A stacking-based short-term wind power forecasting method by CBLSTM and ensemble learning
importance 5/10
ai assisted
Energy Load and Power Forecasting wind energy forecasting stage: research
Problem
Traditional short-term wind power forecasting struggles with large, highly volatile time-series data and limited processing capacity.
Why it matters
Accurate short-term wind power forecasts matter for grid stability and renewable energy economics.
What the AI did
A stacking ensemble embedding CBLSTM, XGBoost and other tree models forecast wind power after DBSCAN cleaning of turbine data.
Results
not stated in source
Impact
Feasibility demonstrated on actual data from two wind farms; broader adoption not evidenced.
Record
| Venue | Journal of Renewable and Sustainable Energy |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Nier Wang, Zhanming Li |
| Citations | 13 (OpenAlex, at import time) |
| References | 22 |
| Model or method | CBLSTM, XGBoost, stacking ensemble |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1063/5.0097757 |
| Open access | closed |
| Keywords | wind power forecasting, stacking ensemble, CBLSTM, XGBoost, DBSCAN |
Importance rating
5
real but narrower advance
Wind power forecasting supports grid dispatch and renewable consumption, a real but method-level advance; validated on two wind farms' data, no broader scale given.
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