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, A.; Araneo, R.; Andreotti, A.; Succetti, F.; Panella, M.. - In: ENERGIES. - ISSN 1996-1073. - 14:9(2021), p. 2392. [10.3390/en14092392]

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

Andreotti A.;
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.
2021
2-d convolutional deep neural network for the multivariate prediction of photovoltaic time series / Rosato, A.; Araneo, R.; Andreotti, A.; Succetti, F.; Panella, M.. - In: ENERGIES. - ISSN 1996-1073. - 14:9(2021), p. 2392. [10.3390/en14092392]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/866421
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 7
social impact