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

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...

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Bibliographic Details
Main Authors: Huiting Zheng, Jiabin Yuan, Long Chen
Format: Article
Language:English
Published: MDPI AG 2017-08-01
Series:Energies
Subjects:
long short-term memory neural networks
similar day
extreme gradient boosting
k-means
empirical mode decomposition
short-term load forecasting
Online Access:https://www.mdpi.com/1996-1073/10/8/1168

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https://www.mdpi.com/1996-1073/10/8/1168

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