Assessment of BIPV power generation potential at the city scale based on local climate zones: Combining physical simulation, machine learning and 3D building models

IF 9 1区 工程技术 Q1 ENERGY & FUELS
Haida Tang , Xingkang Chai , Jiayu Chen , Yang Wan , Yuqin Wang , Wei Wan , Chunying Li
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引用次数: 0

Abstract

The adoption of distributed photovoltaic (PV) in cities can alleviate energy shortages, and building integrated photovoltaic (BIPV) has multiple advantages including building material saving and space saving. Predicting the potential of annual BIPV power generation (BIPVPG) and exploring the influencing factors in built environment is of great significance. This study created 50 simplified LCZ models based on the indicator ranges of LCZ categories 1–10. These models assume uniform building height and distribution to facilitate the simulation of annual BIPVPG. The annual BIPVPG (with PV materials applied on roofs and vertical facades) and the urban heat island (UHI) effects of the LCZ models in 15 cities were simulated. The UHI effects of the LCZ models were used to account for the reduction in BIPV efficiency caused by urban heat islands. Following that, the impact of climate and urban morphological factors on BIPVPG was studied using a multiple linear regression (MLR) and random forest (RF) model. According to the results, the RF model performed better in BIPV power generation prediction. Among urban morphological factors, average building height (ABH), aspect ratio (AR), and sky view factor (SVF) have dominant impact on urban BIPVPG. The impact on BIPVPG is minimal when ABH is between 10 and 15 m, and AR is between 1.5 and 2. When SVF is less than 0.8, the BIPVPG per unit building roof and wall area can be improved with building surface fraction (BSF) below 80. Among climate factors, solar radiation has significant impact on urban BIPVPG over other factors. Finally, based on the Guangzhou building data, the validated RF model was applied to predict Guangzhou's annual BIPV power potential. This study provides insights for machine learning models in BIPVPG assessment and offers quantitative recommendations for decision-makers and urban planners in developing BIPV cities with high energy resilience and sustainable level.
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来源期刊
Renewable Energy
Renewable Energy 工程技术-能源与燃料
CiteScore
18.40
自引率
9.20%
发文量
1955
审稿时长
6.6 months
期刊介绍: Renewable Energy journal is dedicated to advancing knowledge and disseminating insights on various topics and technologies within renewable energy systems and components. Our mission is to support researchers, engineers, economists, manufacturers, NGOs, associations, and societies in staying updated on new developments in their respective fields and applying alternative energy solutions to current practices. As an international, multidisciplinary journal in renewable energy engineering and research, we strive to be a premier peer-reviewed platform and a trusted source of original research and reviews in the field of renewable energy. Join us in our endeavor to drive innovation and progress in sustainable energy solutions.
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