Abstract: Considering that the randomness of the new power load is enhanced, the traditional accurate forecasting methods can not meet the requirements, an EMD-BiLSTM-ANFIS (Empirical Mode Decomposition Bi-directional Long Short Term Memory Adaptive Network is proposed based Fuzzy Inference System) quantile method to predict the load probability density. It replaces the accurate value of point prediction with the load prediction interval, which can provide more data for power System analysis and decision-making, The reliability of prediction is enhanced. First, the original load sequence is decomposed into several components by EMD, and then divided into three types of components by calculating the sample entropy. Then, the reconstructed three types of components and the characteristics of external factors screened by correlation. And they are used together with the Bilstm and ANFIS models for prediction training and QR(Quantile Regression), and accumulate the results of the prediction interval of the components to obtain the prediction interval of the final load. Finally, the kernel density estimation is used to output the user load probability density prediction results at any time. The validity of this method is proved by comparing the point prediction and interval prediction results with CNN- BiLSTM(Convolutional Neural Network-Bidirectional Long Short-Term Memory) and LSTM ( Long Short-Term Memory)models. 

Key words: empirical mode decomposition, two way long and short term neural network, fuzzy inference system, quantile regression, probability density prediction