Extraction of High-Resolution Air Conditioning Load Profiles From Low-Resolution Smart Meter: A Semi-supervised Nonintrusive Approach

Air conditioning load (ACL) is an important flexibility resource in smart grids, and its analysis and evaluation have significant implications for demand response, which depends on the nonintrusive extraction of high-frequency ACL profiles. Existing methods based on thermodynamic models require high parameter accuracy, and high-frequency data-driven methods incur high costs for data collection and storage, which limit their widespread application. Considering that smart meters are widely deployed as low-frequency data sources, in this article, we propose a semi-supervised ACL monitoring method based on a small number of high-frequency ACL feature samples in low-frequency data scenarios. First, we introduce a low-frequency ACL state recognition model based on self-supervised contrastive representation learning, which enhances the smart meter data feature unsupervisedly. Then, by merging the identified ACL state with smart meter data, we present an ACL super-resolution generative adversarial network with a specific aggregated adversarial loss, for the super-resolution extraction of ACL curves. Validation on the Dataport dataset shows that the proposed method improves the accuracy of ACL state recognition under low-resolution smart meter data and can accurately extract and reconstruct high-resolution ACL curves.