Research on the prediction and alarm mechanism of grid dispatch automation data based on the XGBoost-informer model

With the swift advancement of smart grids, the power grid dispatch automation system holds a pivotal position in guaranteeing the stability and security of power supply. However, the current power grid dispatch system encounters difficulties in efficiently processing and intelligently analyzing extensive monitoring data. This paper introduces a power grid dispatch automation data prediction and alarm mechanism, which is grounded on the XGBoost-Informer model. This mechanism achieves intelligent prediction and real-time alerting of the power grid status through the integrated analysis of multi-source monitoring data. Experimental findings indicate that this mechanism can substantially enhance the processing efficiency of power grid dispatch data, precisely predict the power grid status, and promptly trigger alarms in abnormal situations, thereby providing robust support for the safe and stable operation of the power grid.