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

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Computer science Robustness (evolution) Deep learning Convolutional neural network Artificial intelligence Artificial neural network Series (stratigraphy) Superposition principle Scheme (mathematics) Multivariate statistics Time series Photovoltaic system Machine learning Engineering Mathematics Gene Electrical engineering Biology Biochemistry Mathematical analysis Chemistry Paleontology

Antonello Rosato , Rodolfo Araneo , Amedeo Andreotti , Federico Succetti , Massimo Panella ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.3390/en14092392 · OA: W3158233180

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.