Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets

Guo，Zhiling1,2; Zhuang，Zhan3; Tan，Hongjun1; Liu，Zhengguang4; Li，Peiran2; Lin，Zhengyuan5; Shang，Wen Long6,7,8; Zhang，Haoran9; Yan，Jinyue1

2023-12-01

10.1016/j.renene.2023.119471

Renewable Energy   影响因子和分区

0960-1481

1879-0682

The widespread adoption of photovoltaic (PV) technology for renewable energy necessitates accurate segmentation of PV panels to estimate installation capacity. However, achieving highly efficient and precise segmentation methods remains a pressing challenge. Recent advancements in artificial intelligence and remote sensing techniques have shown promise in PV segmentation. Nevertheless, real-world scenarios introduce complexities such as diverse sensing platforms, sensors, panel categories, and testing regions. These factors contribute to resolution, size, and foreground-background class imbalances, impeding accurate and generalized PV panel segmentation over large areas. To address these challenges, we propose GenPV, a deep learning model that leverages data distribution analysis and PV panel characteristics to enhance segmentation accuracy and generalization. GenPV employs a multi-scale feature learning approach, utilizing an enhanced feature pyramid network to fuse data features from multiple resolutions, effectively addressing resolution imbalance. Moreover, inductive learning is employed through a multitask approach, facilitating the detection and identification of both small and large-sized PV panels to mitigate size imbalance. To address significant class imbalance in PV panel recognition tasks, we integrate the Focal loss function for effective hard sample mining. Through experimental evaluation conducted in Heilbronn, Germany, our proposed method demonstrates superior performance compared to state-of-the-art approaches in PV panel segmentation. The results exhibit progressively higher accuracy and improved generalization capability. These findings highlight the potential of our method to serve as an advanced and practical tool for PV segmentation in the renewable energy field.

Deep learning Generalization capability Photovoltaics Remote sensing Renewable energy Semantic segmentation

SCI ; EI

英语

其他

WOS:001102916200001

20234314938072

Deep learning ; Learning systems ; Renewable energy resources ; Semantic Segmentation ; Semantics ; Solar panels

Ergonomics and Human Factors Engineering:461.4 ; Energy Resources and Renewable Energy Issues:525.1 ; Solar Cells:702.3 ; Artificial Intelligence:723.4

ENGINEERING

2-s2.0-85174461392

Scopus

1.Department of Building Environment and Energy Engineering,The Hong Kong Polytechnic University,Kowloon,Hong Kong
2.Center for Spatial Information Science,University of Tokyo,Kashiwa,277-8568,Japan
3.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
4.Department of Power and Electrical Engineering,Northwest A&F University,Yangling,712100,China
5.School of Computer Science,South China Normal University,China
6.College of Metropolitan Transportation,Beijing University of Technology,Beijing,100124,China
7.Transport Studies,Imperial College London,London,SW7 2AZ,United Kingdom
8.School of Architecture and Cities,University of Westminster,London,NW1 5LS,United Kingdom
9.School of Urban Planning and Design,Peking University,Shenzhen,No.2199 Lishui Road, Nanshan District, Guangdong,518055,China

Guo，Zhiling,Zhuang，Zhan,Tan，Hongjun,et al. Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets[J]. Renewable Energy,2023,219.

Guo，Zhiling.,Zhuang，Zhan.,Tan，Hongjun.,Liu，Zhengguang.,Li，Peiran.,...&Yan，Jinyue.(2023).Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets.Renewable Energy,219.

Guo，Zhiling,et al."Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets".Renewable Energy 219(2023).