Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm

Meng, Anbo; Ge, Jiafei; Yin, Hao; Chen, Sizhe

doi:10.1016/j.enconman.2016.02.013

Cited by 234 publications

(60 citation statements)

References 32 publications

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“…Using MAPE as the index, and randomly selecting 10 days as the test set, the activation function and the number of hidden layer neurons of WRELM are determined using the cross-validation method [25,28]. The forecasting accuracies with different characteristics of WRELM are shown in Table 4.…”

Section: Forecasting Model For Wind Speed Based On Wrelmmentioning

confidence: 99%

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

et al. 2016

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Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD), the partial autocorrelation function (PACF), and weighted regularized extreme learning machine (WRELM), is proposed to improve the accuracy of wind speed forecasting. First, the historic wind speed time series is decomposed into several intrinsic mode functions (IMFs). Second, the partial correlation of each IMF sequence is analyzed using PACF to select the optimal subfeature set for particular predictors of each IMF. Then, the predictors of each IMF are constructed in order to enhance its strength using WRELM. Finally, wind speed is obtained by adding up all the predictors. The experiment, using real wind speed data, verified the effectiveness and advancement of the new approach.

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Section: Forecasting Model For Wind Speed Based On Wrelmmentioning

confidence: 99%

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

et al. 2016

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“…Therefore, according to the fast DWT algorithm, which was developed by Mallat [31], the approximate component and detailed component of a certain WD level can be obtained through multiple low-pass filters (LPF) and high-pass filters (HPF) [30,32,33]. As shown in Figure 2, the original data sequence S can be first decomposed into two part: approximate component A1 and detailed component D1 at WD level 1.…”

Section: Wavelet Decomposition Based Irradiance Forecastingmentioning

confidence: 99%

Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization

et al. 2018

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Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather conditions, such as cloudy or partly cloudy days, the variability of solar irradiance makes the accurate PV power forecasting a very hard task. Ensemble forecasting based on multiple models established by different theory has been proved as an effective means on improving forecasting accuracy. Classification modeling according to different patterns could reduce the complexity and difficulty of intro-class data fitting so as to improve the forecasting accuracy as well. When combining the two above points and focusing on the different fusion pattern specifically in terms of hourly time dimension, a time-section fusion pattern classification based day-ahead solar irradiance ensemble forecasting model using mutual iterative optimization is proposed, which contains multiple forecasting models based on wavelet decomposition (WD), fusion pattern classification model, and fusion models corresponding to each fusion pattern. First, the solar irradiance is forecasted using WD based models at different WD level. Second, the fusion pattern classification recognition model is trained and then applied to recognize the different fusion pattern at each hourly time section. At last, the final forecasting result is obtained using the optimal fusion model corresponding to the data fusion pattern. In addition, a mutual iterative optimization framework for the pattern classification and data fusion models is also proposed to improve the model's performance. Simulations show that the mutual iterative optimization framework can effectively enhance the performance and coordination of pattern classification and data fusion models. The accuracy of the proposed solar irradiance day-ahead ensemble forecasting model is verified when compared with a standard Artificial Neural Network (ANN) forecasting model, five WD based models and a single ensemble forecasting model without time-section fusion classification.

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“…After the input variables have been selected from candidate input variables such as the historical wind speed, temperature, humidity, and atmospheric pressure using the RF method, the functional relationship between the input and the output of the model becomes y = f(x ) (6) where x represents the optimal feature set obtained through input variable selection using the RF method. After the model input variables have been determined, an input variable matrix containing x and an output variable matrix containing y can be generated.…”

Section: Kelm Modelling and Ga Optimizationmentioning

confidence: 99%

“…Wind speeds are random and fluctuate significantly; therefore, accurate short-term wind speed forecasting is difficult. Methods based on time series [1][2][3][4] and machine learning (ML) [5][6][7][8][9][10] have been widely used to construct wind speed forecasting models. Because of their high forecasting accuracy and ability to generalize, Traditional ML methods such as neural networks (NN) and support vector machines (SVM) have become a research focus in recent years.…”

Section: Introductionmentioning

confidence: 99%

Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

Wang

Sun

et al. 2017

Energies

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Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF) is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model's accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.

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Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm

Cited by 234 publications

References 32 publications

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization

Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

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