2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Antonello Rosato, Rodolfo Araneo, Amedeo Andreotti, Federico Succetti and Massimo Panella
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Antonello Rosato: Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy
Rodolfo Araneo: Electrical Engineering Division of DIAEE, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy
Amedeo Andreotti: Electrical Engineering Department, University Federico II of Napoli, 80125 Napoli, Italy
Federico Succetti: Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy
Massimo Panella: Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy

Energies, 2021, vol. 14, issue 9, 1-18

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 inter-dependencies 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.

Keywords: multivariate prediction; deep learning; energy time series; convolutional neural network; long short-term memory network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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