (6) : 1464-1481 . |
| MLA | Li, Ruixu et al. "A numerical study on changes in air temperature around buildings due to retrofits in existing residential districts" . | INDOOR AND BUILT ENVIRONMENT 31 . 6 (2022) : 1464-1481 . |
| APA | Li, Ruixu , Zhang, Weirong , Qiu, Luo , Zhang, Huibo . A numerical study on changes in air temperature around buildings due to retrofits in existing residential districts . | INDOOR AND BUILT ENVIRONMENT , 2022 , 31 (6) , 1464-1481 . |
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Abstract ：
Current buildings are facing different environmental demands, such as improving thermal comfort and energy efficiency and reducing exergy loss. However, conventional mixing ventilation (MV) cannot satisfy the different demands; therefore, advanced ventilation modes utilizing a non-uniform environment have gained attention. Multiple scenarios with different occupant activity levels cause difficulties in ventilation control. For these multi factor and multi-objective oriented problems, two issues exist: 1) how to change the operational mode when the scenarios and demands change simultaneously, and 2) how to evaluate the overall ventilation performance under different scenarios and demand conditions. Therefore, this study proposes an approach for assisting in the decision making of ventilation strategies in different scenarios under single or multiple demand conditions. Using a typical office, three occupancy conditions were predesigned by combining MV, displacement ventilation (DV), and stratum ventilation (SV) solutions. Computational fluid dynamics simulations were performed to calculate the indicators for a single demand, namely, heat removal efficiency (HRE), contaminant removal efficiency (CRE), air diffusion performance index (ADPI), and modified index of mixing (IOM*). In the case of multiple demands, the Z-score was applied for standardization and ranking. The result showed that different scenarios with different demand orientations require different ventilation modes. Taking the HRE and IOM* as objectives, DV performed best for the scenario of independent working, whereas SV performed best for discussion and meeting. When considering four demands simultaneously, SV performed best in the scenario of meeting, while MV performed best in the remaining scenarios. This study has verified the effectiveness of using a multi-indicator evaluation method for ventilation strategy optimization under multi-factor and multi-scenario conditions.
Keyword ：
Decision-making analysis Decision-making analysis Multi-indicator evaluation Multi-indicator evaluation Z-score Z-score CFD CFD Multi-scenario Multi-scenario
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| GB/T 7714 | Zhang, Weijia , Zhang, Weirong , Mizutani, Kunio et al. Decision-making analysis of ventilation strategies under complex situations: A numerical study [J]. | BUILDING AND ENVIRONMENT , 2021 , 206 . |
| MLA | Zhang, Weijia et al. "Decision-making analysis of ventilation strategies under complex situations: A numerical study" . | BUILDING AND ENVIRONMENT 206 (2021) . |
| APA | Zhang, Weijia , Zhang, Weirong , Mizutani, Kunio , Zhang, Haotian . Decision-making analysis of ventilation strategies under complex situations: A numerical study . | BUILDING AND ENVIRONMENT , 2021 , 206 . |
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Abstract ：
In recent years, in order to promote the construction of low-carbon communities (LCCs) in China, many scholars have proposed an evaluation indicator system of LCC. The existing indicator systems are mostly established from the macro perspective of environmental impact and resource conservation, but few are from the micro technical perspective. Thus, the aim of this study is to construct a micro technical evaluation indicator system for LCCs. Firstly, the index system was divided into three categories: low-carbon building, low-carbon transportation, and low-carbon environment. Then, the technical indicators were selected through empirical analysis. The indicator weights were assigned by the improved analytic hierarchy process (AHP) and the multi-level fuzzy comprehensive evaluation method was used as the evaluation method of the indicators. Finally, in order to examine the practicality of the indicator system, two typical communities in Tianjin and Shanghai were selected as case studies. The results showed that the indicator system gave a reasonable low-carbon level for the two communities, which was in line with the actual low-carbon construction status of each community. In addition, the evaluation results pointed out that the low-carbon community (LCC) in Tianjin needs to further strengthen the construction of the low-carbon environment, including community compactness, rainwater collection and utilization, and waste recycling. For the LCC in Shanghai, it was pointed out that the construction of the low-carbon building and low-carbon transportation needs to be strengthened. The indicator system can be used as a tool for urban planning and construction personnel to evaluate the construction progress and low-carbon degree of LCC.
Keyword ：
low-carbon community low-carbon community improved analytic hierarchy process improved analytic hierarchy process technical indicators technical indicators multi-level fuzzy comprehensive evaluation method multi-level fuzzy comprehensive evaluation method
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| GB/T 7714 | Bai, Yifei , Zhang, Weirong , Yang, Xiu et al. The Framework of Technical Evaluation Indicators for Constructing Low-Carbon Communities in China [J]. | BUILDINGS , 2021 , 11 (10) . |
| MLA | Bai, Yifei et al. "The Framework of Technical Evaluation Indicators for Constructing Low-Carbon Communities in China" . | BUILDINGS 11 . 10 (2021) . |
| APA | Bai, Yifei , Zhang, Weirong , Yang, Xiu , Wei, Shen , Yu, Yang . The Framework of Technical Evaluation Indicators for Constructing Low-Carbon Communities in China . | BUILDINGS , 2021 , 11 (10) . |
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