Radial Basis Function Networks

Du, Ke-Lin; Swamy, M. N. S.

doi:10.1007/978-1-4471-7452-3_11

Cited by 1 publication

(2 citation statements)

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“…One of the learning algorithm approaches is just applying a BP algorithm as it is done in MLP. The most widely known algorithm is the stochastic gradient descent (RBFN‐SGD) 21–23 . The difference in relation to ANNs lies in the fact that the RBFN has different parameters to adapt throughout the learning process.…”

Section: Methodsmentioning

confidence: 99%

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Comparative performance of AI methods for wind power forecast in Portugal

Godinho¹,

Castro

2020

Wind Energy

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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.

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Section: Methodsmentioning

confidence: 99%

“…In RBFN‐SGD, all parameters are adapted simultaneously, whereas another approach is using a two‐stage strategy 21,23,24 . In the first stage, unsupervised techniques, such as clustering algorithms (e.g., k‐means), are performed with the purpose of computing the centers and, later, the widths.…”

Section: Methodsmentioning

confidence: 99%

Comparative performance of AI methods for wind power forecast in Portugal

Godinho¹,

Castro

2020

Wind Energy

View full text Add to dashboard Cite

show abstract

Radial Basis Function Networks

Cited by 1 publication

References 100 publications

Comparative performance of AI methods for wind power forecast in Portugal

Comparative performance of AI methods for wind power forecast in Portugal

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