Dynamic Multi-Indicator Fusion Model for Real-Time Prediction Analysis

Power load forecasting is influenced by various factors, including meteorological, economic, and social factors. Considering all influencing factors will significantly increase the model complexity, affect accuracy and prediction timeliness. In addition, as time, region, and season change, the different influence factors will also change, which could lead a great impact on accuracy to existing prediction models. In order to improve the accuracy of real-time predictive analysis, this paper proposes a Dynamic Multi Indicator Fusion (DMIF) model for processing the calculation of real-time load impact indicators and adaptively predicting and adjusting the weights of the most influential indicators to reduce complex calculations and improve real-time prediction accuracy. In the final experiment, our model showed high prediction accuracy and fast calculation speed, while reducing information redundancy in multiple indicators. Therefore, in the context of smart grids, this method has practical application value for the stable operation of power grid systems.