Abstract
Wind speed is an important factor in wind power generation. Wind speed forecasting is complicated due to its highly nonstationary character. Therefore, this paper presents a hybrid framework for the development of multi-step wind speed forecasting based on variational model decomposition and convolutional neural networks. In the first step of signal pre-processing, the variational model decomposition approach decomposes the wind speed data into several independent modes under different center pulsation. The vibrations of decomposed modes are useful for accurate wind speed forecasting. Then, the influence of different numbers of modes and the input length of the convolutional neural network are discussed to select the optimal value through calculating the errors. During the regression step, each mode is treated as a channel that constitutes the input of the forecasting model. The convolution operations in convolutional neural networks extract helpful local features in each mode and the relationships between modes for forecasting. We take advantage of the convolutional neural network and directly output multi-step forecasting results. In order to show the forecasting and generalization performance of the proposed method, wind seed data from two wind farms in Inner Mongolia, China and Sotavento Galicia, Spain with different statistical information were employed. Some classic statistical approaches were adopted for comparison. The experimental results show the satisfactory performance for all of the methods in single-step forecasting and the advantages of using decomposed modes. The root mean squared errors range from 0.79 m/s to 1.64 m/s for all of the methods. In the case of multi-step forecasting, our proposed method achieves an outstanding improvement compared with the other methods. The root mean squared error of our proposed method was 1.30 m/s while the worst performance of the other methods was 9.68 m/s. The proposed method is able to directly predict the variation trend of wind speed based on historical data with minor errors. Hence, the proposed forecasting schemes can be utilized for wind speed multi-step forecasting to cost-effectively manage wind power generation.
Highlights
In recently years, sustainability transitions, which aim to create more sustainable consumption and production for socio-technical systems, have become a significant issue
To verify the effectiveness of the proposed Variational Mode Decomposition (VMD)-convolutional neural networks (CNNs) method, the following experiments were conducted to compare it with some other existing methods that have been proved feasible for wind speed forecasting
The input matrices of support vector regression (SVR) were determined by partial autocorrelation function (PACF) values [13], and the parameters
Summary
Sustainability transitions, which aim to create more sustainable consumption and production for socio-technical systems, have become a significant issue. Considering the randomness and non-stationarity of wind speed, it is difficult to accurately long-term predict through these time-series approaches Machine learning approaches such as artificial neural networks (ANN) [7,9], recurrent neural networks (RNN) [10,11,12], extreme learning machine (ELM) [13,14,15] and support vector regression (SVR) [15,16] exhibit great nonlinear fitting ability through modeling from the historical data. Naik et al combined variational mode decomposition with low rank multi-kernel ridge regression for short-term forecasting They constructed prediction intervals with different confidence levels for wind speed and wind power [21]. The convolutional operation in the CNN can learn the correlation relationships through integrating the decomposed modes into the input of one model, which enhanced the performance of multi-step forecasting. The time series of wind speed from different wind farms are applied to demonstrate the validity of the proposed method Section 5 presents our conclusions
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