Energy and Buildings最新文献

{"title":"Opto-electro-thermal analysis of semi-transparent perovskite solar cells applied to BIPV","authors":"Penghui Liu,&nbsp;Longxiang Wang,&nbsp;Jiapeng Wang,&nbsp;Yirong Zhai,&nbsp;Guiqiang Li","doi":"10.1016/j.enbuild.2025.115585","DOIUrl":"10.1016/j.enbuild.2025.115585","url":null,"abstract":"<div><div>Semi-transparent perovskite solar cells (ST-PSCs) are regarded as ideal for building-integrated photovoltaic (BIPV) applications due to their many advantages, but practical applications still face challenges, among which how to improve the stability and simultaneously increase the power conversion efficiency (PCE) and average visible transmission (AVT) values are the most critical. For the first time, this paper uses rigorous opto-electro-thermal coupling simulation to explain the energy conversion mechanism inside the ST-PSC device, quantify the contribution of heat generation of each internal part, and propose optimization methods for each part. By optimizing the device structure, the light utilization efficiency (LUE) value is increased from 1.28 % to 3.56 %, and the PCE and AVT of the device are 12.6 % and 28.26 % respectively. In addition, the ST-PSC heat transfer model applied to BIPV is proposed, and the theoretical operating temperature of the device is found to be 32.9 °C at the maximum LUE. On this basis, the back electrode was optimized to increase the LUE value to 3.99 %, proving that improving the transparency of the back electrode is a powerful way to get rid of the obvious negative correlation between PCE and AVT and significantly increase the LUE value. The day and night use of the device was also investigated, with efficiencies of more than 14 % maintained at night under the reverse illumination of an indoor light source, and efficiencies of up to 17.53 % in high color temperature environments. This study provides an exploration of the energy analysis and the equilibrium relationship between PCE and AVT for ST-PSC devices, which provides a strong guideline to promote the multifaceted application of ST-PSC in BIPV systems.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115585"},"PeriodicalIF":6.6,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial distribution and influencing factors of data centers in China: An empirical analysis based on the geodetector model","authors":"Lei Wang ,&nbsp;Donglin Chen ,&nbsp;Mengdi Yao ,&nbsp;Guolong She","doi":"10.1016/j.enbuild.2025.115588","DOIUrl":"10.1016/j.enbuild.2025.115588","url":null,"abstract":"<div><div>Data centers are vital infrastructure for the digital economy’s growth. Analyzing the spatial distribution of data centers and the factors influencing this distribution can guide their sustainable and regionally balanced development. Using data from Chinese data centers between 2016 and 2022, this study employs the nearest neighbor index, geographic concentration index, imbalance index, kernel density estimation, and Anselin Local Moran’s I to quantitatively analyze the spatial distribution characteristics of data centers. Additionally, Geodetector and Pearson correlation analysis are used to identify factors that significantly correlate with the spatial distribution of data centers. The results indicate that: (1) Data centers exhibit clear agglomeration characteristics, forming a “dense east and sparse west” distribution pattern, with three cores in the Beijing-Tianjin-Hebei region, the Yangtze River Delta, and the Pearl River Delta. (2) Provincially, the spatial distribution of data centers shows a significant imbalance, with “high-low” clustering observed in Guangzhou and “high-high” clustering in Shanghai. (3) Multiple factors influence the spatial distribution, with computing demand and economic development showing the strongest correlations. Furthermore, data center distribution is shifting from solely pursuing economic benefits to taking into account both economic and environmental benefits. (4) Regional variations exist in influencing factors. In the eastern region, computing demand and economic development levels show the strongest correlations, while in the central and western regions, government financial support is more significantly correlated. Based on the analysis results, this study proposes specific recommendations for the development and distribution of data centers across various regions of China from the perspectives of policymakers and data center operators.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"336 ","pages":"Article 115588"},"PeriodicalIF":6.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative AI strategies for enhancing smart building operations through digital twins: A survey","authors":"Adel Oulefki ,&nbsp;Hamza Kheddar ,&nbsp;Abbes Amira ,&nbsp;Fatih Kurugollu ,&nbsp;Yassine Himeur ,&nbsp;Ahcene Bounceur","doi":"10.1016/j.enbuild.2025.115567","DOIUrl":"10.1016/j.enbuild.2025.115567","url":null,"abstract":"<div><div>The Digital Twins (DT) have emerged as a digital transformation automation process with ubiquitous applications that span various domains, including buildings, manufacturing, and healthcare. These virtual clones of physical systems provide relevant insights, enhance decision-making processes, and optimize operations, along with allowing the prediction of future operations. Artificial intelligence (AI) has been instrumental in enhancing the functionalities of DT. This survey paper explores recent developments in advanced AI algorithms tailored for DT in building settings. Moreover, a wide spectrum of AI techniques