The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead forecasting of gas consumption ...
PDF | The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead fore- casting of gas.
Abstract. The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead fore- casting of gas ...
Abstract. The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead fore- casting of gas ...
The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead forecasting of thermal comfort ...
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Sensitivity based feature selection for recurrent neural network applied to forecasting of heating gas consumption. M Macas, F Lauro, F Moretti, S Pizzuti ...
Sep 26, 2023 · The literature recently applied recurrent neural networks (RNN) to predict biomass HHV from all proximate and ultimate compositional analyses.
Jun 15, 2023 · In this article, we examined the performance of several DRNN networks for the task of hourly electricity and heat demand prediction over long-term time ...
The present research study explores three types of neural network approaches for forecasting natural gas consumption in fifteen cities throughout Greece.
We investigate prediction performance resulting from various model configurations, including training techniques, hidden neurons, delays, and data segmentation.