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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 architecture of the TNN.
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Related In: Results  -  Collection


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fig2: The architecture of the TNN.

Mentions: A TNN generally consists of four layers, an input layer, two hidden layers, and an output layer. Each of those layers contains nodes, and these nodes are connected to nodes at adjacent layer(s). The basic architecture of a TNN is shown in Figure 2. The calculated process can be described as follows.


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

Wang J - ScientificWorldJournal (2014)

The architecture of the TNN.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: The architecture of the TNN.
Mentions: A TNN generally consists of four layers, an input layer, two hidden layers, and an output layer. Each of those layers contains nodes, and these nodes are connected to nodes at adjacent layer(s). The basic architecture of a TNN is shown in Figure 2. The calculated process can be described as follows.

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