CC-GAIN: Clustering and classification-based generative adversarial imputation network for missing electricity consumption data imputation

Abstract

The widespread use of data across various fields has made missing data imputation technology a crucial tool. High-quality data is essential for effective energy management in smart grid environments, but essential data may be absent during collection. Despite the development of various imputation techniques for electricity consumption data, previous studies have made limited efforts to address the distinctive characteristics of this domain adequately. To overcome this limitation, a high-performance imputation model must effectively leverage time-series and pattern features of the data. This study proposes a novel missing data imputation model based on unsupervised clustering and classification-based generative adversarial imputation network(CC-GAIN), which excels in pattern classification and feature extraction. The CC-GAIN model demonstrates superior performance across all types and rates of missing data, outperforming alternative models.