2-D Convolutional Deep Neural Network for Multivariate Energy Time Series Prediction

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.