TransPV: Refining photovoltaic panel detection accuracy through a vision transformer-based deep learning model

Zhiling Guo, Jiayue Lu, Qi Chen, Zhengguang Liu, Chenchen Song, Hongjun Tan, Haoran Zhang and Jinyue Yan

Applied Energy, 2024, vol. 355, issue C, No S030626192301646X

Abstract: The increasing need to develop renewable energy sources to combat climate change has led to a significant rise in demand for photovoltaic (PV) installations. Consequently, accurately detecting and estimating the capacity and potential for electricity generation of these installed PV systems has become crucial for effective energy management. However, due to the limitation of localized mechanism in convolution operations, traditional FCN-based methods face challenges in capturing global dependencies among remote sensing image patches. As a result, they struggle to model long-range interactions between different PV panels with diverse structures, including varying size, shape, and texture, particularly in distributed residential areas. To tackle the challenge of modeling PV panels with diverse structures, we propose a coupled U-Net and Vision Transformer model named TransPV for refining PV semantic segmentation. Specifically, Mix Transformer block is incorporated in the encoder to enhance the modeling of global context, while the U-Shaped structure enables the combination of multi-level features, resulting in enriched feature representation, which helps the model better understand objects in the images, regardless of their shape, size, and texture. In addition, we implement the PointRend module in the decoder to obtain finer segmentation boundary details, and utilize a novel refiner loss function during the training process to alleviate the problem of extremely unbalanced samples. The experimental results from the Heilbronn datasets demonstrate the remarkable performance of our proposed TransPV model in addressing intra-class diversity of PV structures, surpassing previous state-of-the-art methods with an impressive IoU and accuracy of 0.802 and 0.876, respectively. Furthermore, our model exhibits high generalization capability on the BDPV dataset with IoU of 0.745. The obtained results highlight the superiority of TransPV in improving accuracy and addressing diverse structure issues in PV segmentation, which provides valuable insights for optimizing the efficiency and sustainability of renewable energy.

Keywords: Renewable energy; Photovoltaic panels; Deep learning; Vision transformer; Semantic segmentation (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.apenergy.2023.122282

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