The increasing integration of intermittent renewable generation, especially at the distribution level,necessitates advanced planning and optimisation methodologies contingent on the knowledge of thegrid, specifically the admittance matrix capturing the topology and line parameters of an electricnetwork. However, a reliable estimate of the admittance matrix may either be missing or quicklybecome obsolete for temporally varying grids. In this work, we propose a data-driven identificationmethod utilising voltage and current measurements collected from micro-PMUs. More precisely,we first present a maximum likelihood approach and then move towards a Bayesian framework,leveraging the principles of maximum a posteriori estimation. In contrast with most existing con-tributions, our approach not only factors in measurement noise on both voltage and current data,but is also capable of exploiting available a priori information such as sparsity patterns and knownline parameters. Simulations conducted on benchmark cases demonstrate that, compared to otheralgorithms, our method can achieve significantly greater accuracy.

Natural gas is the most important energy source in Italy: it fuels thermoelectric power plants, industrial facilities and domestic heating. Gas demand forecasting is a critical task for any energy provider as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of Italian daily residential gas demand is studied. Five predictors are developed and compared: Ridge Regression, Gaussian Process, k-Nearest Neighbour, Artificial Neural Network, and Torus Model. Preprocessing and feature selection are also discussed in detail. Concerning the prediction error, a theoretical bound on the best achievable root mean square error is worked out assuming ideal conditions, except for the inaccuracy of meteorological temperature forecasts, whose effects are properly propagated. The best predictors, namely the Artificial Neural Network and the Gaussian Process, achieve an RMSE which is twice the performance limit, suggesting that precise predictions of residential gas demand can be achieved at country level.

Forecasting natural gas demand is a key problem for energy providers, as it allows for efficient pipe reservation and power plant allocation, and enables effective price forecasting. We propose a study of Italian gas demand, with particular focus on industrial and thermoelectric components. To the best of our knowledge, this is the first work about these topics. After a preliminary discussion on the characteristics of gas demand, we apply several statistical learning models to perform day-ahead forecasting, including regularized linear models, random forest, support vector regression and neural networks. Moreover, we introduce four simple ensemble models and we compare their performance with the one of basic forecasters. The out-of-sample Mean Absolute Error (MAE) achieved on 2017 by our best ensemble model is 5.16 Millions of Standard Cubic Meters (MSCM), lower than 9.57 MSCM obtained by the predictions issued by SNAM, the Italian Transmission System Operator (TSO).