2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series
Keyword(s):
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.
2018 ◽
Vol 159
◽
pp. 132-147
◽
2019 ◽
Vol 57
(6)
◽
pp. 114-119
◽
Keyword(s):
2019 ◽
Vol 1213
◽
pp. 042039
◽
Keyword(s):
Keyword(s):
2021 ◽
Keyword(s):
Keyword(s):