Abstract
The novel adaptive hybrid models are proposed for time series forecasting and features classification problems. The proposed forecasting model combines the Hilbert–Huang transform and random forests. The efficiency of proposed adaptive approaches is demonstrated on two cases studies: wind power ramps prediction and detection of alarm states in a power systems.
| Original language | English |
|---|---|
| Pages (from-to) | 211-228 |
| Number of pages | 18 |
| Journal | International Journal of Artificial Intelligence |
| Volume | 13 |
| Issue number | 1 |
| State | Published - 2015 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Alarm states detection
- Classification
- Energy efficiency
- Forecast parameters
- Hilbert-Huang transform
- Power systems
- Random forest
- Regression
- SVM
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