Design of short-term load forecasting method considering user behavior

Abstract

In order to ensure optimize the short-term power load forecasting effect, load forecasting method design taking into account the user behavior is studied. Mining user power consumption behavior data from power user power consumption information acquisition system, and suppressing power consumption behavior data noise through empirical mode decomposition algorithm; Input the denoised user behavior data into the improved Relief algorithm, extract the user power consumption behavior characteristics, eliminate redundant features through correlation analysis, and cluster the user behavior characteristics using K-means clustering algorithm to reduce the dimension of the feature data and the complexity of the features, then input the clustered features into the online extreme learning machine (OS-ELM), load forecasting is realized through online learning. Experiments show that this method can accurately predict the power consumption of each type of users in different time periods, and its prediction results have a high interval coverage.