Jingcheng Qian1, Mingfang Zhu1, Yingnan Zhao2,*, Xiangjian He3
Computer Systems Science and Engineering, Vol.39, No.2, pp. 197-209, 2021, DOI:10.32604/csse.2021.016911
Abstract Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power. Temporal-spatial wind speed features contain rich information; however, their use to predict wind speed remains one of the most challenging and less studied areas. This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attention-based long short-term memory (LSTM), termed 2Attn-LSTM, a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data. To eliminate the unevenness of the original wind speed,… More >
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