Grey-Box Method for Urban Building Energy Modelling: Advancements and Potentials
Urban building energy modelling (UBEM) has consistently been a pivotal tool to evaluate and control a building stock’s energy consumption. There are two main approaches to build up UBEM: top-down and bottom-up. The latter is the most commonly used in engineering. The bottom-up approach includes three methods: the physical-based method, the data-driven method, and the grey-box method. The first two methods have previously received ample attention and research. The grey-box method is a modelling method that has emerged in recent years that combines the traditional physical method with the data-driven method while it aims to avoid their problems and merge their advantages. Nowadays, there are several approaches for modelling the grey-box model. However, the majority of existing reviews on grey-box methods concentrate on a specific technical approach and thus lack a comprehensive overview of modelling method perspectives. Accordingly, by conducting a comprehensive review of the literature on grey-box research in recent years, this paper classifies grey-box models into three categories from the perspective of modelling methods and provides a detailed summary of each, concluding with a synthesis of potential research opportunities in this area. The aim of this paper is to provide a foundational understanding of grey-box modelling methods for similar research, thereby removing potential barriers in the field of research methods.
Authors
- Ying Guo (ORCID: https://orcid.org/0000-0002-5199-8128)
- Jie Shi (ORCID: https://orcid.org/0000-0003-3182-7193)
- Feng Lu (ORCID: https://orcid.org/0000-0002-8501-2670)
- Lingqi Su
- Tong Guo (ORCID: https://orcid.org/0000-0002-8118-7422)
- Fei Guo (ORCID: https://orcid.org/0000-0001-5739-9436)
Institutions
- Tongji University (CN)
- Dalian University of Technology (CN)
- Shanghai Tongji Urban Planning and Design Institute (CN)
- Technische Universität Berlin (DE)
Publication Details
- Journal
- Energies
- Published
- 2024-10-31
- DOI
- https://doi.org/10.3390/en17215463
- Citations
- 5
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 1.03