Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

Huiting Zheng, Jiabin Yuan and Long Chen
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Huiting Zheng: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Jiabin Yuan: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Long Chen: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Energies, 2017, vol. 10, issue 8, 1-20

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.

Keywords: long short-term memory neural networks; similar day; extreme gradient boosting; k-means; empirical mode decomposition; short-term load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (79)

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