designed to address the challenges posed by DT in buildings are categorized and reviewed, including convolution neural networks (CNN), recurrent neural networks (RNNs), and generative adversarial networks (GANs), among other cutting edge transformative technologies. Furthermore, the integration of reinforcement learning (RL) and transfer learning (TL) into the DT ecosystem is discussed. This survey explores practical use cases, such as predictive scenarios, anomaly detection, and optimization of DT models. The incorporation of multimodal AI sensor data and edge computing in enhancing the accuracy and efficiency of DT is analyzed. Additionally, challenges and future directions in the field are explored, including data privacy concerns using Blockchain (BC), scalability issues, and the potential impact of quantum computing (QC) and large language models (LLMs) on DT technology. This comprehensive survey serves as a valuable resource for researchers, practitioners, and decision makers looking to utilize cutting-edge techniques to harness the full potential of DT technology in smart buildings (SB).</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115567"},"PeriodicalIF":6.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of green façade retrofitting on thermal performance and energy efficiency of existing buildings in northern China: An experimental study","authors":"Sun Qi ,&nbsp;Nangkula Utaberta ,&nbsp;Allen Lau Khin Kiet ,&nbsp;Xu Yanfang ,&nbsp;Han Xiyao","doi":"10.1016/j.enbuild.2025.115550","DOIUrl":"10.1016/j.enbuild.2025.115550","url":null,"abstract":"<div><div>In recent years, vertical greenery systems have gradually entered public attention, and more and more people are beginning to know it. This study aims to explore the impact of green façade retrofitting on building thermal performance and building energy consumption under hot summer conditions in northern China and to derive detailed data for future building energy efficiency retrofitting. This article used a comparative experiment to complete this research. Four laboratories of the same structure were constructed in Shandong Province, China. A movable metal frame was installed on the outside of the laboratory, and the green façades could be adjusted to direct green façades or indirect green façades according to the need of the experiment. In addition, two different plants were studied. This study was carried out under two experimental conditions: cooling and no cooling. The experiment was carried out from July to August 2024. According to the data obtained from the experiment, the green façade well reduced the surface temperature of the walls around the experimental room and the average temperature level in the room. The most significant temperature drop of 23.1 °C was observed on the surface of the external walls of the room with the indirect green façade covered with Parthenocissus quinquefolia. The data show that the average temperature in several experimental rooms decreased by 1–5 °C. The indirect green façade improves the thermal insulation of the building envelope better than the direct green façade. In the cooling experiments, the room with indirect green façades covered with Parthenocissus quinquefolia has the highest energy-saving rate of 45.75 %. However, the room with direct green façades covered with Humulus scandens only has an energy saving rate of 6.43 %.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115550"},"PeriodicalIF":6.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating CFD and thermoregulation models: A novel framework for thermal comfort analysis of non-uniform indoor environments","authors":"Juan Mahecha Zambrano,&nbsp;Luca Baldini","doi":"10.1016/j.enbuild.2025.115570","DOIUrl":"10.1016/j.enbuild.2025.115570","url":null,"abstract":"<div><div>Occupant-centric radiant cooling strategies have the potential to enhance thermal comfort and reduce energy consumption by influencing the operative temperature and creating a non-uniform environment around the occupant instead of conditioning the entire indoor space. However, current literature shows significant limitations: environmental and physiological parameters are often estimated at a few spatial points and averaged to calculate comfort metrics, hindering the study of local comfort in non-uniform environments; the human body is often represented by fixed mean heat rates or temperatures, neglecting thermoregulation responses. To advance the state-of-the-art, this paper presents a novel numerical framework to evaluate the impact of non-uniform indoor environments on physiological responses, heat balance, and thermal comfort. Using an efficient and scalable co-simulation protocol, the framework integrates a thermoregulation and a computational fluid dynamics model.</div><div>Further, the framework introduces two novel metrics: Reference Heat Deviation and Reference Heat Deviation Temperature. The former quantifies changes in human heat balance by measuring deviations in metabolic and sensible heat from a reference condition, while the latter translates this information into equivalent temperatures. A case study demonstrated the framework’s application by studying the performance of a personal radiant cooling system. Results indicate that at 28 °C air temperature, the heat balance is not restored to comfort levels at 25 °C, but latent heat exchange is minimised, and radiant asymmetry remains low. Further, the perceived temperature is up to 2 °C lower than the air temperature. Finally, this work’s limitations, potential applications, and outlook are discussed.