Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network
In one sentence. An LSTM network maps climate-model meteorology to monthly wind power utilisation hours, giving medium- to long-term generation forecasts for power planning; the case study is limited.
Abstract
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.
Labels
- Topic
- Applications & impacts
- Type
- Application study
- Method
- RNN / LSTM / ConvLSTM
- Time scale
- Seasonal to annual
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1016/j.gloei.2021.01.003 OpenAlex:W3126276509
- Added
- 2026-10-22
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