A Model Combining Stacked Auto Encoder and Back Propagation Algorithm for Short-Term Wind Power Forecasting
Recently, many countries have spent great efforts on wind power generation. Although there have been many methods in the field of wind power forecasting, the persistence statistics model based on historical data is still being challenged due to the randomness and uncontrollability in wind power. Hen...
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| Vydáno v: | IEEE access Ročník 6; s. 17851 - 17858 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | Recently, many countries have spent great efforts on wind power generation. Although there have been many methods in the field of wind power forecasting, the persistence statistics model based on historical data is still being challenged due to the randomness and uncontrollability in wind power. Hence, a more accurate and effective wind power forecasting method is still required. In this paper, a new forecasting method is proposed by combining stacked auto-encoders (SAE) and the back propagation (BP) algorithm. First, an SAE with three hidden layers is designed to extract the characteristics from the reference data sequence, and the subsequent loss function is used in the pre-training process to obtain the optimal initial connection weights of the deep network. Second, after adding one output layer to the stacked auto encoders, the BP algorithm is used to fine tune the weights of the whole network. To achieve the best network architecture, the particle swarm optimization is adopted to decide the number of neurons of the hidden layer and the learning rate of each auto encoder. Experimental results show that, for short-term wind power forecasting, the proposed method achieves more stable and effective performance than the existing BP neural network and support vector machines. The improvement in accuracy is 12% on average under different time steps. |
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| AbstractList | Recently, many countries have spent great efforts on wind power generation. Although there have been many methods in the field of wind power forecasting, the persistence statistics model based on historical data is still being challenged due to the randomness and uncontrollability in wind power. Hence, a more accurate and effective wind power forecasting method is still required. In this paper, a new forecasting method is proposed by combining stacked auto-encoders (SAE) and the back propagation (BP) algorithm. First, an SAE with three hidden layers is designed to extract the characteristics from the reference data sequence, and the subsequent loss function is used in the pre-training process to obtain the optimal initial connection weights of the deep network. Second, after adding one output layer to the stacked auto encoders, the BP algorithm is used to fine tune the weights of the whole network. To achieve the best network architecture, the particle swarm optimization is adopted to decide the number of neurons of the hidden layer and the learning rate of each auto encoder. Experimental results show that, for short-term wind power forecasting, the proposed method achieves more stable and effective performance than the existing BP neural network and support vector machines. The improvement in accuracy is 12% on average under different time steps. |
| Author | Han, Liye Tian, Wei Ma, Xuehai Huang, Xujian Jiao, Runhai |
| Author_xml | – sequence: 1 givenname: Runhai orcidid: 0000-0003-3759-4175 surname: Jiao fullname: Jiao, Runhai email: runhaijiao@ncepu.edu.cn organization: School of Control and Computer Engineering, North China Electric Power University, Beijing, China – sequence: 2 givenname: Xujian surname: Huang fullname: Huang, Xujian organization: School of Control and Computer Engineering, North China Electric Power University, Beijing, China – sequence: 3 givenname: Xuehai surname: Ma fullname: Ma, Xuehai organization: School of Control and Computer Engineering, North China Electric Power University, Beijing, China – sequence: 4 givenname: Liye surname: Han fullname: Han, Liye organization: School of Control and Computer Engineering, North China Electric Power University, Beijing, China – sequence: 5 givenname: Wei surname: Tian fullname: Tian, Wei organization: Electrical and Computer Engineering Department, Illinois Institute of Technology, Chicago, IL, USA |
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| SubjectTerms | Algorithms Back propagation Back propagation networks Coders Computer architecture Electric power generation Feature extraction Forecasting Machine learning Mathematical models Neural networks Neurons Particle swarm optimization Predictive models stacked auto-encoders Support vector machines Training Wavelet analysis wind energy Wind power wind power forecasting Wind power generation |
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| Title | A Model Combining Stacked Auto Encoder and Back Propagation Algorithm for Short-Term Wind Power Forecasting |
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