Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods

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

doi:10.1016/j.energy.2011.05.006

Cited by 139 publications

(72 citation statements)

References 32 publications

(33 reference statements)

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“…The collected time data included the values of produced power, wind speed, temperature and pressure; the data was collected for a period of 5 years with a recorded measurement every 10 min [6], although the present wind forecasting models only consider the power produced in 1 year and the average value for the three turbines was calculated for the input vector. To verify the opportunity to use the averaged value, the correlation between the three turbines was analyzed by the estimation of the Pearson's coefficient, calculated as the ratio between the covariance of two variables and the product of their standard deviations.…”

Section: Wind Farm Characteristics and Available Time Datamentioning

confidence: 99%

“…Generally, statistical techniques give good results for short time predictions, while meteorological models are more suitable for long-term forecasts, as reported in [6]. 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.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [6,23] showed that hybrid methods based on the wavelet decomposition technique and Elman ANN are characterized by narrow error distributions, in particular for short time horizons. In [25], a hybrid approach based on the combination of WD, ANN and evolutionary algorithm, was successfully proposed for hourly wind power forecasting.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Wind Farm Characteristics and Available Time Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…The literature cites numerous investigations into the use of Neural Networks for wind speed prediction (More and Deo 2003, Reikard 2008, Wu et al 2009, Hong et al 2010, Anvari Moghaddam and Seifi 2011, De Giorgi et al 2011, Shi et al 2011 Hatziargyriou 2012) but the majority of the work done to date uses historical wind speed data as the only (meteorological) parameter to train the networks. Very few exceptions exist to this with some adding other parameters such as ambient temperature and humidity when predicting wind speed (Cali et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Influence of neural network training parameters on short-term wind forecasting

Brka

Al-Abdeli

Kothapalli

2014

International Journal of Sustainable Energy

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This paper investigates factors which can affect the accuracy of short-term wind speed prediction when done over long periods spanning different seasons. Two types of Neural Networks are used to forecast power generated via specific horizontal axis wind turbines.Meteorological data used is for a specific Western Australian location.Results reveal that seasonal variations affect the prediction accuracy of the wind resource, but the magnitude of this influence strongly depends on the details of the Neural Network deployed. Factors investigated include the span of the time series needed to initially train the networks, the temporal resolution of this data, the length of training pattern within the overall span which are used to implement the predictions and whether the inclusion of solar irradiance data can appreciably affect wind speed prediction accuracy. There appears to be a relatively complex relationship between these factors and the accuracy of wind speed prediction via Neural Networks. Predicting wind speed based on Neural Networks trained using wind speed and solar irradiance data also increases the prediction accuracy of wind power generated, as can the type of network selected.

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“…On the other hand, predictions of other meteorological variables besides the wind speed are often added in models. In some works, researchers have proven that the combination of wind direction and wind speed is effective in reducing the error of prediction [18,19], temperature and pressure can also improve the performance of statistical models [20,21], and the spatial interdependency of different variables has been proven to be effective by some studies [22]. However, in many circumstances, especially under complicated weather conditions, these parameters still cannot offer enough information for bias estimation and sometimes even worsen the forecast results, which implies that some additional or more relevant parameters are needed to provide more complete information.…”

Section: Introductionmentioning

confidence: 99%

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Chu

Wang

et al. 2016

Energies

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Abstract:Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28-52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.

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Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods

Cited by 139 publications

References 32 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)

Influence of neural network training parameters on short-term wind forecasting

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

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