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

• 作者: Dengxuan Li Rui Yin Weidong Chen Yifeng Wang
• 来源期刊: 全球能源互联网（英文）
• 年，卷（期）: 2020,（6）
• 所属期刊栏目: Clean energy
• 页码范围: 571-576
• 页数: 6页
• 语种: 英文
• DOI: 10.14171/j.2096-5117.gei.2020.06.007

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.