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Energies 2017, 10(8), 1168; https://doi.org/10.3390/en10081168

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

†,* ,
and
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 31 May 2017 / Revised: 9 July 2017 / Accepted: 1 August 2017 / Published: 8 August 2017
(This article belongs to the Section Electrical Power and Energy System)
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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. View Full-Text
Keywords: long short-term memory neural networks; similar day; extreme gradient boosting; k-means; empirical mode decomposition; short-term load forecasting long short-term memory neural networks; similar day; extreme gradient boosting; k-means; empirical mode decomposition; short-term load forecasting
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zheng, H.; Yuan, J.; Chen, L. Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation. Energies 2017, 10, 1168.

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