Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation
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 predictio...
Main Authors: | Huiting Zheng, Jiabin Yuan, Long Chen |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2017-08-01
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Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/10/8/1168 |
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