Unlocking solar potential through machine learning techniques for roof geometry prediction- A review

IF 6 2区 工程技术 Q2 ENERGY & FUELS
Dina Shata , Sara Omrani , Robin Drogemuller , Simon Denman , Ayman Wagdy
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引用次数: 0

Abstract

The global transition to renewable energy underscores the necessity of optimising residential solar photovoltaic (PV) systems. Accurate roof geometry prediction is critical for enhancing the efficiency and scalability of rooftop solar installations. This review examines recent machine learning (ML) methodologies designed to enhance roof geometry prediction accuracy. It synthesises recent techniques, including Convolutional Neural Networks (CNNs) and multi-task learning frameworks, to evaluate improvements in predictive accuracy and computational efficiency. The review also highlights challenges such as data availability, diverse dataset integration (e.g., aerial imagery and LiDAR scans), and geographic generalisation, proposing opportunities for future research. Opportunities for future research include developing robust, scalable ML models and automated data preprocessing frameworks. Furthermore, the review advocates for accessible, open-source tools to accelerate the global adoption of sustainable solar energy. Overall, the findings aim to advance roof geometry predictions, improving the performance and broader implementation of residential solar PV systems.
通过房顶几何形状预测的机器学习技术释放太阳能潜力-综述
全球向可再生能源的过渡强调了优化住宅太阳能光伏(PV)系统的必要性。准确的屋顶几何形状预测对于提高屋顶太阳能装置的效率和可扩展性至关重要。本文综述了最近用于提高顶板几何形状预测精度的机器学习(ML)方法。它综合了包括卷积神经网络(cnn)和多任务学习框架在内的最新技术,以评估预测准确性和计算效率的改进。该综述还强调了诸如数据可用性、多样化数据集集成(例如,航空图像和激光雷达扫描)和地理泛化等挑战,为未来的研究提出了机会。未来研究的机会包括开发强大的、可扩展的ML模型和自动化数据预处理框架。此外,该报告还提倡使用易于获取的开源工具,以加速全球对可持续太阳能的采用。总的来说,研究结果旨在推进屋顶几何形状预测,提高住宅太阳能光伏系统的性能和更广泛的实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Solar Energy
Solar Energy 工程技术-能源与燃料
CiteScore
13.90
自引率
9.00%
发文量
0
审稿时长
47 days
期刊介绍: Solar Energy welcomes manuscripts presenting information not previously published in journals on any aspect of solar energy research, development, application, measurement or policy. The term "solar energy" in this context includes the indirect uses such as wind energy and biomass
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