Abstract
In this study, a novel hybrid model using signal decomposition technique and extreme learning machine (ELM) is developed for wind speed forecasting. In the proposed model, signal decomposition technique, namely wavelet packet decomposition (WPD), is utilized to decompose the raw non-stationary wind speed data into relatively stable sub-series; then, ELMs are employed to predict wind speed using these stable sub-series, eventually, the final wind speed forecasting results are calculated through combination of each sub-subseries prediction. To evaluate the forecasting performance, real historical wind speed data from a wind farm in China are employed to make short term wind speed forecasting. Compared with other forecasting method mentioned in the paper, the proposed hybrid model WPD-ELM can improve the wind speed forecasting accuracy.
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Acknowledgments
This work was supported by the Open Research Fund of Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University; the Projects of Science and Technology Commission of Shanghai Municipality of China under Grant (No. 17511107002 and No. 15JC1401900); Natural Capital Project of Anhui Province under Grant Nos. 1408085ME105 and 1501021015.
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Sun, S., Fu, J., Zhu, F. (2017). A Short Term Wind Speed Forecasting Method Using Signal Decomposition and Extreme Learning Machine. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 763. Springer, Singapore. https://doi.org/10.1007/978-981-10-6364-0_3
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