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

Hao Zhen, Dongxiao Niu, Min Yu, Keke Wang, Yi Liang and Xiaomin Xu
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Hao Zhen: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Min Yu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Keke Wang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Yi Liang: School of Management, Hebei Geo University, Shijiazhuang 050031, China
Xiaomin Xu: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2020, vol. 12, issue 22, 1-24

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 R 2 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.

Keywords: wind power; deep learning; power forecast; hybrid model; BiLSTM-CNN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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