Ricerc@Sapienza

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
in the modern management of electrical grids shifting from reactive to proactive, with also the help of
advanced monitoring systems, data analytics and advanced demand side management programs. The gradual
move towards a smart grid environment impacts not only the operating control/management of the grid, but
also the electricity market. The focus of this article is on advanced methods for predicting photovoltaic
energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system
management, even at prosumer's level and for improving the resilience of smart grids. Four different deep
neural models for the multivariate prediction of energy time series are proposed; all of them are based on the
Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term
dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher
levels of abstraction, since they allow to combine and filter different time series considering all the available
information. The proposed models are applied to real-world energy problems to assess their performance
and they are compared with respect to the classic univariate approach that is used as a reference benchmark.
The significance of this work is to show that, once trained, the proposed deep neural networks ensure their
applicability in real online scenarios characterized by high variability of data, without requiring retraining
and end-user's tricks.