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• W3209586344 abstract "With the significant increase in wind speed usage as a clean source of energy, an accurate wind speed forecasting system is a must for more effective utilization of this energy. Failure to consider the inherent spatio-temporal features of wind speed time series leads to the lack of generalization capacity for current wind speed forecasting approaches. This paper proposes an end-to-end deep neural network framework, i.e., convolutional rough long short-term memory (ConvRLSTM), to extract spatiotemporal wind correlations and mitigate the inherent uncertainties in wind time series by incorporating the Rough set theory into a combination of convolution neural network (CNN) and LSTM units. Our proposed model receives the historical data of wind speed for a $20 times 20$ array of wind turbines in North Carolina, US. Several ConvRLSTM layers extract the most relevant features for the forecasting task, and finally, fully connected layers predict 400 wind speed values using the spatial features obtained by the CNN and temporal features computed by the LSTM. Through analyzing the numerical forecasting results, it can be inferred that the proposed approach outperforms the mainstream and recently published forecasting strategies in terms of the RMSE metric." @default.
• W3209586344 created "2021-11-08" @default.
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• W3209586344 date "2021-09-07" @default.
• W3209586344 modified "2023-10-17" @default.
• W3209586344 title "Robust Wind Speed Forecasting: A Deep Spatio-Temporal Approach" @default.
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• W3209586344 doi "https://doi.org/10.1109/eeeic/icpseurope51590.2021.9584602" @default.
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