Research on anomaly data mining method of new energy field stations based on improved Adaboost algorithm
Nan Wang, Yanzhuo Wang, Yan Cheng, Ti Guan, Qiang Ma, Shumin Sun, Yifei Guan, Yuejiao Wang, Shibo Wang · IOP Conference Series Earth and Environmental Science · 2021
Abstract Traditional anomalous data mining methods require a lot of prior knowledge, which leads to low data mining integrity and efficiency. For this reason, a new energy field abnormal data mining method based on improved Adaboost algorithm is proposed. After pre-processing the new energy field data, the algorithm is improved by introducing dynamic weight parameters to address the shortcomings of the Adaboost algorithm. After calculating the degree of data anomaly using the direct push belief machine, the neural network is used to reduce the error value of the Adaboost algorithm, and finally the output of the Adaboost algorithm is used to realize abnormal data mining. The simulation experiment proves that the researched abnormal data mining method has high data integrity and high efficiency.