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This article introduces an #efficient #multistep #nonparametric residual network for improved #solar #power #forecasting. The proposed architecture first incorporates a synchrosqueezing transform to extract #high-#resolution #time-#frequency coefficients of solar power inputs in their respective #time-#frequency #scales. An improved #residual #network, a #Multicondense #Residual #Network (#M-#cDRN), integrating multiresidual network and condense network techniques to #predict #solar #power #coefficients, is proposed. M-cDRN addresses challenges of overfitting and vanishing gradients in residual networks. A quantile regression network is employed to generate quantiles with different proportions.----Garima Prashal, Sumathi parasuraman, Narayana Prasad Padhy More details can be found at this link: https://lnkd.in/gxX5PnAx

Synchrosqueezed Transform Based Multicondense Residual Network for Ultra-Short-Term Solar Power Forecasting

Synchrosqueezed Transform Based Multicondense Residual Network for Ultra-Short-Term Solar Power Forecasting

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