Abnormal behavior modeling and intelligent error prevention algorithm in AI load forecasting

Aiming at the problem of violent fluctuations caused by sudden abnormalities in power load forecasting due to extreme weather, equipment failure and other reasons, this paper introduces the Informer model to model sudden abnormal behaviors and uses Generative Adversarial Network (GAN) for intelligent error prevention. The GAN discriminator identifies and marks abnormal points, and the generator repairs abnormal data. Comparative experiments are conducted to verify the sudden anomaly recognition and repair capabilities of the intelligent error prevention mechanism in this paper. The ROC curve results show that the AUC value of the anomaly recognition designed in this paper is 0.95, significantly higher than 0.48 of the baseline model and 0.87 of the GAN model. In most cases, the load of the power system studied in this paper can respond in a shorter time, providing an efficient and intelligent solution for the power system to deal with emergencies. This enhances power load forecasting accuracy and system stability, offering new perspectives for intelligent error prevention and anomaly management in the power industry.