An Advanced Statistical Method for Wind Power Forecasting

Sideratos, G.; Hatziargyriou, Nikos D.

doi:10.1109/tpwrs.2006.889078

Cited by 529 publications

(256 citation statements)

References 12 publications

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“…Hybridization of ANN with other methods will produce very good forecasts [19][20][21]. In [20] an hybrid approach based on ANN and fuzzy logic technique is applied for wind power forecasts.…”

Section: Introductionmentioning

confidence: 99%

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Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

et al. 2014

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A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP). A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM) with Wavelet Decomposition (WD) were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN)-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

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Section: Introductionmentioning

confidence: 99%

“…In [20] an hybrid approach based on ANN and fuzzy logic technique is applied for wind power forecasts. In [21], an enhanced hybrid forecasting method that combines the persistence method, the back propagation neural network, and the radial basis function (RBF) neural network, was applied for short-term wind power prediction.…”

Section: Introductionmentioning

confidence: 99%

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

et al. 2014

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“…Sideratos et al [61] and Fan et al [59] reported the importance of dividing the dataset into several subsets and fitting a model to each subset.…”

Section: Short-term Wind Power Forecasting Using Nwpmentioning

confidence: 99%

A quick guide to wind power forecating : state-of-the-art 2009.

Monteiro¹,

Keko²,

Bessa³

et al. 2009

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“…In view of this, the electrical power system in China has clearly claimed to bring the active output control of the wind farm into the power network dispatching management [3]. Currently, the electric power system dispatching is mainly based on the short-term wind speed and wind power prediction [4][5][6], which fails to consider the impact of wind turbine (WT) outages on the wind power variation. Meanwhile, there is still a lack of warning information of the WT outages to support the internal coordination control technologies of the wind farm [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data

Sun

Chen

et al. 2016

Energies

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This paper presents a short-term wind turbine (WT) outage model based on the data collected from a wind farm supervisory control and data acquisition (SCADA) system. Neural networks (NNs) are used to establish prediction models of the WT condition parameters that are dependent on environmental conditions such as ambient temperature and wind speed. The prediction error distributions are discussed and used to calculate probabilities of the operation of protection relays (POPRs) that were caused by the threshold exceedance of the environmentally sensitive parameters. The POPRs for other condition parameters are based on the setting time of the operation of protection relays. The union probability method is used to integrate the probabilities of operation of each protection relay to predict the WT short term outage probability. The proposed method has been used for real 1.5 MW WTs with doubly fed induction generators (DFIGs). The results show that the proposed method is more effective in WT outage probability prediction than traditional methods.

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An Advanced Statistical Method for Wind Power Forecasting

Cited by 529 publications

References 12 publications

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

A quick guide to wind power forecating : state-of-the-art 2009.

A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data

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