Error analysis of short term wind power prediction models

Giorgi, Maria Grazia De; Ficarella, Antonio; Tarantino, Marco

doi:10.1016/j.apenergy.2010.10.035

Cited by 155 publications

(76 citation statements)

References 21 publications

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“…This is a feed-forward network with a feedback connection from the first-layer output to the first layer input, thus enabling the detection and generation of time-varying patterns [7]. This characteristic is of great importance as the time-length of the prediction increases.…”

Section: The Artificial Neural Network Methodsmentioning

confidence: 99%

“…The authors of [7] compared Autoregressive-moving-average model (ARMA) models, which perform linear mapping between inputs and outputs, with Artificial Neural Network (ANN) models and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform non-linear mapping. The results underline that high accuracy for long time horizon in the wind power forecasting is given by non-linear models as the ANN, as also shown in [8][9][10][11][12][13][14][15][16][17].…”

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: The Artificial Neural Network Methodsmentioning

confidence: 99%

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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“…From many published results, the range of MAPE for wind power short-term forecasting is in the range of 10-30% [78]. Therefore, we can say that the uncertainty in wind power prediction is about one order of magnitude larger than the uncertainty in load prediction.…”

Section: Alarm Generatormentioning

confidence: 95%

Development and testing of improved statistical wind power forecasting methods.

Mendes¹,

Bessa²,

Keko³

et al. 2011

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“…Then the weighted sum is operated upon by an activation function (usually nonlinear), and output data are conveyed to other neurons. The weights are continuously altered while training to improve accuracy and generalize abilities [ 9] [10].…”

Section: Methodsmentioning

confidence: 99%