Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:ENERGIES

Key Words:long short-term memory neural networks similar day extreme gradient boosting k-means empirical mode decomposition short-term load forecasting

Abstract:Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.

ISSN No.:1996-1073

Translation or Not:no

Date of Publication:2017-08-01

Co-author:Zheng, Huiting,cl

Correspondence Author:Yuan Jiabing