Wind Time Series Modeling for Power Turbine Forecasting

Nunnari, Silvia; Fortuna, Luigi; Gallo, Α.

doi:10.12720/ijoee.2.2.112-118

Cited by 3 publications

(7 citation statements)

References 8 publications

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“…Portanto, do correlograma, pode-se selecionar as amostras que apresentamíndices consideráveis de autocorrelação, para comporem as entradas atrasas que serão informadas ao modelo de previsão utilizado, como feito por Nunnari et al (2014).…”

Section: Função De Autocorrelaçãounclassified

“…Neste trabalho foi proposto uma metodologia para determinar as amostras anteriores que serão definidas como entradas do modelo neural, conforme ideia proposta por Nunnari et al (2014), comparando a RNA com a RNW. Estaúltima apresenta melhor desempenho em problemas de predição do que RNA, por unir as vantagens proporcionadas pela decomposição wavelet.…”

Section: Conclusãounclassified

“…Na predição de séries temporais com modelos estatísticoś e necessário selecionar as amostras atrasadas no tempo, usualmente determinadas através da análise do correlograma, para determinar as entradas do modelo (Nunnari et al, 2014). Neste trabalho propõe-se como modelo uma RNA, criada e treinada utilizando o neural network toolbox do MATLAB, e uma RNW implementada aqui, em MA-TLAB, a partir da teoria apresentada por Alexandridis e Zapranis (2014).…”

Section: Introductionunclassified

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Predição Da Velocidade Do Vento Utilizando Redes Neurais Wavelet

Magalhães

Chagas

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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In this work the wind speed is predicted for wind power application estimation. Data obtained from an anemometric station were used for the application of two artificial intelligence techniques, the artificial neural network and the wavelet neural network. The autocorrelation function was used to determine the amount of time-delayed inputs for the time series prediction, which allowed the use of data in order to increase the extrapolation capacity of the networks. The comparison of the predictions obtained from the methods was performed through the mean absolute percentage error, as well as the coefficient of determination, which proved a better generalization capacity and precision by the wavelet neural network. Resumo: Neste trabalhoé feita a predição da velocidade do vento para aplicação na estimação de potência gerada por aerogeradores. Foram utilizados dados obtidos de uma estação anemométrica para a aplicação de duas técnicas de inteligência artificial, a rede neural artificial e a rede neural wavelet. A função de autocorrelação foi empregada na determinação da quantidade de entradas atrasadas no tempo para a predição da série temporal, o que possibilitou a utilização dos dados de forma a aumentar a capacidade de extrapolação das redes. A comparação das predições obtidas a partir dos métodos foi realizada por meio do erro percentual absoluto médio, assim como do coeficiente de determinação, o que comprovou uma melhor capacidade de generalização e precisão por parte da rede neural wavelet.

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Section: Função De Autocorrelaçãounclassified

Section: Conclusãounclassified

Section: Introductionunclassified

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Predição Da Velocidade Do Vento Utilizando Redes Neurais Wavelet

Magalhães

Chagas

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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“…The use of such a clean energy resource can be an incentive for multiple countries to get a high level of energy independence. Additionally, the reduction of greenhouse gas emissions encourages many of them to use renewables and, in particular, wind [1] and solar (photovoltaic) [2] energy. This is especially relevant because of its undoubted ecological aspect.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the default values of the above arguments, the 'fitcurve()' function returns the fitted model of the Weibull CDF and logistic functions with the corresponding calculated parameters and the fitted WT power curve values. 2 3 4 5 6 7 8 11 12 13 14 15 16 17 18 19 20 21 22 23 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23#> [24] 24 25 #> attr(,"class") #> [1] "fitcurve"$ function, which eases the process of extracting discrete WT power curve points from a power curve image. The 'img2points()' function works on a similar principal of the 'digitize' package [28], but designed more specifically for WT power curves fitting with additional features.…”

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confidence: 99%

Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function

2018

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The representation of a wind turbine power curve by means of the cumulative distribution function of a Weibull distribution is investigated in this paper, after having observed the similarity between such a function and real WT power curves. The behavior of wind speed is generally accepted to be described by means of Weibull distributions, and this fact allows researchers to know the frequency of the different wind speeds. However, the proposal of this work consists of using these functions in a different way. The goal is to use Weibull functions for representing wind speed against wind power, and due to this, it must be clear that the interpretation is quite different. This way, the resulting functions cannot be considered as Weibull distributions, but only as Weibull functions used for the modeling of WT power curves. A comparison with simulations carried out by assuming logistic functions as power curves is presented. The reason for using logistic functions for this validation is that they are very good approximations, while the reasons for proposing the use of Weibull functions are that they are continuous, simpler than logistic functions and offer similar results. Additionally, an explanation about a software package has been discussed, which makes it easy to obtain Weibull functions for fitting WT power curves.

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Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

2018

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Wind power penetration ratios of power grids have increased in recent years; thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation.

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Wind Time Series Modeling for Power Turbine Forecasting

Cited by 3 publications

References 8 publications

Predição Da Velocidade Do Vento Utilizando Redes Neurais Wavelet

Predição Da Velocidade Do Vento Utilizando Redes Neurais Wavelet

Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function

Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

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