Abstract: The development of smart grids has brought about a massive amount of energy data,and data quality is the foundation for tasks such as data value mining.However,during the collection and transmission process of large-scale photovoltaic energy data from multiple sources,it is inevitable to encounter abnormal data,thus requiring data cleaning.Currently,traditional statistical machine learning-based data cleaning models have certain limitations.This paper proposes an improved K-means clustering model based on the Transformer autoencoder structure for energy big data cleaning.It adaptively determines the number of clusters using the elbow method and utilizes autoencoder networks to compress and reconstruct data within clusters,thereby detecting and recovering abnormal data.Additionally,the proposed model employs the multi-head attention mechanism of Transformer to learn the relevant features among the data,enhancing the screening capability for abnormal data.Experimental results on a publicly available photovoltaic power generation dataset demonstrate that,compared to other methods,the proposed model achieves better performance in detecting abnormal data,with a screening accuracy of over 96%.Moreover,it is capable of recovering abnormal data to a certain extent,providing effective support for the application of energy big data.

Key words: Autoencoder, Data cleaning, Anomaly detection, Transformer, K-means