Wind speed forecasting: Present status

Bhaskar, M. K.; Jain, Amit; Srinath, N. Venkata

doi:10.1109/powercon.2010.5666623

Cited by 31 publications

(25 citation statements)

References 26 publications

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“…Although certain methodologies are developed like Statistical Methods, Fuzzy System Based Models, Artificial Intelligence Techniques, Evolutionary Algorithms Based Techniques etc. for forecasting [5] but an accurate forecasting is important since wind power is directly proportional to cube of wind speed, hence any error in wind speed will lead to cube of that error in wind power. The following relation gives the output power of variable-speed wind turbine: Where υ stands for wind speed (m/s), R is the radius of the rotor (m), ρ is the air density (kg/m 3 ), and C P stands for maximum value of rotor efficiency for each steady wind speed [6].…”

Section: Introductionmentioning

confidence: 99%

“…The following relation gives the output power of variable-speed wind turbine: Where υ stands for wind speed (m/s), R is the radius of the rotor (m), ρ is the air density (kg/m 3 ), and C P stands for maximum value of rotor efficiency for each steady wind speed [6]. Here in this study we are utilizing Artificial Neural Network (ANN) of Artificial Intelligence Techniques because unlike statistical methods, ANN models are simpler to construct and require shorter development time and these don't require to explicitly defining mathematical expressions [5]. A large set of wind speed data measured at 1 hour intervals is utilized as input in algorithm development.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative study on Different ANN Techniques in Wind Speed Forecasting for Generation of Electricity

Parmar¹,

Shah²,

Shah³

2017

IOSR JEEE

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative study on Different ANN Techniques in Wind Speed Forecasting for Generation of Electricity

Parmar¹,

Shah²,

Shah³

2017

IOSR JEEE

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“…Although the prediction accuracy of wind power forecasting is lower than that of load forecasting. Wind power forecasts still play a key role in addressing the operation challenges of a utility grid [9].…”

Section: Introductionmentioning

confidence: 99%

“…Basically, the statistical method is good for short-term prediction. The disadvantage with this method is the prediction error increases as the prediction time increases [9].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

Chang

2013

Energies

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High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF) neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS) installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

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“…No setor eólico, uma estratégia bastante atual tem sido à busca por métodos de previsão de velocidade de vento que possam fornecer garantias aos investidores do setor eólico, de modo que essa busca possa alavancar cada vez mais pesquisas no campo de estudo da energia eólica. Na literatura é possível identificar alguns trabalhos que mostram a viabilidade de se fazer previsão de variáveis meteorológicas, como em (DORVLO, 2002), (BHASKAR, 2010), e (DÍAZ, 2012).…”

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Previsão de Velocidade Média do Vento através da utilização de modelagem Auto-Regressiva de Médias Móveis (ARMA) em Região Serrana no Estado do Ceará - Brasil

Camelo¹,

Teixeira

Lúcio³

et al. 2015

R. bras. Ener. Renov.

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Nesse trabalho é realizado uma previsão de velocidade média do vento para a cidade de Ubajara região serrana do Estado do Ceará – Brasil, através de modelagem Auto-Regressiva de Médias Móveis – ARMA. A busca por métodos de previsão de velocidade do vento poderá garantir ainda mais investimentos em geração eólica tendo em vista que isso poderar ser um fator adicional na implantação de novos parques eólicos em diversas regiões do país. Nos últimos anos o Estado do Ceará é uma das regiões do território brasileiro que mais crescem em potencia eólica instalada, exemplo disso é que em 1999 possuia um valor de 5 MW, já no ano de 2014 possui um valor de 1,2 GW. A eficácia da previsão é afirmada mediante a análise de erros realizada comparando-se as séries real e prevista. A média do erro absoluto foi de aproximadamente 0,10 m/s, o erro quadrático médio foi de 0,37 m/s, e a média do erro absoluto percentual foi de 4,82 %.

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Wind speed forecasting: Present status

Cited by 31 publications

References 26 publications

A Comparative study on Different ANN Techniques in Wind Speed Forecasting for Generation of Electricity

A Comparative study on Different ANN Techniques in Wind Speed Forecasting for Generation of Electricity

Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

Previsão de Velocidade Média do Vento através da utilização de modelagem Auto-Regressiva de Médias Móveis (ARMA) em Região Serrana no Estado do Ceará - Brasil

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