Rooftop Photovoltaic Potential Estimation Approach Based on Orientation Perception and Quantitative Integration

ZHU Jiasong; LIN Weiyan; YANG Yun; HUA Yuansheng

doi:10.13203/j.whugis20250060

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Rooftop Photovoltaic Potential Estimation Approach Based on Orientation Perception and Quantitative Integration

Photogrammetry and Remote Sensing | 更新时间：2026-11-05

- Rooftop Photovoltaic Potential Estimation Approach Based on Orientation Perception and Quantitative Integration
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 7, Pages: 1482-1493(2026)
- 作者机构：
  
  1.深圳大学土木与交通工程学院，广东深圳，518000
  2.深圳大学建筑与城市规划学院，广东深圳，518000
  3.广东省城市空间信息工程重点实验室，广东深圳，518000
  4.自然资源部大湾区地理环境监测重点实验室，广东深圳，518000
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20250060
  CLC： P237
- Received：13 August 2025，
  
  Published：05 July 2026
- 稿件说明：
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朱家松,林玮妍,杨韵，等.朝向感知与量化集成的屋顶光伏潜力估算方法[J].武汉大学学报（信息科学版）,2026,51(7):1482-1493.

ZHU Jiasong,LIN Weiyan,YANG Yun,et al.Rooftop Photovoltaic Potential Estimation Approach Based on Orientation Perception and Quantitative Integration[J].Geomatics and Information Science of Wuhan University,2026,51(7):1482-1493.
朱家松,林玮妍,杨韵，等.朝向感知与量化集成的屋顶光伏潜力估算方法[J].武汉大学学报（信息科学版）,2026,51(7):1482-1493. DOI：10.13203/j.whugis20250060

ZHU Jiasong,LIN Weiyan,YANG Yun,et al.Rooftop Photovoltaic Potential Estimation Approach Based on Orientation Perception and Quantitative Integration[J].Geomatics and Information Science of Wuhan University,2026,51(7):1482-1493. DOI：10.13203/j.whugis20250060

摘要

屋顶光伏发电作为城市可再生能源的重要组成部分，其发电效率的精准评估依赖于屋顶结构特征的准确判别。其中，屋顶面积及朝向识别是准确评估光伏发电潜能的关键步骤。当前主流方法是通过深度学习模型，从航拍影像中提取屋顶面积和朝向来估计光伏潜力。然而，影像采集时太阳方位角差的不同会导致屋顶阴影分布存在差异，使得相同朝向的屋顶在影像上呈现出显著的外观差异，从而导致深度学习模型对屋顶结构特征的错误判别。为了解决上述问题，提出了一种新的太阳能潜力估算框架。首先，通过语义分割网络DeepLabv3+，从航拍影像中识别城市中屋顶区域及各部分的朝向；然后，针对阴影区域引发的屋顶对向判别混淆问题，结合光伏地理信息系统数据和多向量化集成策略计算太阳能潜力值。屋顶信息数据集的实验结果表明，相比于其他语义分割模型，所提估算框架采用DeepLabv3+时性能表现更优，多向量化集成策略能够有效提高太阳能估算的精度。

Abstract

Objectives

As an essential component of urban renewable energy

the assessment of rooftop photovoltaic (PV) potential depends on the accurate identification of structural characteristics. In particular

determining rooftop area and orientation is crucial for reliable PV capacity evaluation. Current mainstream approaches employ deep learning models to extract rooftop area and orientation from aerial imagery for PV resource assessment. However

variations in solar azimuth angles during image acquisition lead to differences in rooftop shadow distribution

resulting in significant apparent discrepancies among rooftops with the same orientation. Consequently

deep learning models may misclassify rooftop structural features.

Methods

To address this core limitation

a novel framework for robust solar energy potential estimation is proposed. First

the advanced semantic segmentation architecture DeepLabv3+ is employed to simultaneously delineate rooftop boundaries and classify each rooftop region into primary orientation categories from input aerial imagery. Second

recognizing the susceptibility of visual-based orientation classification to shadow-induced errors

the framework incorporates a refinement stage. This stage integrates photovoltaic geographical information system (PVGIS) data

a validated source for location-specific historical and modeled solar radiation data. A multi-orientation quantitative integration strategy is employed

which utilizes the PVGIS to calculate the theoretical annual solar irradiation. By weighting and synthesizing these PVGIS-calculated values

the strategy effectively mitigates errors caused by visual misclassification.

Results

The results on the roof information dataset indicate that DeepLabv3+ outperforms other models and improves the performance of the proposed framework. The quantitative integration strategy narrows the relative error in solar potential estimation. For 4

and 16 orientation categories

the proposed framework reduces estimation errors to 0.15%

0.76%

and 1.07%

respectively. By excluding disturbances of flat roofs

relative errors are further reduced to 0.04%

0.49%

and 0.68%

respectively

which confirming the effectiveness of our framework.

Conclusions

By combining high-resolution rooftop segmentation and orientation with geospatial irradiation modeling and a quantitative mechanism to resolve orientation ambiguity

the proposed framework effectively mitigates a primary source of error in aerial image-based solar potential assessment. This significantly enhances estimation accuracy

providing a more reliable tool for urban-scale renewable energy planning and investment decisions.

关键词

Keywords

references

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