Ricerc@Sapienza

The research project aims at the use of computational intelligence techniques, neural networks and fuzzy logic, in a real context of prediction of energy series, to implement a system for optimal management of renewable energy sources in smart grids.
Distributed scenarios are increasingly common in which small production units are connected directly to the distribution network. Economic, environmental and incentive policies have pushed the energy market towards distributed renewable sources. In these contexts, however, the greatest difficulties are due to the variability and reliability of resources. For an efficient management of the whole system, the prediction of the energy produced becomes necessary so that an intelligent adaptation of the distribution network is possible. This is made possible by the fact that the production parameters of any generation unit are constantly monitored with smart metering technologies that give the possibility to obtain current and output voltage in real time.
In the prediction of time series the non-stationarity and, often, the non-linearity of the series themselves lead to complex dynamics, with chaotic properties, which are difficult to model adequately using standard predictive models. This project therefore aims to adapt machine learning techniques to analyze data associated with renewable energy sources and intellingently adapt it to the management of smart, possibly isolated, smart grids. In particular, the project aims to develop solutions that can make use of machine learning algorithms for prediction in totally distributed systems in which heterogeneous agents interact (large photovoltaic plants, domestic cogeneration, etc.) with the constraint of controlling all the RESs and their surplus storage.