Energy, Economic and Environmental Performances of BIPV Facade in Urban Environment: A Machine Learning Based Urban Morphology Method
Creators: name: Zhao, Hongying
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 330204
subject: Building information modelling and management
classificationCode: 330410
subject: Urban analysis and development
classificationCode: 330206
subject: Building science, technologies and systems

description: To create a sustainable and liveable urban environment, adopting Building-Integrated Photovoltaics (BIPVs) offers a promising solution by transforming building surfaces into green energy generators. BIPVs can integrate various building components such as roofs, curtain walls, windows, balconies, and shading devices, presenting significant potential for large-scale façade applications in urban areas. However, there is a lack of evaluation methods to assess the potential for urban-scale deployment of BIPV façades.
While many studies have examined BIPV feasibility for individual buildings from technical, economic, and environmental perspectives, limited research has explored BIPV potential at the urban scale due to the complexity of urban fabrics. Although solar potential at the urban scale has been investigated through simulation methods, gaps remain in evaluating BIPV façade application potential, particularly for facilitating urban planning decisions.
Urban morphology, which encompasses the physical features shaping city structures and patterns, can influence solar potential in urban areas. Despite its use in various urban studies, there is a need to integrate urban morphology considerations into BIPV façade deployment. Traditional simulation methods for calculating solar irradiation on vertical surfaces in urban environments face significant challenges due to the complexity and variability of urban landscapes, requiring substantial resources and specialized software knowledge.
To address these challenges, this study aims to develop a novel method using machine learning to predict large-scale solar irradiation and energy generation potential for BIPV façade systems in urban environments. By incorporating urban morphological indicators, this method seeks to assist in decision-making processes, facilitating the integration of BIPVs into urban planning and contributing to more sustainable cities.
Using an Australian city as a case study, a hierarchical two-level machine learning model was developed to predict solar potential for building façade PV systems. The model incorporates 13 urban morphological predictors to determine average shading height and unshaded solar irradiance. Combining these predictions with wall datasets and different façade BIPV efficiencies, the proposed prediction method is developed to predict unshaded electricity generation. The model results showcase high efficiency and accuracy in the prediction of the shading height, solar irradiance, and electricity generation. 
Based on the prediction models, the economic and environmental performance is evaluated for the study area. A detailed analysis of those results reveals the key urban morphological parameters for improving the economic and environmental performance, which generate a decision-making matrix. This urban morphology matrix is designed to guide and enhance the economic and environment performance of BIPV façade application in the urban environment, which aids the decision making in the early stage of urban planning. 
The outcome of this research contributes to the study of urban BIPV applications, by providing the comprehensive research framework for BIPV application in the urban environment and identifying the current research gaps and future directions. Meanwhile, this research supports the practical decision making and urban planning, by establishing the efficient prediction-based method and the urban morphology matrix to facilitate informed decision making. Therefore, this research can promote high BIPV penetration in urban environments, contributing to sustainable development goals in the near future.