The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and long-term reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis heavily depends on the quality of PV timeseries data. This paper proposes a novel Spatio-Temporal Denoising Graph Autoencoder (STD-GAE) framework to impute missing PV Power Data. STD-GAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. Experimental results show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods such as MIDA and LRTC-TNN.

翻译：全球光伏市场与实时数据记录仪的整合，使得大规模光伏数据分析管线能够用于发电预测和光伏组群的长期可靠性评估。然而，光伏数据分析的性能在很大程度上取决于光伏时序数据的质量。本文提出了一种新颖的时空去噪图自编码器（STD-GAE）框架，用于补全缺失的光伏功率数据。STD-GAE利用来自领域知识的时间相关性、空间一致性和值依赖关系来恢复缺失数据。实验结果表明，与MIDA和LRTC-TNN等最先进的数据补全方法相比，STD-GAE在补全精度上可实现43.14%的提升，并且对缺失率、不同季节和缺失场景的敏感性较低。