irenekarijadi/CEEMDAN-EWT-LSTM
Wind Power Forecasting Based on Hybrid CEEMDAN-EWT Deep Learning Method
This project helps energy system managers and grid operators predict wind power generation more accurately for the short term. It takes historical wind power data and produces highly reliable forecasts, helping to ensure grid stability and manage renewable energy supply. Power system engineers or anyone involved in grid management and renewable energy integration would find this useful.
100 stars. No commits in the last 6 months.
Use this if you need highly accurate, ultra-short-term wind power forecasts to improve grid reliability and optimize renewable energy management.
Not ideal if you require long-term wind power predictions or forecasts for other types of energy generation.
Stars
100
Forks
7
Language
Python
License
MIT
Category
Last pushed
Sep 28, 2023
Commits (30d)
0
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