Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network
Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-09-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/17/8129 |
id |
doaj-fc88d77bfdfb48c4be29044da2d88256 |
---|---|
record_format |
Article |
spelling |
doaj-fc88d77bfdfb48c4be29044da2d882562021-09-09T13:39:28ZengMDPI AGApplied Sciences2076-34172021-09-01118129812910.3390/app11178129Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural NetworkChangchun Cai0Yuan Tao1Tianqi Zhu2Zhixiang Deng3Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, ChinaJiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, ChinaJiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, ChinaJiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, ChinaAccurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data.https://www.mdpi.com/2076-3417/11/17/8129bidirectional long short-term memorymulti-layer stackedneural networkshort-term load forecastingpower system |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Changchun Cai Yuan Tao Tianqi Zhu Zhixiang Deng |
spellingShingle |
Changchun Cai Yuan Tao Tianqi Zhu Zhixiang Deng Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network Applied Sciences bidirectional long short-term memory multi-layer stacked neural network short-term load forecasting power system |
author_facet |
Changchun Cai Yuan Tao Tianqi Zhu Zhixiang Deng |
author_sort |
Changchun Cai |
title |
Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network |
title_short |
Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network |
title_full |
Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network |
title_fullStr |
Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network |
title_full_unstemmed |
Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network |
title_sort |
short-term load forecasting based on deep learning bidirectional lstm neural network |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-09-01 |
description |
Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data. |
topic |
bidirectional long short-term memory multi-layer stacked neural network short-term load forecasting power system |
url |
https://www.mdpi.com/2076-3417/11/17/8129 |
work_keys_str_mv |
AT changchuncai shorttermloadforecastingbasedondeeplearningbidirectionallstmneuralnetwork AT yuantao shorttermloadforecastingbasedondeeplearningbidirectionallstmneuralnetwork AT tianqizhu shorttermloadforecastingbasedondeeplearningbidirectionallstmneuralnetwork AT zhixiangdeng shorttermloadforecastingbasedondeeplearningbidirectionallstmneuralnetwork |
_version_ |
1717760802333655040 |