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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

DWT results in spring.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig5: DWT results in spring.

Mentions: The WTT converts a wind speed series into a set of constitutive series. These constitutive series present a better behavior than the original wind speed series, and therefore they can be predicted more accurately. The reason for the better behavior of the constitutive series is the filtering effect of the WTT. In the WTT literature, a lot of wavelet functions are used for wavelet decomposition. According to the difference of resolution capability and efficiency, a wavelet function of type Daubechies of order 3 (abbreviated as Db3) is used as the mother wavelet in this paper. Also, considering the characteristics of the experimental data, three decomposition levels are considered, since it describes the wind speed series in a more thorough and meaningful way than the others. Three-level decomposition process is shown in Figure 4. Figure 5 shows the decomposition process of the original wind speed series in spring. From Figure 5, it can be seen that the original wind speed series has been decomposed into a low-pass filter (A3) and three high-pass filters (D1, D2, and D3). The low-pass filter is used to capture the approximated and low frequency nature of the data, whereas the high-pass filter is used to capture the detailed and high-frequency nature of the data. They will be used to build their corresponding TNN forecasting models, respectively. Similarly, the decomposition process of the others can also be got.


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

Wang J - ScientificWorldJournal (2014)

DWT results in spring.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: DWT results in spring.
Mentions: The WTT converts a wind speed series into a set of constitutive series. These constitutive series present a better behavior than the original wind speed series, and therefore they can be predicted more accurately. The reason for the better behavior of the constitutive series is the filtering effect of the WTT. In the WTT literature, a lot of wavelet functions are used for wavelet decomposition. According to the difference of resolution capability and efficiency, a wavelet function of type Daubechies of order 3 (abbreviated as Db3) is used as the mother wavelet in this paper. Also, considering the characteristics of the experimental data, three decomposition levels are considered, since it describes the wind speed series in a more thorough and meaningful way than the others. Three-level decomposition process is shown in Figure 4. Figure 5 shows the decomposition process of the original wind speed series in spring. From Figure 5, it can be seen that the original wind speed series has been decomposed into a low-pass filter (A3) and three high-pass filters (D1, D2, and D3). The low-pass filter is used to capture the approximated and low frequency nature of the data, whereas the high-pass filter is used to capture the detailed and high-frequency nature of the data. They will be used to build their corresponding TNN forecasting models, respectively. Similarly, the decomposition process of the others can also be got.

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