Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that interdependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

2-D convolutional deep neural network for the multivariate prediction of photovoltaic time series / Rosato, Antonello; Araneo, Rodolfo; Andreotti, Amedeo; Succetti, Federico; Panella, Massimo. - In: ENERGIES. - ISSN 1996-1073. - 14:9(2021), pp. 1-18. [10.3390/en14092392]

2-D convolutional deep neural network for the multivariate prediction of photovoltaic time series

Rosato, Antonello;Araneo, Rodolfo;Succetti, Federico;Panella, Massimo
2021

Abstract

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that interdependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.
multivariate prediction; deep learning; energy time series; convolutional neural network; long short-term memory network
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2-D convolutional deep neural network for the multivariate prediction of photovoltaic time series / Rosato, Antonello; Araneo, Rodolfo; Andreotti, Amedeo; Succetti, Federico; Panella, Massimo. - In: ENERGIES. - ISSN 1996-1073. - 14:9(2021), pp. 1-18. [10.3390/en14092392]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1541789
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