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

Forecasting electricity prices is today an essential tool in the day-ahead competitive market. Prediction techniques based on neural and fuzzy neural networks are very promising in terms of prediction performance and model accuracy. In this paper, we investigate the applicability to the electricity market of three well-known approaches, namely Radial Basis Function neural networks, Mixture of Gaussian neural networks and Higher-Order Neuro-Fuzzy Inference System.

Renewable electricity generation has variable and non-dispatchable output that rises several technical, economic and feasibility concerns, calling for energy storage capacity and forecasting techniques to allow the integration of large amounts of variable generation into existing grids. These problems need careful attention in small islands that are not connected to the national transmission grid. In this paper, we present a study for the small Italian island of Ponza on the use of Echo State Networks to forecast real-world energy time series.