Transient Emissions Forecasting of Off-Road Construction Machinery Based on Long Short-Term Memory Network

被引：0

作者：

Li, Tengteng ^{[1
,2
]}

Jing, Xiaojun ^{[1
]}

Wang, Fengbin ^{[1
,2
]}

Wang, Xiaowei ^{[1
]}

Gao, Dongzhi ^{[1
]}

Cai, Xianyang ^{[3
]}

Tang, Bin ^{[3
]}

机构：

[1] CATARC Automot Test Ctr Tianjin Co Ltd, Tianjin 300300, Peoples R China

[2] Tianjin Univ, State Key Lab Engines, Tianjin 300072, Peoples R China

[3] Dalian Univ Technol, Sch Energy & Power Engn, Dalian 116023, Peoples R China

来源：

ENERGIES | 2024年 / 17卷 / 14期

关键词：

emissions forecasting; off-road machinery; long short-term memory network; PEMS test data; NOX EMISSIONS;

D O I：

10.3390/en17143373

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Off-road machinery is one of the significant contributors to air pollution due to its large quantity. In this study, a deep learning model was developed to predict the transient engine emissions of CO, NO, NO2, and NOx, which are the main pollutants emitted by off-road machinery. A portable emission measurement system (PEMS) was used to measure the exhaust emission features of four types of construction machinery. The raw PEMS data were preprocessed using data compensation, local linear regression, and normalization to ensure that the data could handle transient conditions. The proposed model utilizes the preprocessing PEMS data to estimate the CO, NO, NO2, and NOx emissions from off-road machinery using a recurrent neural network (RNN) based on a long short-term memory (LSTM) model. The experimental results show that the proposed method can effectively predict the emissions from off-road construction machinery under transient conditions and can be applied to controlling the emissions from off-road construction machinery.

引用

页数：16

共 50 条

[21] Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting
Xu, Liwen
Li, Chengdong
Xie, Xiuying
Zhang, Guiqing
INFORMATION, 2018, 9 (07)
[22] Short-Term Photovoltaic Power Forecasting Based on Long Short Term Memory Neural Network and Attention Mechanism
Zhou, Hangxia
Zhang, Yujin
Yang, Lingfan
Liu, Qian
Yan, Ke
Du, Yang
IEEE ACCESS, 2019, 7 : 78063 - 78074
[23] Flight short-term booking demand forecasting based on a long short-term network
He, Haonan
Chen, Liangyu
Wang, Shanyong
COMPUTERS & INDUSTRIAL ENGINEERING, 2023, 186
[24] Short-term Load Forecasting with Distributed Long Short-Term Memory
Dong, Yi
Chen, Yang
Zhao, Xingyu
Huang, Xiaowei
2023 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE, ISGT, 2023,
[25] ECG Forecasting System Based on Long Short-Term Memory
Zacarias, Henriques
Marques, Joao Alexandre Lobo
Felizardo, Virginie
Pourvahab, Mehran
Garcia, Nuno M.
BIOENGINEERING-BASEL, 2024, 11 (01):
[26] Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network
Hu S.
Cai W.
Liu J.
Shi H.
Yu J.
Journal of Computing and Information Technology, 2023, 31 (03) : 151 - 166
[27] SHORT-TERM LOAD FORECASTING BASED ON IMPROVED PARTICLE SWARM OPTIMISATION AND LONG SHORT-TERM MEMORY NETWORK1
Yan, Qingyou
Wang, Yonghua
Qin, Guangyu
Zhu, Jingyao
Zidonis, Zilvinas
TRANSFORMATIONS IN BUSINESS & ECONOMICS, 2021, 20 (01): : 154 - 177
[28] Short-Term Traffic Congestion Forecasting Using Attention-Based Long Short-Term Memory Recurrent Neural Network
Zhang, Tianlin
Liu, Ying
Cui, Zhenyu
Leng, Jiaxu
Xie, Weihong
Zhang, Liang
COMPUTATIONAL SCIENCE - ICCS 2019, PT III, 2019, 11538 : 304 - 314
[29] Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network
Michael, Neethu Elizabeth
Hasan, Shazia
Al-Durra, Ahmed
Mishra, Manohar
APPLIED ENERGY, 2022, 324
[30] Short-Term Photovoltaic Power Forecast Based on Long Short-Term Memory Network
Shi, Min
Xu, Ke
Wang, Jue
Yin, Rui
Wang, Tieqiang
Yong, Taiyou
Hongyuan, Tianjin
PROCEEDINGS OF 2019 IEEE 3RD INTERNATIONAL ELECTRICAL AND ENERGY CONFERENCE (CIEEC), 2019, : 2110 - 2116

← 1 2 3 4 5 →