2 citations · 2 across the 6 of their papers we have counts for
7 papers
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…
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…
Probabilistic electric load forecasting through Bayesian Mixture Density Networks
Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli +1
Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. Neural networks are widely considered to…
Estimation of Switched Markov Polynomial NARX models
Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli
This work targets the identification of a class of models for hybrid dynamical systems characterized by nonlinear autoregressive exogenous (NARX) components, with finite-dimensiona…
Identification of Probability weighted ARX models with arbitrary domains
Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli
Hybrid system identification is a key tool to achieve reliable models of Cyber-Physical Systems from data. PieceWise Affine models guarantees universal approximation, local lineari…