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- Title
Comparative performance of AI methods for wind power forecast in Portugal.
- Authors
Godinho, Miguel; Castro, Rui
- Abstract
Because wind has a high volatility and the respective energy produced cannot be stored on a large scale because of excessive costs, it is of utmost importance to be able to forecast wind power generation with the highest accuracy possible. The aim of this paper is to compare 1‐h‐ahead wind power forecasts performance using artificial intelligence‐based methods, such as artificial neural networks (ANNs), adaptive neural fuzzy inference system (ANFIS), and radial basis function network (RBFN). The latter was implemented using three different learning algorithms: stochastic gradient descent (SGD), hybrid, and orthogonal least squares (OLS). The application dataset is the injected wind power in the Portuguese power systems throughout the years 2010–2014. The network architecture optimization and the learning algorithms are presented. An initial data analysis showed data seasonality; therefore, the wind power forecasts were performed according to the seasons of the year. The results showed that ANFIS was the best performer method, and ANN and RBFN‐OLS also showed strong performances. RBFN‐Hybrid and RBFN‐SGD performed poorly. In general, all methods outperformed persistence.
- Subjects
PORTUGAL; WIND power; WIND forecasting; ARTIFICIAL neural networks; RADIAL basis functions; MACHINE learning
- Publication
Wind Energy, 2021, Vol 24, Issue 1, p39
- ISSN
1095-4244
- Publication type
Article
- DOI
10.1002/we.2556