A Contrastive-Learning-Based Abnormal Electricity Load Detection Method

The detection of abnormal electricity load data using big data analysis technology has garnered considerable attention from the academic community. However, traditional methods often require ample labeled data to train the model which increases the cost. This article tackles the issues of high model training costs and poor transferability associated with traditional supervised learning methods. We propose a contrastive learning network-based abnormal electricity load detection method (ED-CLN). First, our model enhances training samples through data augmentation and learns the similarities and differences between samples from temporal and contextual perspectives of the sample sequence. This approach enables the acquisition of common feature representations for model training tasks. Then, the weight data of the model trained using unlabeled data is migrated to the supervised training model. Finally, the trained source model is fine-tuned for abnormal electricity load data detection tasks to improve the overall learning effectiveness of the model. The results demonstrate that ED-CLN outperforms both supervised learning methods and various classic contrastive learning methods in anomaly detection, which can effectively identify the abnormal electricity load data.