7 citations · 11 across the 9 of their papers we have counts for
10 papers
Physics-informed structured learning of a class of recurrent neural networks with guaranteed properties
Daniele Ravasio, Claudia Sbardi, Marcello Farina +1
This paper proposes a physics-informed learning framework for a class of recurrent neural networks tailored to large-scale and networked systems. The approach aims to learn control…
Development of a velocity form for a class of RNNs, with application to offset-free nonlinear MPC design
Daniele Ravasio, Bestem Abdulaziz, Marcello Farina +1
This paper addresses the offset-free tracking problem for nonlinear systems described by a class of recurrent neural networks (RNNs). To compensate for constant disturbances and gu…
Km-scale dynamical downscaling through conformalized latent diffusion models
Alessandro Brusaferri, Andrea Ballarino
Dynamical downscaling is crucial for deriving high-resolution meteorological fields from coarse-scale simulations, enabling detailed analysis for critical applications such as weat…
From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting
Alessandro Brusaferri, Danial Ramin, Andrea Ballarino
While neural networks are achieving high predictive accuracy in multi-horizon probabilistic forecasting, understanding the underlying mechanisms that lead to feature-conditioned ou…
Recurrent neural network-based robust control systems with regional properties and application to MPC design
Daniele Ravasio, Alessio La Bella, Marcello Farina +1
This paper investigates the design of output-feedback schemes for systems described by a class of recurrent neural networks. We propose a procedure based on linear matrix inequalit…
NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shape
Alessandro Brusaferri, Danial Ramin, Andrea Ballarino
Forecasters using flexible neural networks (NN) in multi-horizon distributional regression setups often struggle to gain detailed insights into the underlying mechanisms that lead…
On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices
Alessandro Brusaferri, Andrea Ballarino, Luigi Grossi +1
Probabilistic electricity price forecasting (PEPF) is subject of increasing interest, following the demand for proper quantification of prediction uncertainty, to support the opera…
An optimal hierarchical control scheme for smart generation units: an application to combined steam and electricity generation
Stefano Spinelli, Marcello Farina, Andrea Ballarino
Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical…
A Hierarchical Architecture for Optimal Unit Commitment and Control of an Ensemble of Steam Generators
Stefano Spinelli, Marcello Farina, Andrea Ballarino
A hierarchical architecture for the optimal management of an ensemble of steam generators is presented. The subsystems are coordinated by a multilayer scheme for jointly sustaining…
A Hierarchical Architecture for the Coordination of an Ensemble of Steam Generators
Stefano Spinelli, Elia Longoni, Marcello Farina +3
This work presents a hierarchical architecture for the optimal management of an ensemble of steam generators, which needs to jointly sustain a common load. The coordination of inde…