Short-term Load Forecasting with LSTM based Ensemble Learning

被引：13

作者：

Wang, Lingxiao ^{[1
]}

Mao, Shiwen ^{[1
]}

Wilamowski, Bogdan ^{[1
]}

机构：

[1] Auburn Univ, Dept Elect & Comp Engn, Auburn, AL 36849 USA

来源：

2019 INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA) | 2019年

关键词：

Short-term load forecasting; Deep learning; Ensemble learning; Long short-term memory (LSTM); Levenberg-Marquardt (LM) algorithm; ALGORITHM; NETWORKS; MODEL;

D O I：

10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00145

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, a short-term load forecasting framework with long short-term memory (LSTM)-based ensemble learning is proposed. To fully exploit the correlation in data for accurate load forecasting, the data is first clustered and each cluster is used to train an LSTM model. Then a Fully Connected Cascade (FCC) Neural Network is incorporated for ensemble learning, which is solved by an enhanced Levenberg-Marquardt (LM) training algorithm. The proposed framework is tested with a public dataset, where its superior performance over several baseline schemes is demonstrated.

引用

页码：793 / 800

页数：8