A new strategy for wind speed forecasting using artificial intelligent methods

Monfared, Mohammad; Rastegar, Hasan; Kojabadi, Hossein Madadi

doi:10.1016/j.renene.2008.04.017

Cited by 217 publications

(103 citation statements)

References 4 publications

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“…A good number of such tools rely on statistical methods, either moving average models such as ARMA and ARIMA fitted to the time series of wind speed (Kamal and Jafri, 1997;Torresa et al, 2005;Cadenas and Rivera, 2007;Kavasseri and Seetharaman, 2009) or models based on probability distribution of wind speed (Hennessey, 1977;Celik, 2004;Mathew et al, 2011). Models based on artificial neural networks have also been developed by many authors for making short-term predictions of wind speed and generated wind power (Mohandes et al, 1998;Cadenas and Rivera, 2007;Bilgili et al, 2007;Monfared et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Deterministic nature of the underlying dynamics of surface wind fluctuations

Sreelekshmi

Asokan²,

Kumar

2012

Ann. Geophys.

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Abstract. Modelling the fluctuations of the Earth's surface wind has a significant role in understanding the dynamics of atmosphere besides its impact on various fields ranging from agriculture to structural engineering. Most of the studies on the modelling and prediction of wind speed and power reported in the literature are based on statistical methods or the probabilistic distribution of the wind speed data. In this paper we investigate the suitability of a deterministic model to represent the wind speed fluctuations by employing tools of nonlinear dynamics. We have carried out a detailed nonlinear time series analysis of the daily mean wind speed data measured at Thiruvananthapuram (8.483 • N,76.950 • E) from 2000 to 2010. The results of the analysis strongly suggest that the underlying dynamics is deterministic, low-dimensional and chaotic suggesting the possibility of accurate short-term prediction. As most of the chaotic systems are confined to laboratories, this is another example of a naturally occurring time series showing chaotic behaviour.

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Section: Introductionmentioning

confidence: 99%

Deterministic nature of the underlying dynamics of surface wind fluctuations

Sreelekshmi

Asokan²,

Kumar

2012

Ann. Geophys.

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“…By adding white noise to the signal, the ensemble empirical mode decomposition (EEMD) [27] can solve the problem of the boundary effect of traditional empirical mode decomposition to a certain extent, and the real signal can be preserved to the maximal extent. However EEMD also has some of the following problems [28,29]: if the amplitude of white noise added to EEMD is too low it cannot restrain the mixed mode stack well; if it is too large it would increase the average total amount of calculation, easily cause the decomposition of the high-frequency components, and make the white noise residual too large.…”

Section: Modified Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

Xie

Liang

Zhong

et al. 2017

IJGI

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Abstract:Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD), approximate entropy and the weighted least square support vector machine (WLS-SVM) was proposed. The method mainly started from the chaotic sequence of time-frequency analysis and improved the model performance as follows: first a deformation time series was decomposed into a series of subsequences with significantly different complexity using MEEMD. Then the approximate entropy method was used to generate a new subsequence for the combination of subsequences with similar complexity, which could effectively concentrate the component feature information and reduce the computational scale. Finally the WLS-SVM prediction model was established for each new subsequence. At the same time, phase space reconstruction theory and the grid search method were used to select the input dimension and the optimal parameters of the model, and then the superposition of each predicted value was the final forecasting result. Taking the landslide deformation data of Danba as an example, the experiments were carried out and compared with wavelet neural network, support vector machine, least square support vector machine and various combination schemes. The experimental results show that the algorithm has high prediction accuracy. It can ensure a better prediction effect even in landslide deformation periods of rapid fluctuation, and it can also better control the residual value and effectively reduce the error interval.

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“…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. Models based on artificial neural net-100 works, which emulate the parallel distributed processing of human nervous system to adapt by learning from past data, have also been developed by many researchers for making short term predictions of wind speed and power (Mohandes et al, 1998;Cadenas and Rivera, 2007;Bilgili et al, 2007; 105 Monfared et al, 2009). In general, these models outperform the time series models in short term predictions, but their performance edge is not maintained across all locations universally (Soman et al, 2010).…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

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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A new strategy for wind speed forecasting using artificial intelligent methods

Cited by 217 publications

References 4 publications

Deterministic nature of the underlying dynamics of surface wind fluctuations

Deterministic nature of the underlying dynamics of surface wind fluctuations

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

Deterministic prediction of surface wind speed variations

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