Ultra-Short-Term Wind Power Forecasting Based on CGAN-CNN-LSTM Model Supported by Lidar

Abstract

Accurate prediction of wind power is of great significance to the stable operation of the power system and the vigorous development of the wind power industry. In order to further improve the accuracy of ultra-short-term wind power forecasting, an ultra-short-term wind power forecasting method based on the CGAN-CNN-LSTM algorithm is proposed. Firstly, the conditional generative adversarial network (CGAN) is used to fill in the missing segments of the data set. Then, the convolutional neural network (CNN) is used to extract the eigenvalues of the data, combined with the long short-term memory network (LSTM) to jointly construct a feature extraction module, and add an attention mechanism after the LSTM to assign weights to features, accelerate model convergence, and construct an ultra-short-term wind power forecasting model combined with the CGAN-CNN-LSTM. Finally, the position and function of each sensor in the Sole du Moulin Vieux wind farm in France is introduced. Then, using the sensor observation data of the wind farm as a test set, the CGAN-CNN-LSTM model was compared with the CNN-LSTM, LSTM, and SVM to verify the feasibility. At the same time, in order to prove the universality of this model and the ability of the CGAN, the model of the CNN-LSTM combined with the linear interpolation method is used for a controlled experiment with a data set of a wind farm in China. The final test results prove that the CGAN-CNN-LSTM model is not only more accurate in prediction results, but also applicable to a wide range of regions and has good value for the development of wind power.

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

“…While combining various classical feature extraction methods helps in selecting better input features, it is ineffective in extracting deep and highly nonlinear features from complex wind data. Methods such as autoencoder (AE), variational AE (VAE), restricted Boltzmann machine (RBM), CNN [68], temporal convolutional network (TCN) [69], and attention mechanism [70] have been proven to be effective tools for nonlinear feature extraction and widely applied in the field of WSP and WPP. In [65], the VMD technique was employed to decompose the original wind speed sequence, obtaining relatively stable wind speed sequences.…”

Optimization of Energy Storage Allocation in Wind Energy Storage Combined System Based on Improved Sand Cat Swarm Optimization Algorithm

“…Since the original scenes of wind, PV and load power are excessive in amount and not representative, in order to obtain the corresponding typical scenes, the improved k-means clustering algorithm is adopted to process all the data, reduce and merge the data of the whole year into several typical scenes, and then select the data with the highest frequency of occurrence from these typical scenes as the typical day [43]. The generation process of typical scenes is shown in Figure 3.…”