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
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