A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction

Abstract

The inherent intermittency and uncertainty of wind power have brought challenges in accurate wind power output forecasting, which also cause tricky problems in the integration of wind power to the grid. In this paper, a hybrid deep learning model bidirectional long short term memory-convolutional neural network (BiLSTM-CNN) is proposed for short-term wind power forecasting. First, the grey correlation analysis is utilized to select the inputs for forecasting model; Then, the proposed hybrid model extracts multi-dimension features of inputs to predict the wind power from the temporal-spatial perspective, where the Bi-LSTM model is utilized to mine the bidirectional temporal characteristics while the convolution and pooling operations of CNN are utilized to extract the spatial characteristics from multiple input time series. Lastly, a case study is conducted to verify the superiority of the proposed model. Other deep learning models (Bi-LSTM, LSTM, CNN, LSTM-CNN, CNN-BiLSTM, CNN-LSTM) are also simulated to conduct comparison from three aspects. The results show that the BiLSTM-CNN model has the best accuracy with the lowest RMSE of 2.5492, MSE of 6.4984, MAE of 1.7344 and highest R2 of 0.9929. CNN has the fastest speed with an average computational time of 0.0741s. The hybrid model that mines the spatial feature based on the extracted temporal feature has a better performance than the model mines the temporal feature based on the extracted spatial feature.

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

“…Recent papers addressing wind power forecasts could be broadly classified into 5 categories: papers focused on how to increase NWP accuracy [4][5][6][7][8], good-practice prediction guidelines [9][10][11], comparisons of accuracy across prediction models [12][13][14][15], hybrid and ensemble methods [16][17][18][19][20][21][22][23][24][25][26][27], and conventional methods improved by, among other things, preprocessing [28][29][30][31][32][33][34][35]. At this point, clear distinction should be made between hybrid, ensemble and improved models.…”

“…Hybridization [16][17][18][19][20][21] and parallelization [22][23][24][25][26][27] of prediction models use datarefining and error compensation, respectively, as an approach to maximize prediction accuracy. The most common bases for hybrid models in recent literature are ANNs [17][18][19]21] due to their generalization ability, while the most common hybrid add ons would be single optimization methods [16,18,20,21]. With varying implementation, error reduction can be achieved in different ways.…”

“…With varying implementation, error reduction can be achieved in different ways. For that purpose, Zhen et al, 2020 [17] propose a Bidirectional Long Short Term Memory-Convolutional Neural Network (BiLSTM-CNN) model for better extraction of spatio-temporal features from data and Zhou et al, 2020 [19] describe a long short-term memory-seasonal autoregressive integrated moving average (LSTM-SARIMA) model for capturing seasonality.…”

Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks

“…To the best of our knowledge, no previous study attempted to investigate the potential of using BiLSTM for ST prediction. There are no intelligent algorithms or models that are competent for all problems, and deep learning models cannot be spared as well (Zhen et al 2020). There is still room for the improvement of deep learning models in ST forecasting.…”

Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM

“…The data preprocessing method is selected for noise reduction preprocessing before bus load curve prediction in order to obtain more stable prediction results. In reference [17], the bidirectional long-term and short-term memory (Bi-LSTM) network is used to predict the bus load sequence, which effectively improves the learning ability of the network to historical data. However, with the combination prediction model dividing the complete sequence into multiple subsequences, not all sequences are suitable for bidirectional training.…”