Ricerc@Sapienza

Mediterranean islands have the advantage of favourable climatic conditions to use different marine renewable energy sources. Remote sensing can provide data to determine wind energy production potential and observational activity to identify, assess and detect suitable points in large marine areas. In this paper, a new combined model has been developed to integrate wind speed assessment, mapping and forecasting using Sentinel 1 satellite data through images processing and Adaptive Neuro-Fuzzy Inference System and the Bat algorithm.

This paper proposes a novel hybrid strategy based on intelligent approaches to forecast electricity consumptions. The proposed forecasting strategy includes three main steps: (a) the evaluation of a correlation coefficient for socio-economic indicators on electric energy consumptions, (b) the classification of historical and socio-economic indicators using the proposed feature selection method, (c) the development of a new combined method using Adaptive Neuro-Fuzzy Inference System and Whale Optimization Algorithm to predict electrical energy consumptions.

Microgrids (MGs) play a crucial role for the development of Smart Grids. They are conceived to intelligently integrate the generation from Distributed Energy Resources, to improve Demand Response (DR) services, to reduce pollutant emissions and curtail power losses, assuring the continuity of services to the loads as well. In this work it is proposed a novel synthesis procedure for modelling an Adaptive Neuro-Fuzzy Inference System (ANFIS) featured by multivariate Gaussian Membership Functions (MFs) and ?rst order Takagi-Sugeno rules.

The application of machine learning and soft computing techniques for function approximation is a widely explored topic in literature. Neural networks, evolutionary algorithms and support vector machines proved to be very effective, although these models suffer from very low level of interpretability by human operators. Conversely, Adaptive Neuro Fuzzy Inference Systems (ANFISs) demonstrated to be very accurate models featured by a considerable degree of interpretability. In this paper, a general framework for ANFIS training by clustering is proposed and investigated.

With the capillary spread of multi-energy systems such as microgrids, nanogrids, smart homes and hybrid electric vehicles, the design of a suitable Energy Management System (EMS) able to schedule the local energy flows in real time has a key role for the development of Renewable Energy Sources (RESs) and for reducing pollutant emissions. In the literature, most EMSs proposed are based on the implementation of energy systems prediction which enable to run a specific optimization algorithm.

Microgrids (MGs) development is one of the most pursued solution for the electric grid modernization into smart grids, as an effective approach to achieve the European Funding Program Horizon 2020 targets. In particular, residential grid-connected MGs demand the active role of the customer into the electric market, the fulfilment of Demand Response (DR) services, and a local control of the distribution energy balance.