A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation

Barbounis, T. G.; Theocharis, John B.

doi:10.1016/j.neucom.2006.01.032

Cited by 178 publications

(65 citation statements)

References 26 publications

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“…Compared with other intelligent methods, it has the advantages of having a simple structure, fast calculation speeds, a high forecasting accuracy, and fewer training sample requirements [15][16][17][18]. According to the wind speed forcasting process, the ELM for regression is constructed in this paper using multiple inputs and a single output.…”

Section: Weighted Regularization Extreme Learning Machine (Wrelm)mentioning

confidence: 99%

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

et al. 2016

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Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD), the partial autocorrelation function (PACF), and weighted regularized extreme learning machine (WRELM), is proposed to improve the accuracy of wind speed forecasting. First, the historic wind speed time series is decomposed into several intrinsic mode functions (IMFs). Second, the partial correlation of each IMF sequence is analyzed using PACF to select the optimal subfeature set for particular predictors of each IMF. Then, the predictors of each IMF are constructed in order to enhance its strength using WRELM. Finally, wind speed is obtained by adding up all the predictors. The experiment, using real wind speed data, verified the effectiveness and advancement of the new approach.

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Section: Weighted Regularization Extreme Learning Machine (Wrelm)mentioning

confidence: 99%

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

et al. 2016

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“…Furthermore, several other spatial correlation techniques are proposed for short term wind power forecasting with the goal of achieving higher prediction accuracy [11]. However, by the passage of time, more advanced methods have been proposed.…”

Section: International Journal Of Computing and Digital Systemsmentioning

confidence: 99%

Short-Term and Very Short-Term Wind Power Forecasting Using a Hybrid ICA-NN Method

Ghadi¹,

Gilani²,

Afrakhte³

et al. 2014

IJCDS

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“…The majority of previous approaches use modern regression techniques, many of them based on soft-computing algorithms such as neural networks: multi-layer perceptrons [5e11], fuzzy-based neural approaches [12], two-hidden layer neural networks [13], fast training neural approaches [3], Support Vector Machines [14], Abductive networks [15], Bayessian networks [16], probabilistic methods [17] or generalized mapping regression [18]. In spite of this huge work on modern methods for wind speed prediction from measuring towers, there are still some margin for improvement, coming from methodologies that have been under-explored in this problem.…”

Section: Introductionmentioning

confidence: 99%

Local models-based regression trees for very short-term wind speed prediction

et al. 2015

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a b s t r a c tThis paper evaluates the performance of different types of Regression Trees (RTs) in a real problem of very short-term wind speed prediction from measuring data in wind farms. RT is a solidly established methodology that, contrary to other soft-computing approaches, has been under-explored in problems of wind speed prediction in wind farms. In this paper we comparatively evaluate eight different types of RTs algorithms, and we show that they are able obtain excellent results in real problems of very short-term wind speed prediction, improving existing classical and soft-computing approaches such as multi-linear regression approaches, different types of neural networks and support vector regression algorithms in this problem. We also show that RTs have a very small computation time, that allows the retraining of the algorithms whenever new wind speed data are collected from the measuring towers.

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A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation

Cited by 178 publications

References 26 publications

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

Short-Term and Very Short-Term Wind Power Forecasting Using a Hybrid ICA-NN Method

Local models-based regression trees for very short-term wind speed prediction

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