Abnormal Behavior Modelling of Electricity Market by Artificial Intelligence

The rapid development of electric power technology has exacerbated issues with anomalies in market transactions, making effective identification and regulation of these anomalies a pressing task. This study proposes a trading behavior analysis model for market players, leveraging a stepwise regression algorithm and introducing a monitoring module based on the ARM Cortex series microcontroller. The model enables deep assessment of transaction data, timely detection of anomalies, and pattern mining for accurate regulatory support. The monitoring module enhances real-time monitoring and dynamic feedback, leveraging the ARM Cortex microcontroller's efficient data processing and low-power characteristics. Experimental validation shows the model's significant advantages in evaluation and error control, effectively identifying anomalies and demonstrating its potential to enhance electricity market regulation.