1. Introduction
Intending to achieve zero carbon emissions in all countries, the global generation and installed capacity of new energy are gradually increasing, and the sector is entering a period of rapid development [
1]. As a clean energy source, wind power contributes to the goal of zero carbon emissions [
2]. The Global Wind Energy Council (GWEC) released the Global Wind Report 2023 [
3], which indicates that 77.6 GW of wind power was installed globally in 2022, with a cumulative installed capacity of 906 GW, an increase of 8.3% over the previous year. With the increasing global demand for net-zero greenhouse gas emissions and the pressing need to attain energy security, the market outlook for the global wind power industry is expected to become even more optimistic. However, due to the high uncertainty and randomness of wind power, the grid must reserve a substantial quantity of spare capacity to mitigate the effects of this uncertainty and randomness [
4]. Therefore, improving the accuracy of wind power prediction can help promote new energy consumption to effectively manage and dispatch wind power generation systems.
There are three primary methods for forecasting wind power. The first is a physical method, which requires the use of a range of geographical information. Wind power is obtained by incorporating wind power data into the numerical weather prediction (NWP) model and combining it with geographic information surrounding the wind turbine. Globally, several wind energy forecasting systems based on physical methodologies and applicable to global or local regions have been developed. The Wind Atlas Analysis and Application Program (WAsP) model, which was created by Ris National Laboratory in Denmark [
5], has been widely utilized in wind energy resource assessment and forecasting throughout the globe. In Ref. [
6], applying numerical weather forecasting to the field of wind power prediction, the k-means clustering algorithm is used to process NWP data, simplify the prediction model, and increase the accuracy of the prediction. Ref. [
7] combines sequential forward feature selection algorithms and NWP models for power prediction to improve the overall quality of wind power forecasting. The second method is the statistical method, which predicts future wind power by establishing the relationship between historical data and wind power. The methods used are the autoregressive (AR) [
8] model, the autoregressive moving average (ARMA) [
9] model, the autoregressive integrated moving average (ARIMA) [
10] model, and the generalized autoregressive conditional heteroskedasticity (GRACH) [
11] model. These models predict future data with the linear characteristics of historical data. Due to the volatility of wind power, these algorithms are ineffective at predicting nonlinear wind power data. With the accelerated development of deep learning, numerous deep-learning models, such as convolutional neural networks (CNNs) [
12], support vector machines (SVMs) [
13], and LSTM [
14], are utilized in the field of wind power prediction. Ref. [
15] proposed an ultra-short-term prediction method for wind farm power generation based on long- and short-term memory networks, which has a higher prediction accuracy than artificial neural networks and support vector machines. However, using a single AI method for long-time series training can fall into the problem of local optimum or overfitting, bringing errors into the prediction results.
The third strategy involves combining prediction models. A single prediction model cannot attain a higher level of prediction accuracy; however, combining multiple models can improve prediction performance. Based on the various possible combinations, they can be broadly categorized as data correction prediction models and multi-step processing prediction models. Considering the issue of bias in the prediction results, [
16] proposes an NWP wind speed correction model, and the corrected NWP wind speed is utilized for wind power prediction. In Ref. [
17], combining optical gradient augmentation machine (LigutGBM) and BiLSTM for prediction, the LigutGBM method is used to correct the prediction error of the neural network model, and the results demonstrate that LigutGBM can further improve the prediction accuracy compared to the original method.
Considering that the fluctuations of wind power data cannot be accurately described by a single model, many researchers have adopted a multi-step processing approach to make predictions to extract the complete characteristics of wind power data in recent years. Initially, the original wind power sequence is decomposed, then the sequence is initially processed, the decomposed subsequence is predicted by a neural network model, and then the predicted data are superimposed to obtain the final predicted value [
18]. Ref. [
19] introduces singular entropy to evaluate and eliminate data following singular spectrum decomposition and then contrasts and calculates various preprocessing methods using artificial neural network models. Ref. [
20] uses variational modal decomposition to decompose the original data into multiple eigenmodal functions, then uses the Max-Relevance and Min-Redundancy algorithm to analyze the correlation between the modes, and then predicts them using the improved LSTM algorithm, achieving good results.
Based on the above analysis, this paper combines data correction and multi-step prediction to propose a wind power prediction model based on improved CEEMDAN and Markov chain. Initially, the original wind power data are preprocessed by CEEMDAN to obtain multiple intrinsic mode function (IMF) components and residual components; secondly, the components are reconstructed by using sample entropy and combined into several different new components based on the entropy value; subsequently, the different components are predicted by using BiLSTM neural network, and the data are superimposed to obtain the total predicted value; finally, the predicted values are data-corrected following the original wind power data.
Experiments on a wind power data set from a region in Xinjiang validate the model’s ability to improve prediction accuracy and prediction efficiency relative to other models. The following are the primary contributions of this paper.
A hybrid prediction model that incorporates CEEMDAN, SE, BiLSTM, and MC is proposed. In comparison to EMD, CEEMDAN is capable of resolving the issues of modal mixing and incomplete decomposition, as well as enhancing the efficacy of decomposition.
The decomposed subsequences are reconstructed by using SE to combine components with close entropy values into one reconstructed component to eliminate redundant features of wind power data, improve prediction accuracy and shorten prediction time.
An improved LSTM neural network model, BiLSTM neural network, is utilized. BiLSTM is a network with two LSTMs in reverse parallel, which can transmit both forward and reverse information to better mine the wind power data information.
MC is used to correct the data after the neural network prediction, and the k-step transfer matrix is used to characterize the data deviation, bringing the corrected data closer to the actual value.
The model of this paper was compared to other prediction models, validated on the data set, and evaluated by four indicators to demonstrate its superiority over other models.
6. Conclusions
This paper proposes a short-term wind power prediction model based on the CEEMDAN-SE-BiLSTM-MC algorithm for complex and evolving wind power data. First, the CEEMDAN algorithm is used to decompose the sequences, then the decomposed sequences are merged using SE to reduce the computational complexity, then the reconstructed subsequences are predicted using a BiLSTM neural network to superimpose the prediction results of each sequence, and then the total predicted power of the superimposed sequences is corrected by MC. Through example validation, the following conclusions were drawn:
The original wind power data were decomposed and reconstructed using CEEMDAN-SE. Compared with the unprocessed model, the model using CEEMDAN-SE had an 8% reduction in RMSE and a higher prediction accuracy in the middle of June, which indicates that CEEMDAN-SE can effectively process the wind power data and reduce the degree of redundancy of the data.
An improved LSTM neural network model is used to MC correct the predicted data, which effectively reduces the error of the predicted data and improves the accuracy of the prediction.
The CEEMDAN-SE-BiLSTM-MC model proposed in this paper is compared with other models, and the model is evaluated by four kinds of indexes using wind power data from four different months, which demonstrates the accuracy of this paper’s model in the prediction of wind power and has a better prediction effect compared with other prediction models.
Although the overall performance of the method presented in this paper is superior, the prediction results of individual points contain some errors when compared to other methods; therefore, the reasons for this must be investigated further in the future, and solutions are provided. Secondly, this paper only uses the historical data of a single wind farm to construct the model; in the future, data from multiple wind farms can be utilized for validation to improve the practical application value of the model. At the same time, to better serve the power dispatching work, the model can be software materialized to facilitate its use in engineering practice and provide strong support for the power dispatching work.