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A hybrid wavelet transform based short-term wind speed forecasting approach.

Wang J - ScientificWorldJournal (2014)

Bottom Line: In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN.Afterwards, the final prediction value can be obtained by the sum of these prediction results.Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

View Article: PubMed Central - PubMed

Affiliation: School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China.

ABSTRACT
It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

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Related in: MedlinePlus

The PACFs of the original wind speed series, low-pass filter, and three high-pass filters (spring).
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig6: The PACFs of the original wind speed series, low-pass filter, and three high-pass filters (spring).

Mentions: In order to overcome the limitation of ignoring the relationship between input(s) and output(s) of TNN, inspired from the identification of parameter p in ARMA (p, q) model, the PACF is utilized to identify the inputting data structure of the TNN models. Figure 6 shows the plots of PACF against the lag length in spring. According to the potential existing relation between the wind subseries and their lags, the input numbers of forecasting models are decided. Similarly, the plots of PACF in others can be shown. Table 2 lists them.


A hybrid wavelet transform based short-term wind speed forecasting approach.

Wang J - ScientificWorldJournal (2014)

The PACFs of the original wind speed series, low-pass filter, and three high-pass filters (spring).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4129147&req=5

fig6: The PACFs of the original wind speed series, low-pass filter, and three high-pass filters (spring).
Mentions: In order to overcome the limitation of ignoring the relationship between input(s) and output(s) of TNN, inspired from the identification of parameter p in ARMA (p, q) model, the PACF is utilized to identify the inputting data structure of the TNN models. Figure 6 shows the plots of PACF against the lag length in spring. According to the potential existing relation between the wind subseries and their lags, the input numbers of forecasting models are decided. Similarly, the plots of PACF in others can be shown. Table 2 lists them.

Bottom Line: In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN.Afterwards, the final prediction value can be obtained by the sum of these prediction results.Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

View Article: PubMed Central - PubMed

Affiliation: School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China.

ABSTRACT
It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

Show MeSH
Related in: MedlinePlus