multivariate prediction | Ricerc@Sapienza

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

The large-scale penetration of renewable energy sources is forcing the transition towards
the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new
methodologies for the dynamic energy management of distributed energy resources and foster to form
partnerships and overcome integration barriers. The prediction of energy production of renewable energy
sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool

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.

Multidimensional feeding of LSTM networks for multivariate prediction of energy time series

We propose a deep learning approach for multivariate forecasting of energy time series. It is developed by using Long Short-Term Memory deep neural networks so that different related time series, incorporating information of longterm dependencies, can be joined together as a multidimensional input of the deep neural network. The learning scheme can be represented as a stacked LSTM network in which one or more layers are cascaded, feeding their output to the input of the sequent layer.

Multivariate prediction in photovoltaic power plants by a stacked deep neural network

In this paper, a new approach on energy time series prediction is carried out. We propose a deep learning technique with the employment of specific neural network architectures: Convolutional Neural Network and Long Short-Term Memory network. The goal is to exploit the correlation between several time series, joining and filtering them together as to bring out the long-term dependencies among all the observations. We superpose many different functional layers, thus providing a stacked scheme that can result in a greater approximation capability.