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115570"},"PeriodicalIF":6.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis of climate friendly neighbourhoods in China and Norway: Insights on approaches, practices and policies from four case studies","authors":"Johannes Brozovsky ,&nbsp;John Clauß ,&nbsp;Peng Liu ,&nbsp;Tonje Trulsrud Healey-Brudal ,&nbsp;Yunbo Yang ,&nbsp;Ørjan Healey-Brudal ,&nbsp;Jinping Li ,&nbsp;Bojia Li","doi":"10.1016/j.enbuild.2025.115569","DOIUrl":"10.1016/j.enbuild.2025.115569","url":null,"abstract":"<div><div>Climate friendly neighbourhoods such as positive energy districts and zero emission and sustainable plus energy neighbourhoods aim to significantly reduce the environmental impact of urban areas by achieving higher levels of sustainability, energy efficiency and the use of renewable energy. Based on a questionnaire survey, this study analyses and compares four selected cases from China and Norway, providing a detailed description of commonalities and differences between them. Additionally, the study highlights current gaps in both countries’ regulations and proposes and discusses updates. The results show that the commonalities include the widespread use of photovoltaic systems and heat pumps, and the main difference is the use of building materials, with concrete in China and timber in Norway. From the selected cases it was found that both countries face challenges in integrating mobility considerations and citizen engagement in the planning of climate friendly neighbourhoods. Recommendations for future policy enhancements include the introduction of mandatory zero-carbon building codes, standards for circular economy practices, and financial incentives for deep retrofitting in order to speed up the decarbonisation of the building sector. The study also underscores the need for consistent definitions and regulations for climate friendly neighbourhoods and related concepts to streamline implementation and enhance credibility. Enhanced international cooperation and knowledge exchange are vital for global climate goals, with Norway and China offering valuable lessons in the pursuit of sustainable urban development.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115569"},"PeriodicalIF":6.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on optimization and risk resilience of integrated energy system in near-zero carbon park considering carbon taxes","authors":"Yufeng Sang ,&nbsp;Jiaxing Li ,&nbsp;Pengxiang Li ,&nbsp;Zhaoying Wang ,&nbsp;Zhihao Wan ,&nbsp;Jakub Jurasz ,&nbsp;Wandong Zheng","doi":"10.1016/j.enbuild.2025.115578","DOIUrl":"10.1016/j.enbuild.2025.115578","url":null,"abstract":"<div><div>Carbon emissions from industrial parks are the main carbon source and battlefield for carbon mitigation, accounting<!--> <!-->for<!--> <!-->1/4 of global carbon emissions in 2022. In order to realize the low-carbon and sustainable development as well as promote the near-zero carbon transition of industrial parks, two low-carbon energy transition roadmaps based on natural gas for short-term and electricity for long-term are developed to analyze their comprehensive benefits as well as risk resilience in the context of carbon tax. Consequently two low-carbon transition energy systems are built and optimized for a light industrial park in China, and comparison analysis is conducted for the future scenarios with carbon tax in short- and long-term. The results show that energy systems based on natural gas and electricity can significantly enhance energy utilization efficiency and effectively reduce carbon emissions, achieving reductions of 60.1 % and 70.9 %, respectively. The natural gas-based energy system demonstrates better short-term economic performance with a payback period of 3.69 years, but has limited resilience to carbon tax risks. The electricity-based energy system performs better in the long term, with a payback period of up to 4.79 years, and offers greater adaptability to carbon tax policies. This study can provide an effective way and decision support for the park to realize near-zero carbon transition.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115578"},"PeriodicalIF":6.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing greenhouse emissions from Australia’s housing stock through solar pre-cooling and pre-heating","authors":"Shayan Naderi ,&nbsp;Declan Heim ,&nbsp;Simon Heslop ,&nbsp;Dong Chen ,&nbsp;Iain MacGill ,&nbsp;Alistair Sproul ,&nbsp;Gloria Pignatta","doi":"10.1016/j.enbuild.2025.115556","DOIUrl":"10.1016/j.enbuild.2025.115556","url":null,"abstract":"<div><div>This paper explores the use of surplus Photovoltaics (PV) generation for Solar Pre-Cooling and pre-Heating (SPCaH) in residential buildings. SPCaH not only alleviates daytime minimum demand challenges in the electricity network by reducing solar power exports to the grid but also reduces evening residential Air-Conditioning (AC) demand. This, in turn, reduces total electricity industry costs and potentially saves households on electricity bills. This study addresses the underexplored and complex impact of SPCaH on electricity industry emissions. The results, which are based on the simulated thermal performance of nine building types via AccuRate and hourly measured PV generation, AC demand, and net demand profiles of approximately 450 households in four Australian capital cities, reveal that summer has the highest potential for both minimum demand mitigation (up to 4 kW per building) and maximum demand reduction (up to 0.8 kW per building). Thermal comfort does not significantly limit SPCaH implementation. SPCaH reduces emissions, with a potential annual reduction exceeding 600 kg CO₂–e in a 6-star building in Brisbane. Seasonal analysis revealed that households might reduce their carbon emissions by up to 30 % during summer and spring, significantly cutting emissions in electricity industries with substantial solar generation.