Abstract
Accurate ultrashort-term forecasting of mid–low-altitude wind is essential to the safe and stable flight of aircraft. Traditional artificial intelligence (AI) wind forecasting methods convert the dynamic time series regression problem into a static spatial modeling problem, while ignoring the dynamic characteristics of the wind as a typical time series, and the prediction accuracy is limited. In this paper, the long short-term memory (LSTM) network is used to dynamically model the time series of wind speed to realize ultrashort-term forecasting at mid–low altitudes. The measured data of wind lidar are used to verify the conclusions. The results show that the performance of LSTM model outperforms artificial neural network and support vector machine.
Foundation items: National Natural Science Foundation of China (61505036), the Fund Project of Guizhou Provincial Science and Technology Department (QKHJZ [2015] 2009).
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Chen, C. et al. (2019). Ultrashort-Term Forecasting of Mid–Low-Altitude Wind Based on LSTM. In: Wang, L., Wu, Y., Gong, J. (eds) Proceedings of the 5th China High Resolution Earth Observation Conference (CHREOC 2018). CHREOC 2018. Lecture Notes in Electrical Engineering, vol 552. Springer, Singapore. https://doi.org/10.1007/978-981-13-6553-9_12
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DOI: https://doi.org/10.1007/978-981-13-6553-9_12
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