Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders
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| Titel: | Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders |
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| Autoren: | Weichao Dong, Hexu Sun, Zheng Li, Jingxuan Zhang, Huifang Yang |
| Quelle: | IEEE Access, Vol 8, Pp 146901-146914 (2020) |
| Verlagsinformationen: | Institute of Electrical and Electronics Engineers (IEEE), 2020. |
| Publikationsjahr: | 2020 |
| Schlagwörter: | multiscale mathematical morphological decomposition, short-term wind-speed forecasting, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, K-means clustering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, 7. Clean energy, stacked denoising autoencoders, TK1-9971 |
| Beschreibung: | Wind energy plays an increasingly important role in economic development. In this study, we propose a hybrid short-term wind-speed forecasting model comprising multiscale mathematical morphological decomposition (MMMD), K-means clustering algorithm, and stacked denoising autoencoder (SDAE) networks. First, in contrast to traditional signal-decomposing tools, the original wind-speed sequence is decomposed into a series of subsequences with different frequencies and fluctuant levels using the adaptive multiscale mathematical morphological algorithm directly in the time domain. The signal does not need to be transferred from the time domain to the frequency domain; hence, the accuracy can be considerably improved. Moreover, this is the first study that uses a time domain signal-decomposing tool in a hybrid wind forecasting model. Next, the data are split into different clusters of similar frequencies and fluctuant level subsequences using the K-means algorithm. The characteristics of each cluster are then captured using the SDAE as the core forecasting unit. Finally, the predictions of all subsequences are aggregated to obtain the final wind speed. The data from two real wind turbines are used to evaluate the performance of the proposed model, and the forecasting results are compared with five different benchmark models, namely, backpropagation neural network (BPNN), stacked denoising autoencoder (SDAE), mathematical morphology-backpropagation, mathematical morphology-SDAE, and K-means-SDAE for multiple scales, and two novel hybrid wind forecasting models namely, wavelet transform (WT)-K-means-SDAE and variation mode decomposition (VMD)-K-means-long short-term memory networks (LSTMs). The results of the comparison demonstrate that the proposed model provides a short-term wind-speed forecasting method whose prediction accuracy decreases with time; however, the proposed model achieves a better performance in comparison with other exiting models. At same time, the proposed model significantly increases the prediction accuracy of wind-speed forecasting and can be a reference for future research in this area. |
| Publikationsart: | Article |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3015336 |
| Zugangs-URL: | https://ieeexplore.ieee.org/ielx7/6287639/8948470/09163090.pdf https://doaj.org/article/080a81e4e4bd4b88a6dd0f8a63710a2d https://ieeexplore.ieee.org/document/9163090 https://dblp.uni-trier.de/db/journals/access/access8.html#DongSLZY20 https://doaj.org/article/080a81e4e4bd4b88a6dd0f8a63710a2d |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....ebf029034b4ec494f74e555969b27ff3 |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Wind energy plays an increasingly important role in economic development. In this study, we propose a hybrid short-term wind-speed forecasting model comprising multiscale mathematical morphological decomposition (MMMD), K-means clustering algorithm, and stacked denoising autoencoder (SDAE) networks. First, in contrast to traditional signal-decomposing tools, the original wind-speed sequence is decomposed into a series of subsequences with different frequencies and fluctuant levels using the adaptive multiscale mathematical morphological algorithm directly in the time domain. The signal does not need to be transferred from the time domain to the frequency domain; hence, the accuracy can be considerably improved. Moreover, this is the first study that uses a time domain signal-decomposing tool in a hybrid wind forecasting model. Next, the data are split into different clusters of similar frequencies and fluctuant level subsequences using the K-means algorithm. The characteristics of each cluster are then captured using the SDAE as the core forecasting unit. Finally, the predictions of all subsequences are aggregated to obtain the final wind speed. The data from two real wind turbines are used to evaluate the performance of the proposed model, and the forecasting results are compared with five different benchmark models, namely, backpropagation neural network (BPNN), stacked denoising autoencoder (SDAE), mathematical morphology-backpropagation, mathematical morphology-SDAE, and K-means-SDAE for multiple scales, and two novel hybrid wind forecasting models namely, wavelet transform (WT)-K-means-SDAE and variation mode decomposition (VMD)-K-means-long short-term memory networks (LSTMs). The results of the comparison demonstrate that the proposed model provides a short-term wind-speed forecasting method whose prediction accuracy decreases with time; however, the proposed model achieves a better performance in comparison with other exiting models. At same time, the proposed model significantly increases the prediction accuracy of wind-speed forecasting and can be a reference for future research in this area. |
|---|---|
| ISSN: | 21693536 |
| DOI: | 10.1109/access.2020.3015336 |