Low-Carbon Power Grid Economic Assessment Model Based on CNN and LSTM

被引：0

作者：

Wu, Yinghen ^{[1
]}

He, Wenting ^{[1
]}

Li, Mingjia ^{[1
]}

Zhang, Rui ^{[2
]}

Zhang, Hongfeng ^{[1
]}

Jie, Suo Lang Bu Duo ^{[1
]}

Yang, Yuxin ^{[1
]}

机构：

[1] State Grid Tibet Elect Power Co Ltd, Tibet, Peoples R China

[2] Nanjing Univ Posts & Telecommun, Nanjing, Peoples R China

来源：

2023 IEEE INTERNATIONAL CONFERENCES ON INTERNET OF THINGS, ITHINGS IEEE GREEN COMPUTING AND COMMUNICATIONS, GREENCOM IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING, CPSCOM IEEE SMART DATA, SMARTDATA AND IEEE CONGRESS ON CYBERMATICS,CYBERMATICS | 2024年

关键词：

Energy-saving and Economic Operation; Low-carbon; CNN; LSTM; Fuzzy Comprehensive Evaluation;

D O I：

10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00135

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the context of a complex power grid environment, it is crucial to analyze and optimize power system planning and operational strategies to enhance energy efficiency and reduce the carbon emissions intensity of the system. Given the multitude of indicators and external factors affecting grid planning and development, accurately assessing and rating the development status of the grid can help in understanding developmental patterns. Therefore, this paper employs a combined CNN and LSTM model to predict grid benefits. Subsequently, based on the prediction results, it utilizes fuzzy comprehensive evaluation to conduct an economic assessment, providing planning regulators with insights into the economic feasibility and associated risks of grid projects. The proposed methodology is applied to actual grid data through case analysis, demonstrating that the rating results align with reality. Consequently, this assessment model exhibits a high level of accuracy and superiority.

引用

页码：771 / 775

页数：5

共 25 条

[1] Review of deep learning: concepts, CNN architectures, challenges, applications, future directions [J].

Alzubaidi, Laith ;

Zhang, Jinglan ;

Humaidi, Amjad J. ;

Al-Dujaili, Ayad ;

Duan, Ye ;

Al-Shamma, Omran ;

Santamaria, J. ;

Fadhel, Mohammed A. ;

Al-Amidie, Muthana ;

Farhan, Laith .

JOURNAL OF BIG DATA, 2021, 8 (01)

[2] Predicting stock market index using LSTM [J].

Bhandari, Hum Nath ;

Rimal, Binod ;

Pokhrel, Nawa Raj ;

Rimal, Ramchandra ;

Dahal, Keshab R. ;

Khatri, Rajendra K. C. .

MACHINE LEARNING WITH APPLICATIONS, 2022, 9

[3] A BP Neural Network-Based Hierarchical Investment Risk Evaluation Method Considering the Uncertainty and Coupling for the Power Grid [J].

Chen, Shujuan ;

Jiang, Qin ;

He, Yuqing ;

Huang, Ruanming ;

Li, Jiayong ;

Li, Can ;

Liao, Jing .

IEEE ACCESS, 2020, 8 :110279-110289

[4] The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation [J].

Chicco, Davide ;

Warrens, Matthijs J. ;

Jurman, Giuseppe .

PEERJ COMPUTER SCIENCE, 2021,

[5] PcGAN: A Noise Robust Conditional Generative Adversarial Network for One Shot Learning [J].

Deng, Lizhen ;

He, Chunming ;

Xu, Guoxia ;

Zhu, Hu ;

Wang, Hao .

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (12) :25249-25258

[6]

Dongfeng L., 2021, 2021 IEEE SUSTAINABL, P485

[7]

Haiji Zhao, 2021, IOP Conference Series: Earth and Environmental Science, V651, DOI 10.1088/1755-1315/651/2/022019

[8]

He CM, 2023, IEEE I CONF COMP VIS, P12577, DOI 10.1109/ICCV51070.2023.01159

[9] Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not [J].

Hodson, Timothy O. .

GEOSCIENTIFIC MODEL DEVELOPMENT, 2022, 15 (14) :5481-5487

[10] Multienergy Networks Analytics: Standardized Modeling, Optimization, and Low Carbon Analysis [J].

Huang, Wujing ;

Zhang, Ning ;

Cheng, Yaohua ;

Yang, Jingwei ;

Wang, Yi ;

Kang, Chongqing .

PROCEEDINGS OF THE IEEE, 2020, 108 (09) :1411-1436

← 1 2 3 →