基于变分自编码器的配电网电缆网络数据增强机会
Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders
AI总结：
本研究利用变分自编码器解决配电网电缆数据不完整问题，通过丹麦案例验证了其在数据增强和缺失信息填补方面的潜力，并提出了未来改进方向。
摘要：
配电电缆网络面临数据不完整和不平衡的问题，这阻碍了机器学习模型在预测性维护和可靠性评估中的有效性。诸如电缆安装日期等特征经常缺失。为解决数据稀缺问题，本研究探讨了变分自编码器（VAEs）在数据增强、合成数据生成、不平衡数据处理和异常检测中的应用。基于丹麦的概念验证案例研究，针对电缆网络资产登记中缺失年龄信息的填补，分析强调了生成模型支持数据驱动维护的潜力。然而，研究也指出了若干改进领域，包括增强特征重要性分析、纳入网络特征和外部特征，以及处理缺失数据中的偏差。未来举措应通过纳入半监督学习、先进采样技术以及包括低压网络在内的更多配电网元件来扩展VAEs的应用。
英文摘要：
Electricity distribution cable networks suffer from incomplete and unbalanced data, hindering the effectiveness of machine learning models for predictive maintenance and reliability evaluation. Features such as the installation date of the cables are frequently missing. To address data scarcity, this study investigates the application of Variational Autoencoders (VAEs) for data enrichment, synthetic data generation, imbalanced data handling, and outlier detection. Based on a proof-of-concept case study for Denmark, targeting the imputation of missing age information in cable network asset registers, the analysis underlines the potential of generative models to support data-driven maintenance. However, the study also highlights several areas for improvement, including enhanced feature importance analysis, incorporating network characteristics and external features, and handling biases in missing data. Future initiatives should expand the application of VAEs by incorporating semi-supervised learning, advanced sampling techniques, and additional distribution grid elements, including low-voltage networks, into the analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
