Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network

被引：0
作者：
Yin R. [1 ]
Li D. [2 ]
Wang Y. [1 ]
Chen W. [2 ]
机构：
[1] State Grid Hebei Electric Power Company, Shijiazhuang
[2] China Electric Power Research Institute, Nanjing
来源：
Global Energy Interconnection | 2020年 / 3卷 / 06期
关键词：
Climate model; LSTM neural network; Monthly generation forecast; Wind power;
D O I：
10.1016/j.gloei.2021.01.003
中图分类号：
学科分类号：
摘要：
Predicting wind power generation over the medium and long term is helpful for dispatching departments, as it aids in constructing generation plans and electricity market transactions. This study presents a monthly wind power generation forecasting method based on a climate model and long short-term memory (LSTM) neural network. A nonlinear mapping model is established between the meteorological elements and wind power monthly utilization hours. After considering the meteorological data (as predicted for the future) and new installed capacity planning, the monthly wind power generation forecast results are output. A case study shows the effectiveness of the prediction method. © 2020 Global Energy Interconnection Development and Cooperation Organization
引用
收藏
页码：571 / 576
页数：5
相关论文
共 50 条
[41]   Short-Term Wind Power Prediction Based on Genetic Algorithm to Optimize RBF Neural Network [J].
Guo Pengfei ;
Qi Zhiyuan ;
Huang Wei .
PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), 2016, :1220-1223
[42]   SHORT-TERM WIND POWER PREDICTION BASED ON IWOA-SA-ELMAN NEURAL NETWORK [J].
Liu J. ;
Zhu X. ;
Yu J. .
Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2024, 45 (01) :143-150
[43]   Prediction Model of Wind Speed and Direction using Convolutional Neural Network - Long Short Term Memory [J].
Sari, Anggraini Puspita ;
Suzuki, Hiroshi ;
Kitajima, Takahiro ;
Yasuno, Takashi ;
Prasetya, Dwi Arman ;
Nachrowie, Nachrowie .
2020 IEEE INTERNATIONAL CONFERENCE ON POWER AND ENERGY (PECON 2020), 2020, :356-361
[44]   Local Path Planning of Mobile Robot Based on Long Short-Term Memory Neural Network [J].
Caihong Na Guo ;
Di Li ;
Yong Wang ;
Guoming Song ;
Tengteng Liu .
Automatic Control and Computer Sciences, 2021, 55 :53-65
[45]   Water level prediction of Lake Poyang based on long short-term memory neural network [J].
Guo Y. ;
Lai X. .
Hupo Kexue/Journal of Lake Sciences, 2020, 32 (03) :865-876
[46]   Enhancing wind power forecasting and ramp detection using long short-term memory networks and the swinging door algorithm [J].
Pandit, Ravi ;
Mu, Shikun ;
Astolfi, Davide .
IET RENEWABLE POWER GENERATION, 2025, 19 (01)
[47]   SHORT-TERM WIND POWER FORECASTING BASED ON VMD-MBWO-KELM [J].
Chang, Zhencheng ;
You, Guodong ;
Xiao, Ziyue ;
Lu, Yuran ;
Liu, Ruijun ;
Xi, Zhongqi .
Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2025, 46 (06) :623-631
[48]   Short-term probabilistic forecasting based on KPCA-KMPMR for wind power [J].
Li J. ;
Chang Y. .
Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2017, 37 (02) :22-28and36
[49]   Short-term wind power combined forecasting based on error forecast correction [J].
Liang, Zhengtang ;
Liang, Jun ;
Wang, Chengfu ;
Dong, Xiaoming ;
Miao, Xiaofeng .
ENERGY CONVERSION AND MANAGEMENT, 2016, 119 :215-226
[50]   Local Path Planning of Mobile Robot Based on Long Short-Term Memory Neural Network [J].
Guo, Na ;
Li, Caihong ;
Wang, Di ;
Song, Yong ;
Liu, Guoming ;
Gao, Tengteng .
AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2021, 55 (01) :53-65
12345