Big Data Anomaly Prediction Algorithm of Smart City Power Internet of Things Based on Parallel Random Forest

Abstract

Abstract Conventional power data prediction algorithms easily lead to the loss of key power data in a complex wireless network environment. Therefore, a power big data anomaly prediction algorithm based on parallel random forest is proposed. According to the power big data anomaly prediction algorithm based on parallel random forest, a network power big data anomaly prediction algorithm platform is established, and based on the platform, key data features such as user address and power transmission packet structure are extracted according to the category of power users. According to the relationship between power shunt function value and power data unit density, the parameter value of the system and finally the reasonable anomaly prediction of power big data in wireless network are determined. Finally, filter the classified data through attribute reduction and gene expression programming algorithm to obtain the data to be encrypted and complete the research on the anomaly prediction algorithm of power big data. Experimental results show that the proposed algorithm has better prediction performance and can ensure better data prediction effect.