One day ahead prediction of wind speed class

Fortuna, Luigi; Nunnari, Silvia; Guariso, Giorgio

doi:10.1109/icrera.2015.7418553

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…ANFIS results in better forecasts. In (Fortuna et al [19], 2016), the clustering tool is used to form wind speed classes. Then, two models, namely the Hidden Markov Model and the Nonlinear Autoregressive are compared for predicting the class of each new wind speed data entry.…”

Section: Motivation and State-of-the-artmentioning

confidence: 99%

A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence

2019

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Wind speed forecasting is an important element for the further development of offshore wind turbines. Due to its importance, many researchers have proposed different models for wind speed forecasting that differ in terms of the time-horizon of the forecast, types and number of inputs, complexity, structure, and others. Wind speed series present high nonlinearity and volatilities, and thus an effective model should successfully deal with those features. An approach to deal with the nonlinearities and volatilities is to utilize a time series processing technique such as the wavelet transform. In the present paper, an ensemble data-driven short-term wind speed forecasting model is developed, tested and applied. The term “ensemble” refers to the combination of two different predictors that run in parallel and the prediction is obtained by the predictor that leads to the lowest error. The proposed model utilizes the wavelet transform and is compared with other models that have been presented in the related literature and outperforms their accuracy. The proposed forecasting model can be used effectively for 1 min and 10 min ahead horizon wind speed predictions.

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Section: Motivation and State-of-the-artmentioning

confidence: 99%

A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence

2019

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“…However, as done in this paper, usually a pattern is assigned to the class with the highest degree of membership. As regards the choice of the number of classes, a parameter required to run the fcm algorithm, a classification into 3 classes was considered, based on results described in [21].…”

Section: Wind Speed Time Series Clusteringmentioning

confidence: 99%

“…Como is a town located at about 200 m a.s.l., on the North rim of the Po Valley, close to pre-Alps. The general structureof the approach described in this paper were presented in [21].…”

Section: Introductionmentioning

confidence: 99%

One Day Ahead Prediction of Wind Speed Class by Statistical Models

Nunnari¹,

Fortuna

Guariso³

2016

IJRER

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This paper deals with the clustering of daily wind speed time series based on two features, namelythe daily average wind speed and the corresponding degree of fluctuation. Daily values of the feature pairs are first classified by means of the fuzzy c-means unsupervised clustering algorithm and then results are used to train a supervised MLP neural network classifier.It is shown that associating to a true wind speed time series a time series of classes allows performing some useful statistics. Further, the problem of predicting the class of daily wind speed 1-step ahead is addressed by using both theHidden Markov Models (HMM) and theNon-linear Auto-Regressive (NAR) approaches. The performances of the considered class prediction models are finally assessed in terms of True Positive rate (TPR) and True Negative rate (TNR), also in comparison with the persistent model.

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“…Autoregressive models, such as autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) are commonly utilized for short-term wind speed prediction [9,10]. In recent years, some new and improved statistical models were proposed for wind prediction [11][12][13]. Kavasseri and Seetharaman [14] applied a fractional-ARIMA model in wind speed prediction of one-and two-day-ahead horizons in four potential wind generation sites located in North Dakota, United States of America (USA).…”

Section: Introductionmentioning

confidence: 99%

Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

et al. 2019

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Accurate predictions of wind speed and wind energy are essential in renewable energy planning and management. This study was carried out to test the accuracy of two different neuro fuzzy techniques (neuro fuzzy system with grid partition (NF-GP) and neuro fuzzy system with substractive clustering (NF-SC)), and two heuristic regression methods (least square support vector regression (LSSVR) and M5 regression tree (M5RT)) in the prediction of hourly wind speed and wind power using a cross-validation method. Fourfold cross-validation was employed by dividing the data into four equal subsets. LSSVR’s performance was superior to that of the M5RT, NF-SC, and NF-GP models for all datasets in wind speed prediction. The overall average root-mean-square errors (RMSE) of the M5RT, NF-GP, and NF-SC models decreased by 11.71%, 1.68%, and 2.94%, respectively, using the LSSVR model. The applicability of the four different models was also investigated in the prediction of one-hour-ahead wind power. The results showed that NF-GP’s performance was superior to that of LSSVR, NF-SC, and M5RT. The overall average RMSEs of LSSVR, NF-SC, and M5RT decreased by 5.52%, 1.30%, and 15.6%, respectively, using NF-GP.

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One day ahead prediction of wind speed class

Cited by 5 publications

References 11 publications

A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence

A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence

One Day Ahead Prediction of Wind Speed Class by Statistical Models

Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

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