A novel deep learning approach in proposed in this paper for multivariate prediction of energy time series. It is developed by using Convolutional Neural Network and Long Short-Term Memory models, in such a way that several correlated time series can be joined and filtered together considering the long term dependencies on the whole information. The learning scheme can be viewed as a stacked deep neural network where one or more layers are superposed, feeding their output in the sequent layer's input. The new approach is applied to real-world problems in energy area to prove robustness and accuracy.

2-D convolutional deep neural network for multivariate energy time series prediction / Rosato, A.; Araneo, R.; Andreotti, A.; Panella, M.. - (2019), pp. 1-4. ((Intervento presentato al convegno 19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 tenutosi a Genova, Italia [10.1109/EEEIC.2019.8783304].

2-D convolutional deep neural network for multivariate energy time series prediction

Rosato A.;Araneo R.;Panella M.
2019

Abstract

A novel deep learning approach in proposed in this paper for multivariate prediction of energy time series. It is developed by using Convolutional Neural Network and Long Short-Term Memory models, in such a way that several correlated time series can be joined and filtered together considering the long term dependencies on the whole information. The learning scheme can be viewed as a stacked deep neural network where one or more layers are superposed, feeding their output in the sequent layer's input. The new approach is applied to real-world problems in energy area to prove robustness and accuracy.
19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
convolutional neural network; deep learning; energy time series; LSTM; multivariate prediction
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
2-D convolutional deep neural network for multivariate energy time series prediction / Rosato, A.; Araneo, R.; Andreotti, A.; Panella, M.. - (2019), pp. 1-4. ((Intervento presentato al convegno 19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 tenutosi a Genova, Italia [10.1109/EEEIC.2019.8783304].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1310850
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