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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：ENERGIES
关键字：long short-term memory neural networks similar day extreme gradient boosting k-means empirical mode decomposition short-term load forecasting
摘要：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号：1996-1073
是否译文：否
发表时间：2017-08-01
合写作者：Zheng, Huiting,陈龙
通讯作者：袁家斌