Study of the Load Forecasting based on AKDC and LSTM algorithms

Abstract

As one of the traditional research subjects of power system, load forecasting has always been a hot research direction of related experts and scholars. This paper uses an extended algorithm combining the advantages of adaptive K-means algorithm and distributed clustering algorithm, improves the traditional K-means algorithm, and uses LSTM algorithm to build a load prediction model. LSTMS can learn the advantages of long distance time series dependence to recognize load patterns from the horizontal (time dimension). The simulation results show that the LSTM algorithm based on Adam optimizer improves the accuracy of load prediction, and the proposed algorithm is verified.