Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant

Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant

A multi-layer LSTM (Long short-term memory) model is proposed for condenser vacuum degree prediction of power plants. Firstly, Min-max normalization is used to pre-process the input data. Then, the model proposes the two-layer LSTM architecture to identify the time series pattern effectively. ADAM（A...

Full description

Bibliographic Details
Main Authors: Lu Kuan, Gao Song, Xiangkun Pang, lingkai Zhu, Meng Xiangrong, Sun Wenxue
Format: Article
Language:English
Published: EDP Sciences 2019-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/62/e3sconf_icbte2019_01012.pdf
id doaj-1a957af5833b44f5a31f7cb5aff81b89
record_format Article
spelling doaj-1a957af5833b44f5a31f7cb5aff81b892021-04-02T09:50:22ZengEDP SciencesE3S Web of Conferences2267-12422019-01-011360101210.1051/e3sconf/201913601012e3sconf_icbte2019_01012Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power PlantLu Kuan0Gao Song1Xiangkun Pang2lingkai Zhu3Meng Xiangrong4Sun Wenxue5State Grid Shandong Electric Power Research InstituteState Grid Shandong Electric Power Research InstituteState Grid Shandong Electric Power Research InstituteState Grid Shandong Electric Power Research InstituteState Grid Shandong Electric Power Research InstituteState Grid Zhangqiu Power Supply CompanyA multi-layer LSTM (Long short-term memory) model is proposed for condenser vacuum degree prediction of power plants. Firstly, Min-max normalization is used to pre-process the input data. Then, the model proposes the two-layer LSTM architecture to identify the time series pattern effectively. ADAM（Adaptive moment）optimizer is selected to find the optimum parameters for the model during training. Under the proposed forecasting framework, experiments illustrates that the two-layer LSTM model can give a more accurate forecast to the condenser vacuum degree compared with other simple RNN (Recurrent Neural Network) and one-layer LSTM model.https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/62/e3sconf_icbte2019_01012.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Lu Kuan
Gao Song
Xiangkun Pang
lingkai Zhu
Meng Xiangrong
Sun Wenxue
spellingShingle Lu Kuan
Gao Song
Xiangkun Pang
lingkai Zhu
Meng Xiangrong
Sun Wenxue
Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant
E3S Web of Conferences
author_facet Lu Kuan
Gao Song
Xiangkun Pang
lingkai Zhu
Meng Xiangrong
Sun Wenxue
author_sort Lu Kuan
title Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant
title_short Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant
title_full Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant
title_fullStr Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant
title_full_unstemmed Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant
title_sort multi-layer long short-term memory based condenser vacuum degree prediction model on power plant
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2019-01-01
description A multi-layer LSTM (Long short-term memory) model is proposed for condenser vacuum degree prediction of power plants. Firstly, Min-max normalization is used to pre-process the input data. Then, the model proposes the two-layer LSTM architecture to identify the time series pattern effectively. ADAM（Adaptive moment）optimizer is selected to find the optimum parameters for the model during training. Under the proposed forecasting framework, experiments illustrates that the two-layer LSTM model can give a more accurate forecast to the condenser vacuum degree compared with other simple RNN (Recurrent Neural Network) and one-layer LSTM model.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/62/e3sconf_icbte2019_01012.pdf
work_keys_str_mv AT lukuan multilayerlongshorttermmemorybasedcondenservacuumdegreepredictionmodelonpowerplant
AT gaosong multilayerlongshorttermmemorybasedcondenservacuumdegreepredictionmodelonpowerplant
AT xiangkunpang multilayerlongshorttermmemorybasedcondenservacuumdegreepredictionmodelonpowerplant
AT lingkaizhu multilayerlongshorttermmemorybasedcondenservacuumdegreepredictionmodelonpowerplant
AT mengxiangrong multilayerlongshorttermmemorybasedcondenservacuumdegreepredictionmodelonpowerplant
AT sunwenxue multilayerlongshorttermmemorybasedcondenservacuumdegreepredictionmodelonpowerplant
_version_ 1724168626811961344

Similar Items