Stefano Squartini

TL;DR: An approach based on the mixed-integer linear programming paradigm, which is able to provide an optimal solution in terms of tasks power consumption and management of renewable resources, is developed and yields an optimal task scheduling under dynamic electrical constraints.

TL;DR: A novel, data-driven approach to voice activity detection based on Long Short-Term Memory Recurrent Neural Networks trained on standard RASTA-PLP frontend features clearly outperforming three state-of-the-art reference algorithms under the same conditions.

TL;DR: This paper presents a novel unsupervised approach based on a denoising autoencoder which significantly outperforms existing methods by achieving up to 93.4% F-Measure.

TL;DR: In this paper, the authors present the operational results of a real life residential microgrid which includes six apartments, a 20kWp photovoltaic plant, a solar based thermal energy plant, and a geothermal heat pump, in the form of a 1300l water tank and two 5.8kWh batteries supplying, each, a couple of apartments.

TL;DR: Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.