scholarly journals Previsão de velocidade do vento em termos de médias mensais e horárias a partir da combinação dos modelos Holt-Winters e Redes Neurais Artificiais (Forecast wind speed in terms of monthly and hourly averages from the combination of the Holt-Winters...)

2017 ◽  
Vol 10 (5) ◽  
pp. 1391
Author(s):  
Henrique Do Nascimento Camelo ◽  
Paulo Sérgio Lucio ◽  
João Verçosa Leal Junior
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Wind Speed ◽  
Hybrid Model ◽  
Northeastern Brazil ◽  
Percentage Error ◽  
Wind Speed Prediction ◽  
Wind Potential ◽  
Speed Prediction ◽  
Brazilian Northeast

O presente artigo propõe a criação de modelo híbrido combinado a partir de modelos de série temporal com inteligência artificial, com o objetivo de fornecer previsões de médias mensais e horárias da velocidade do vento em regiões do nordeste brasileiro. Métodos para previsão de velocidade do vento podem constituir em técnica útil no setor de geração eólica, por exemplo, sendo capaz de adquirir informações importantes de que maneira o potencial eólico local poderá ser aproveitado para possível geração de energia elétrica. É possível constatar a eficiência do modelo híbrido em fornecer perfeitos ajustes aos dados observados, sendo essa afirmativa baseada nos baixos valores encontrados na análise estatística de erros, por exemplo, com erro percentual de aproximadamente 5,0%, e também com o valor do coeficiente de eficiência de Nash-Sutcliffe, cujo valor encontrado em aproximadamente de 0,96. Esses resultados certamente foram importantes nas precisões das séries temporais previstas da velocidade do vento, fazendo com que pudessem acompanhar o perfil das séries temporais observadas da velocidade do vento, principalmente revelando maiores semelhanças de valores máximos e mínimos entre ambas as séries, e mostrando assim, a capacidade do modelo em representar características de sazonalidades local. A importância de fornecer garantias na estimativa da intensidade da velocidade do vento de uma região poderá ser tarefa de auxílio em tomada de decisão no setor de energia.  A B S T R A C TThe present article proposes the creation of hybrid model combined from time series models with artificial intelligence, aiming to provide forecasts of monthly and hourly wind speed averages in regions of northeastern Brazil. Wind speed prediction methods may be a useful technique in the wind power sector, for example, being able to acquire important information about how local wind potential can be harnessed for possible electric power generation. It is possible to verify the efficiency of the hybrid model in providing perfect adjustments to the data observed, being this affirmation based on the low values found in the statistical analysis of errors, for example, with percentage error of approximately 5.0%, and also with the coefficient value Of Nash-Sutcliffe efficiency, whose value was found to be approximately 0.96. These results were certainly important in the precisions of the predicted time series of the wind velocity, so that they could follow the profile of the observed time series of the wind speed, mainly revealing greater similarities of maximum and minimum values between both series, the ability of the model to represent characteristics of local seasonalities. The importance of providing assurances in the estimation of the wind speed intensity of a region may be a decision aid in the energy sector.Keywords: time series, artificial intelligence, wind energy, brazilian northeast. 

scholarly journals Modelo híbrido de previsão de séries temporais para possíveis aplicações no setor de geração eólica

2018 ◽  
Vol 40 ◽  
pp. 01
Author(s):  
João Bosco Verçosa Leal Junior ◽  
Henrique Do Nascimento Camelo ◽  
Paulo Sérgio Lucio ◽  
Paulo César Marques de Carvalho
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Prediction ◽  
Northeastern Brazil ◽  
Percentage Error ◽  
Wind Speed Prediction ◽  
Proposed Model ◽  
Wind Potential ◽  
Speed Prediction ◽  
Short And Long Term

In this paper an innovative hybrid model of time series prediction based on the combination of two functions (linear and nonlinear) of the Holt-Winters and Artificial Neural Networks models is presented. This model is applied in wind speed in northeastern Brazil, and was able to perform short and long term forecasts with good accuracy. We highlight the efficiency of the proposed model in providing perfect adjustments to the data observed, being this affirmative according to the low values found in the statistical analysis of errors, for example, with percentage error of approximately 5.0%, and also with the value of the Nash-Sutcliffe coefficient of efficiency of approximately 0.96. These results were important for the accuracy of the data, so that they could follow the profile of the observed time series, mainly revealing greater similarities of maximum and minimum values between both series, thus showing the capacity of the model to represent characteristics of local seasonality. Wind speed prediction methods can be a useful technique in the wind power sector, for example, being able to acquire important information on how local wind potential can be harnessed for possible electric power generation.

scholarly journals Deterministic and Interval Wind Speed Prediction Method in Offshore Wind Farm Considering the Randomness of Wind

