Zhenzhong Hu

    He recieved both his BE and PhD degree in the Department of Civil Engineering at Tsinghua University, China. He was a visiting researcher in Carnegie Mellon University.
    He is now the associate professor in Shenzhen International Graduate School, Tsinghua University, and also the secretary general of the BIM Specialty Committee of the China Graphics Society.
    His research interests include information technologies in civil and marine engineering, building information modeling (BIM) and digital disaster prevention and mitigation.
  • 2020-11-07

    I participated in the sixth National BIM Academic Conference as the host in Taiyuan, Shanxi. The conference focused on the application of "GIS+BIM" technology in highway engineering, and it attracted more than 500 university teachers and students, enterprise leaders and technical leaders from all over the country. At the meeting, my student Liu Yi made a paper report entitled "Research on the Application of Knowledge Graph in BIM Model Review", which mainly proposed a method of using knowledge graph to assist manual review of BIM models. In this paper, the deep learning model is used to extract entities and relationships from text data in mechanical and electrical field, and a small knowledge graph is constructed. When a BIM model is reviewed, the attributes of equipment can be shown by searching the knowledge map, and the relevant constraint statements in the specification can be obtained automatically. This method can save the reviewers the time of browsing and searching the specification, and effectively assist manual review of BIM model.

  • 2020-10-28

    In order to commend the authors and reviewers who have made outstanding contributions to the development of the journal, the editorial department of Journal of Tsinghua University (Science and Technology) launched the selection of excellent papers and reviewers in 2020, and finally selected 3 excellent papers and 10 excellent reviewers. I am honored to be selected as the excellent reviewer in 2020, and I will continue to support the development of Journal of Tsinghua University (Science and Technology) and fulfill the responsibilities of reviewers in the future. Note: Journal of Tsinghua University (Science and Technology) is a core Chinese journal included in EI.

  • 2020-10-25

    I made a theme report in the sub-forum of The Third International Conference on Sustainable Development of Shanshui Cities held in Chongqing. The new infrastructure system includes three aspects: first, information infrastructure, including communication networks, new technologies and computing power and facilities; Second, the integration of infrastructure, the integration of different technologies and the use of new infrastructure, such as smart transportation, smart energy and smart architecture; Third, the innovation infrastructure, which is a series of infrastructure platforms that support innovation, such as industrial innovation infrastructure. The implementation of new infrastructure cannot be separated from the support of big data. In the application of big data, three major problems need to be faced: how do computers know and understand data, how do computers manage big data and how should big data create value.

  • 2020-9-17

    “Extraction Technique of Standardized Dynamic Big Data of Buildings and Attached MEP Systems” has been approved in expert evaluation. This technique is an achievement of the National Key R&D Program subject “Key Techniques of Data Integration and Databases for Buildings and Attached MEP Systems” (project number: 2017YFC0704202) under the project “Research and Exemplification of Green Building Management Technologies based on Full-Process Big Data”, independently developed by the research team. It addresses the problems of incompatibility among energy consumption monitoring systems, and complications in energy consumption monitoring data, and proposed an entity-relationship-model-based standardized energy consumption data model for buildings and attached MEP systems on the basis of energy consumption data criteria, stipulating the organization and description of energy consumption data. There forth, it proposed an integration method for energy consumption monitoring data, and an energy consumption data extraction method based on standardized interfaces and relay servers, and implemented pertinent software to validate the methods. This technique provided a solution for the extraction of standardized dynamic energy consumption data from multi-source heterogeneous energy consumption monitoring systems, laying the foundation for analyses and applications of energy consumption big data, thus possessing important application value and reference significance.

  • 2020-9-21

    A Novel Probabilistic Approach to Optimize Stand-Alone Hybrid Wind-Photovoltaic Renewable Energy System has been published in Energies .

    In this paper, a novel probabilistic approach is proposed to optimize a stand-alone hybrid wind-photovoltaic renewable energy system installed in the South China Sea. This approach uses the levelized cost of energy (LCOE) as the objective and the stability of the power generation as the constraint. In addition, the present study proposes a model of a battery-level coefficient, based on which the battery capacity can be probabilistically estimated, given the expected power shortage in a given continuous duration. This model discusses the optimization employing genetic algorithm (GA) when the model estimating the power generated from a hybrid wind- photovoltaic power system (HWPS) with a specific configuration is combined with the widely used cost model. The optimization verifies that the proposed probabilistic approach provides reasonable estimates of the power generation of a hybrid system in an optimization process. The verification reveals that the specifications of the duration to calculate the expected power shortage could have significant impacts on the estimates of power generations. Consequently, the present study performs a sensitivity analysis concerning the continuous power deficit days. The optimization and sensitivity analyses both indicate that the well-established loss of power supply probability (LPSP) criterion of 1% should not be applied universally across the South China Sea. In the areas with more stable winds and solar resources, the LPSP criterion can be relaxed when only the power deficit duration is concerned.