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[12] Hu T, Li K, Ma H, et al. Quantile forecast of renewable energy generation based on indicator gradient descent and deep residual BiLSTM.Control Engineering Practice,
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[13] Hu T, Wu W, Guo Q, et al. Very short-term spatial and temporal wind power forecasting: A deep learning approach.CSEE Journal of Power and Energy Systems,
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[14] Hu T, Ma H, Sun H, et al. Electrochemical-theory-guided modeling of the conditional generative adversarial network for battery calendar aging forecast.IEEE Journal of emerging and selected topics in power electronics,
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[15] H. Liu, J. Liu, T. Hu* and H. Ma, Spatio-Temporal Probabilistic Forecasting of Wind Speed Using Transformer-Based Diffusion Models.IEEE Transactions on Sustainable Energy,
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