1. Introduction
Wind energy is an important form of renewable energy and the most attractive alternative to fossil energy. The wind strikes the turbine blades during motion, driving the blades to rotate, and the turbine converts the rotational energy of the blades into electrical energy [
1]. In the process of wind power generation, unstable wind speed conditions can lead to degradation of power quality and even destabilize the power generation system [
2,
3]. If the wind speed conditions can be grasped in advance and the turbines and other equipment can be adjusted in time according to the wind speed conditions [
4], the power generation efficiency of the turbines can be greatly improved and the hardware equipment losses can be reduced. Based on the wind speed data of the wind field over time and the current wind speed data for wind speed prediction, we can make point predictions or interval predictions for the future wind speed. Wind speed prediction is an important technical support module in the production decision of wind power.
In recent years, a lot of work has been carried out on wind speed prediction. The current wind speed prediction content is divided into four types: long-term prediction, medium-term prediction, short-term prediction, and ultra-short-term prediction [
5]. The application of different prediction contents is shown in
Table 1.
According to the characteristics of random, complex, and many influencing factors of wind speed, the current prediction model includes four types: the physical model, statistical model, neural network model, and hybrid method model [
7]. Chen et al [
8]. developed an adaptive ARMA (auto-regressive moving-average) model for short-term load forecasting of a power system. Tyass et al. [
9,
10] realized short-term wind speed prediction based on the SARIMA (seasonal auto-regressive integrated moving average). Kritharas et al. [
11] made use of the long-term historical wind speed record data from the ground station and used the SARIMAX model to achieve the monthly wind speed prediction. However, because of the solidified regression fitting method, the statistical model did not have an advantage in dealing with the time-series wind speed with nonlinear characteristics [
12].
With the continuous development of artificial intelligence, model construction and prediction accuracy have changed dramatically [
13]. Zhu Q et al. [
14,
15] combined the advantages of LSTM and CNNs (convolutional neural networks), put forward a spatiotemporal correlation prediction model, and realized wind speed prediction by extracting spatial features from CNNs and temporal features from LSTM. Liu M et al. [
16] established a prediction system based on the GRU model, which used relevant historical data of the wind power system to predict results, and the prediction system could provide support for network structure analysis and the control strategy of a multi-energy system. Li C et al. [
17] proposed a novel hybrid model based on gated recurrent unit neural network and variational mode decomposition for wind speed interval prediction, which had higher prediction interval coverage probability and narrower prediction interval width. With the deepening of research, some scholars found that the single network structure of most machine learning algorithms was not suitable for complex and variable wind speed conditions. To further optimize the accuracy of model prediction, some scholars have put forward hybrid models with multiple advantages by combining different research methods. Zhou B et al. [
18,
19] proposed a hybrid method for ultra-short-term wind power prediction considering meteorological characteristics (wind direction, wind speed, temperature, pressure, humidity, etc.) and seasonal information, using ensemble empirical modal decomposition to decompose wind energy data into smooth subseries and principal component analysis to reduce redundant meteorological characteristics and improve the model accuracy. Jung J et al. [
20] used several historical meteorological variables, such as wind speed, temperature, humidity, pressure, dew point, and solar radiation, as indirect predictive characteristic quantities to achieve high accuracy wind speed prediction. The hybrid model improved the accuracy of wind speed prediction, but the feature expansion of wind speed data was less considered, thus requiring a large amount of feature data (e.g., meteorological features such as temperature) and high computational cost in the accuracy prediction.
Based on the above analysis, a hybrid short-term wind speed prediction model combining Tsfresh, Sparse PCA, and GRU is proposed in this paper. The model includes data pre-processing, feature extraction, feature selection, and data prediction parts. The model first converted the non-linear and non-smooth wind speed data into a data format suitable for the deep learning model. Then, the data of each subseries was feature transformed by the feature extraction and selection module to output the subseries with the most distinct data features. Finally, the subseries were predicted hierarchically using the prediction module, and the final prediction results were obtained after the reconstruction of different component predictions.
The contributions of this paper are as follows:
A new short-term wind speed hybrid prediction model was proposed;
A detailed comparison of the performance and computation time of the proposed model with neural network models and statistical models was made;
It was verified that the feature expansion approach was superior to the approach using data features related to wind speed in the point prediction of univariate wind speed time series data.
3. Realization of Prediction Model
Complex weather conditions such as wind speed and direction aggravated the difficulty of wind power generation. As shown in
Figure 2, the wind turbine control system timely adjusted the wind turbine according to the short-term wind speed prediction results.
The core of the proposed hybrid model was to create feature data similar to other meteorological features, select appropriate features, and predict with a GRU network. The block diagram of the prediction method is shown in
Figure 3.
The hybrid model steps were divided into the following steps:
All data were sliced and normalized.
Data feature processing included feature extraction and feature selection, with Tsfresh in charge of feature extraction and Sparse PCA in charge of feature selection.
GRU accepted the input data for data fitting to obtain the predicted values and calculated the error based on the predicted and true values.
The weights of the layers of the GRU network were updated based on the BPTT algorithm [
27].
After setting the number of training steps, steps 3 and 4 were repeated, and the Adam optimization algorithm was used to calculate the update steps until the loss function was minimized.
After the network was trained, the wind speed was predicted for each minute of the next hour.
Error analysis was carried out to evaluate the model performance.
During data normalization, outliers in the original data needed to be removed and the null values were filled using the average of the first seven historical data. All data were normalized using the Min-Max normalization method, as shown in Equation (14):
xi represents each value of the time series, xmax denotes the maximum value, xmin denotes the minimum value, and Xi represents the normalized value.
The normalized time series data needed to be sliced before feature extraction. The sliced data had more importance for discovering the detailed information of the time series data. The data slicing in the data preprocessing module was as shown in
Figure 4. Suppose there was a set of time series. Its length is marked as “
length (TimeSeriesData)”. The preprocessing module decomposed all the time series data into
n copies,
lt denoted the length of the subsequence,
λ denoted the length of time each segment of the sequence needed to be predicted in advance, and the subsequence sliding window was
δ.
The evaluation indexes for wind speed regression prediction included
MAE,
MSE,
RMSE, and
MAPE, which were calculated as shown in Equations (15)–(18):
yi denoted the true value and ŷi denoted the predicted value.
5. Conclusions
For the ultra-short-term wind speed prediction problem, a hybrid prediction method based on the GRU network, sparse PCA, and Tsfresh was proposed in this paper. The model first processed the univariate wind speed data for feature extraction and feature selection. Finally, the wind speed was predicted by the GRU network. To verify the predictive ability of the proposed model, this paper performed a comparative validation using different datasets and different models, respectively. The experimental results showed that:
- (1)
The proposed model GRU_Tsfresh_PCA had the best prediction results.
- (2)
Experimentally, it was demonstrated that the GRU network outperformed the LSTM network under the same computational conditions after the wind speed data were processed by features.
- (3)
The experimental results showed that the feature expansion could not only fit the existing meteorological features, but also fit the feature data more suitable for wind speed prediction than using the meteorological feature data related to the original wind speed data directly. These data were not directly obtained in reality.
- (4)
In summary, the proposed method in this paper could be expected to have good application prospects in the future because of its low computational cost and good prediction performance.
However, there were still some limitations in this study, such as the quality of the raw wind speed data. In future research work, we will further optimize the model based on the prediction effect of the actual wind speed data to further improve the generalization ability of the model.