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A Three-Layer Hybrid Model for Wind Power Prediction
- Source :
- 2020 IEEE Power & Energy Society General Meeting (PESGM).
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Accurate wind power prediction (WPP) is important for stable operation of power systems. However, the intermittent nature and high variability of wind causes many challenges. This paper proposes a three-layer WPP model considering the data from historical power measurements and numerical weather prediction (NWP) systems. The first layer uses a linear model to learn the wind power generation equation. The second layer includes several non-linear models to learn the seasonality and the inertia of wind turbines. The third layer uses stacked regression to learn a hybrid combination of predictors in the previous layer. We compared the proposed approach against the state-of-the-art algorithm as well as two neural network models. Experiment results show that our approach has the best performance.
- Subjects :
- Wind power
Artificial neural network
business.industry
Computer science
020209 energy
05 social sciences
02 engineering and technology
Numerical weather prediction
Power (physics)
Renewable energy
Electric power system
Control theory
0502 economics and business
0202 electrical engineering, electronic engineering, information engineering
Layer (object-oriented design)
business
Physics::Atmospheric and Oceanic Physics
050205 econometrics
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 IEEE Power & Energy Society General Meeting (PESGM)
- Accession number :
- edsair.doi...........e5e32665445b77534e1a7e283a535416