Research on Power Load Forecasting Method Based on LSTM Model

被引：0

作者：

Cui, Can ^{[1
]}

He, Ming ^{[2
]}

Di, Fangchun ^{[1
]}

Lu, Yi ^{[2
]}

Dai, Yuhan ^{[2
]}

Lv, Fengyi ^{[3
]}

机构：

[1] China Elect Power Res Inst, Beijing Key Lab Res & Syst Evaluat Power Dispatch, Beijing, Peoples R China

[2] State Grid Sichuan Elect Power Co, Chengdu, Peoples R China

[3] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha, Peoples R China

来源：

PROCEEDINGS OF 2020 IEEE 5TH INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC 2020) | 2020年

关键词：

Power dispatching and controlling; deep learning; power load forecasting; LSTM; STORAGE; CLOUD;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Power load forecasting is an important part of power system planning and the foundation of power system economic operation. It is very important for power system planning and operation. The LSTM forecast model is used to Get more accurate power load prediction results. According to the time series rule of power load, the LSTM prediction model for load prediction is established in this paper. A verification experiment has been done to reflect the effect of this method. Experimental results show that accuracy of power load prediction is increased by using LSTM model.

引用

页码：1657 / 1660

页数：4

共 50 条

[1] Research on urban power load forecasting based on improved LSTM
Zhenglei, Zhou
Jun, Chen
Zhou, Yang
Wenguang, Wu
Hong, Ding
[J]. Frontiers in Energy Research, 2024, 12
[2] Power Load Forecasting Based on the Combined Model of LSTM and XGBoost
Li, Chen
Chen, Zhenyu
Liu, Jinbo
Li, Dapeng
Gao, Xingyu
Di, Fangchun
Li, Lixin
Ji, Xiaohui
[J]. PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE (PRAI 2019), 2019, : 46 - 51
[3] A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security
Lv, Lingling
Wu, Zongyu
Zhang, Jinhua
Zhang, Lei
Tan, Zhiyuan
Tian, Zhihong
[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (09) : 6474 - 6482
[4] Research on Short-Term Power Load Forecasting Method Based on PCA-VMD-LSTM-MTL
Jia, Wei
Chen, Jiefeng
Luo, Shaowei
Huang, Yuchun
Zhang, Bin
[J]. 2023 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA, I&CPS ASIA, 2023, : 440 - 444
[5] Load forecasting method based on CNN and extended LSTM
Wang, Chong
Li, Xiang
Shi, Yan
Jiang, Wenshuai
Song, Qiong
Li, Xueling
[J]. ENERGY REPORTS, 2024, 12 : 2452 - 2461
[6] Research on power energy load forecasting method based on KNN
Li, Fan
Jin, Guang
[J]. INTERNATIONAL JOURNAL OF AMBIENT ENERGY, 2019, 43 (01) : 946 - 951
[7] Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM
Tang, Jiajie
Zhao, Jie
Zou, Hongliang
Ma, Gaoyuan
Wu, Jun
Jiang, Xu
Zhang, Huaixun
[J]. SUSTAINABILITY, 2021, 13 (19)
[8] A research on power load forecasting model based on data mining
Sun, Fuyu
Yang, Yunshi
[J]. RESEARCH AND PRACTICAL ISSUES OF ENTERPRISE INFORMATION SYSTEMS II, VOL 2, 2008, 255 : 1369 - +
[9] Research on Cloud Computing load forecasting based on LSTM-ARIMA combined model
Liu, Xiaomin
Xie, Xiaolan
Guo, Qiang
[J]. 2022 TENTH INTERNATIONAL CONFERENCE ON ADVANCED CLOUD AND BIG DATA, CBD, 2022, : 19 - 23
[10] Power Load Forecasting Based on LSTM Deep Learning Algorithm
Wu, Dalei
Liang, Shuhua
Chen, Changji
Chen, Yupei
Wang, Pishi
Long, Zhiyuan
[J]. Tehnicki Vjesnik, 2024, 31 (06): : 2156 - 2160

← 1 2 3 4 5 →