Day-ahead wind speed forecasting using f-ARIMA models

Kavasseri, Rajesh G.; Seetharaman, K.

doi:10.1016/j.renene.2008.09.006

Cited by 773 publications

(283 citation statements)

References 12 publications

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“…Models which use statistical methods for wind speed predictions are also popular in the literature. They include moving average models such as ARMA, ARIMA and its variants fitted to the time series of wind speed (Kamal and Jafri, 1997;Cadenas and Rivera, 2007;Kavasseri and Seetharaman, 2009) and models based on probability distribution of wind speed (Hennessey Jr., 1977;Celik, 2004;Mathew et al, 2011;Jiang et al, 2013). These models are fairly good in very short-term predictions, but do not improve significantly on prediction error compared to the elementary method of persistence.…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

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Deterministic prediction of surface wind speed variations

Drisya

Kiplangat

Asokan³

et al. 2014

Ann. Geophys.

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Abstract. Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error) of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods.Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.

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Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

“…Among the various statistical methods used in wind speed prediction ARIMA is a popular model which gives reasonably accurate predictions of wind speed at many locations (Kamal and Jafri, 1997;Cadenas and Rivera, 2007;Kavasseri and Seetharaman, 2009). An ARIMA(p,d,q) model com-…”

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confidence: 99%

Deterministic prediction of surface wind speed variations

Drisya

Kiplangat

Asokan³

et al. 2014

Ann. Geophys.

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“…The physical models use numerical weather prediction (NWP) to predict wind speed and then input the data into wind power output models to obtain the output power [4]. The common statistical forecast methods include the time series method [5,6], artificial neural network (ANN) method [7,8], and support vector machine (SVM) [9]. The main focus of these methods is to reduce the point forecast errors of wind power by introducing new models.…”

Section: Introductionmentioning

confidence: 99%

A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization

et al. 2017

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Intermittency and uncertainty pose great challenges to the large-scale integration of wind power, so research on the probabilistic interval forecasting of wind power is becoming more and more important for power system planning and operation. In this paper, a Naive Bayesian wind power prediction interval model, combining rough set (RS) theory and particle swarm optimization (PSO), is proposed to further improve wind power prediction performance. First, in the designed prediction interval model, the input variables are identified based on attribute significance using rough set theory. Next, the Naive Bayesian Classifier (NBC) is established to obtain the prediction power class. Finally, the upper and lower output weights of NBC are optimized segmentally by PSO, and are used to calculate the upper and lower bounds of the optimal prediction intervals. The superiority of the proposed approach is demonstrated by comparison with a Naive Bayesian model with fixed output weight, and a rough set-Naive Bayesian model with fixed output weight. It is shown that the proposed rough set-Naive Bayesian-particle swarm optimization method has higher coverage of the probabilistic prediction intervals and a narrower average bandwidth under different confidence levels.

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“…Alguns consistindo de análises espaciais e temporais da velocidade do vento, realizadas utilizando várias abordagens metodológicas (Kavasseri & Seetharaman, 2009;Shi et al, 2012;Jeong et al, 2014), porém, nenhum realizado em regiões semiáridas.…”

Section: Introductionunclassified

Potencial de geração de energia eólica no Submédio São Francisco

Lopes

Júnior

Melo

et al. 2017

J. Env. Anal. Progr.

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A B S T R A C TThe major potential of wind power generation in the world and a possibility of electric energy production make it an alternative source to increase the diversity in the world energy matrix. The objective was to characterize a predominant velocity and direction of the wind for generation of wind power in the Submédio São ) of the São Francisco Submiddle. The highest values of wind power are highlighted for the municipalities of Dormentes, Lagoa Grande, Curaçá, Petrolina, Afrânio, Pedro Alexandre, part of the municipality of Santa Filomena, Santa Maria da Boa Vista, Chorrochó, Macururé, Paulo Afonso, and Juazeiro. The municipality of Curaçá, in the State of Bahia, show the most favorable installation of wind generators, instead of larger values and constant wind transmission. Consequently, it has the highest values of wind energy and a dominant frequency in the southeast direction of the winds throughout the day, being able to fix the towers in this geographical orientation. A large part of the Submédio São Francisco is suitable for the installation of wind energy generation centers.

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Day-ahead wind speed forecasting using f-ARIMA models

Cited by 773 publications

References 12 publications

Deterministic prediction of surface wind speed variations

Deterministic prediction of surface wind speed variations

A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization

Potencial de geração de energia eólica no Submédio São Francisco

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