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

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such fashion that inter-dependencies among several different can be exploited used for forecasting purposes by filtering joining their samples. resulting summarized as superposition network layers, stacked neural architecture. We proved accuracy robustness proposed approach testing it real-world problems.