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• W3215708800 abstract "With the advancement of wind power generation technology, wind power plays an increasing role in modern power grids. To properly consider wind power for power systems planning and operation purpose, wind power and wind speed must be forecasted accurately. Wind is chaotic, random, irregular, and non-stationary in nature, which creates significant challenges in wind speed forecasting. This paper aims to forecast wind speed using both the statistical time series analysis method (autoregressive moving average (ARMA)) and neural network methods (feedforward neural network (FNN), recurrent neural network (RNN), long short-term memory (LSTM), and the gated recurrent unit (GRU)). The performance of the proposed five models is compared with the measured wind speed data, and the GRU model shows the best performance with the highest prediction accuracy. The four ANN models outperform the ARMA model." @default.
• W3215708800 created "2021-12-06" @default.
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• W3215708800 date "2021-10-22" @default.
• W3215708800 modified "2023-09-25" @default.
• W3215708800 title "Wind Speed Forecasting Using ARMA and Neural Network Models" @default.
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• W3215708800 doi "https://doi.org/10.1109/epec52095.2021.9621650" @default.
• W3215708800 hasPublicationYear "2021" @default.
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