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5595
Author(s):  
Qin Chen ◽  
Yan Chen ◽  
Xingzhi Bai
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Hybrid Model ◽  
Stationary Time Series ◽  
Percentage Error ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Interval Prediction ◽  
Stationary Time ◽  
Speed Prediction

In order to improve the prediction accuracy of wind speed, this paper proposes a hybrid wind speed prediction (WSP) method considering the fluctuation, randomness and nonlinear of wind, which can be applied to short-term deterministic and interval prediction. Variational mode decomposition (VMD) decomposes wind speed time series into nonlinear series Intrinsic mode function 1 (IMF1), stationary time series IMF2 and error sreies (ER). Principal component analysis-Radial basis function (PCA-RBF) model is used to model the nonlinear series IMF1, where PCA is applied to reduce the redundant information. Long short-term memory (LSTM) is used to establish a stationary time series model for IMF2, which can better describe the fluctuation trend of wind speed; mixture Gaussian process regression (MGPR) is used to predict ER to obtain deterministic and interval prediction results simultaneously. Finally, above methods are reconstructed to form VMD-PRBF-LSTM-MGPR which is the abbreviation of hybrid model to obtain the final results of WSP, which can better reflect the volatility of wind speed. Nine comparison models are built to verify the availability of the hybrid model. The mean absolute percentage error (MAE) and mean square error (MSE) of deterministic WSP of the proposed model are only 0.0713 and 0.3158 respectively, which are significantly smaller than the prediction results of comparison models. In addition, confidence intervals (CIs) and prediction interval (PIs) are compared in this paper. The experimental results show that both of them can quantify and represent forecast uncertainty and the PIs is wider than the corresponding CIs.

scholarly journals A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 83347-83358 ◽  
Author(s):  
Hai Tao ◽  
Sinan Q. Salih ◽  
Mandeep Kaur Saggi ◽  
Esmaeel Dodangeh ◽  
Cyril Voyant ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Wind Speed ◽  
Wind Speed Prediction ◽  
Speed Prediction

scholarly journals Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Aiqing Kang ◽  
Qingxiong Tan ◽  
Xiaohui Yuan ◽  
Xiaohui Lei ◽  
Yanbin Yuan
Keyword(s):  
Wind Speed ◽  
Empirical Mode Decomposition ◽  
Hybrid Model ◽  
Superposition Principle ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Wind Speed Prediction ◽  
Mode Decomposition ◽  
Speed Prediction

Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

Short Term Wind Speed Prediction Based on Linear Combination and Error Correction

2014 ◽  
Vol 521 ◽  
pp. 135-142 ◽  
Author(s):  
Jiang Ping Zou ◽  
Bi De Zhang ◽  
Yuan Tian
Keyword(s):  
Wind Speed ◽  
Error Correction ◽  
Linear Combination ◽  
Wind Farm ◽  
Arma Model ◽  
Weibull Model ◽  
Percentage Error ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Grey Verhulst Model

In order to improve the accuracy of wind speed prediction, a model based on linear combination and error correction is proposed. Firstly, sustainability model, grey verhulst model and weibull model are modified to obtain three predictions; secondly, the three predictions are matrix empowering analyzed based on the proximity to the ideal value to gain weights and linearly combined based on weights to gain the combination result; finally, the error between the actual value and the combination value is predicted by ARMA model, to correct the prediction wind speed to improve accuracy. The wind speed prediction results in the future for a wind farm in china are evaluated by RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error), demonstrating that the proposed model is reasonable and effective.

scholarly journals An Optimized Method of Wind Speed Prediction with Support Vector Machine and Genetic Algorithm

2021 ◽  
Vol 15 ◽  
pp. 212-217
Author(s):  
Guan-fa Li ◽  
Wen-sheng Zhu
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Support Vector Machine ◽  
Wind Speed ◽  
Power System ◽  
Prediction Models ◽  
Optimization Method ◽  
Support Vector ◽  
Wind Speed Prediction ◽  
Speed Prediction

Due to the randomness of wind speed and direction, the output power of wind turbine also has randomness. After large-scale wind power integration, it will bring a lot of adverse effects on the power quality of the power system, and also bring difficulties to the formulation of power system dispatching plan. In order to improve the prediction accuracy, an optimized method of wind speed prediction with support vector machine and genetic algorithm is put forward. Compared with other optimization methods, the simulation results show that the optimized genetic algorithm not only has good convergence speed, but also can find more suitable parameters for data samples. When the data is updated according to time series, the optimization range of vaccine and parameters is adaptively adjusted and updated. Therefore, as a new optimization method, the optimization method has certain theoretical significance and practical application value, and can be applied to other time series prediction models.

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