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"336 ","pages":"Article 115556"},"PeriodicalIF":6.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining the generalizability of inverse surrogate models for different geometries and locations","authors":"Liam Jowett-Lockwood ,&nbsp;Ralph Evins","doi":"10.1016/j.enbuild.2025.115539","DOIUrl":"10.1016/j.enbuild.2025.115539","url":null,"abstract":"<div><div>While building surrogate modelling has been shown to accurately replicate the outputs of computationally intensive building energy modelling, successfully adopting surrogate modelling in practice still has challenges. As surrogate models are machine learning models, they require an extensive quantity of training data in order to train effectively. The process of acquiring training data often requires numerous simulation runs of a building energy model. To offset this issue, surrogate models that demonstrate a suitable level of generalizability can be applied successfully to multiple projects without the need for the further generation of data.</div><div>This study examines the generalizability of multiple inverse surrogate models. Inverse surrogate modelling is a more difficult task than traditional surrogate modelling as it tries to extract building energy model inputs from output data. As the output data required to do this is often comprehensive, deep learning models are preferred. For the inverse surrogate models, a basic deep artificial neural network, convolutional neural network, recurrent neural network and transformer were examined. Output data in this study consisted primarily of temperature and energy time series data with input data being building energy model parameters reflective of thermally important building characteristics.</div><div>Generalizability is assessed by first training the inverse surrogate models on data from 3 separate building energy models. Each of the building energy models contain geometry that is randomly scaled. Additionally we examine training the inverse surrogate models on building energy model data produced with multiple locations as well as on data from all building energy models at once. Parameters relating to the building envelope demonstrated the highest prediction performance among the models, whereas the prediction performance for less influential parameters was more varied depending on the inverse surrogate model. Overall, the convolutional neural network typically outperformed the other models with the recurrent neural network and transformer producing slightly worse performance. The artificial neural network was unable to accurately predict parameters outside of a select few that were highly influential to the time-series data. In the cases of training with data from multiple locations or all buildings at once, prediction performance decreased, however several parameters remained predictable.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115539"},"PeriodicalIF":6.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An self-adaptive cascade smart window capable of adjusting indoor light and thermal environment for building energy conservation","authors":"Jianjuan Yuan,&nbsp;Xuan Zhao,&nbsp;Xuemei Zhang,&nbsp;Yue Han,&nbsp;Xiangfei Kong","doi":"10.1016/j.enbuild.2025.115568","DOIUrl":"10.1016/j.enbuild.2025.115568","url":null,"abstract":"<div><div>The performance improvement of windows has a great need for development in terms of energy saving and carbon reduction, as their energy consumption is up to 30 % of the total energy consumption of the building. The window with phase change material (PCM) improves the thermal inertia of the window and thus reduces the energy consumption of air conditioning. However, the application effect of window with single PCM was limited by the leakage of PCM with the state of melted and the poor natural lighting. Therefore, a self-adaptive cascade phase-change window (PCMWⅢ) composed of three kinds of flexible-shaped PCM was proposed in this study. The experiments were carried out to compare the thermal performance of PCMWⅢ and conventional PCM window (PCMWⅠ). The results indicated that the indoor peak temperature of the model house with PCMWⅢ was 4.74 °C lower than that with PCMWⅠ under constant irradiation condition. The indoor temperature of the model house with PCMWⅢ exhibited a 2 °C temperature decrease compared with PCMWⅠ under the case of outdoor dynamical weather. Furthermore, a mathematical model of the cascaded PCM window was developed. The total energy consumption (including cooling load and lighting electrical load) was as the optimization goal to optimize the structure of PCMWⅢ. The simulation results showed that when the area ratio of SPCM-19, SPCM-25 and SPCM-28 was 3:1:5, the light and thermal performance of optimized PCMWⅢ showed excellent effect with the total energy consumption reduction of 15.03 % compared with PCMWⅠ. The proposed cascade phase-change smart window can provide reference for the development of PCM windows.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115568"},"PeriodicalIF":6.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}