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Found 5 papers, 1 papers with code
Bayesian Error-in-Variables Models for the Identification of Power Networks
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.
Identification of AC Networks via Online Learning
The increasing penetration of intermittent distributed energy resources in power networks calls for novel planning and control methodologies which hinge on detailed knowledge of the grid.
Ensembling methods for countrywide short term forecasting of gas demand
Gas demand is made of three components: Residential, Industrial, and Thermoelectric Gas Demand.
Forecasting residential gas demand: machine learning approaches and seasonal role of temperature forecasts
On the Italian data, it is shown that temperature forecast errors account for some 18% of the mean squared error of gas demand forecasts provided by ANN.
Fast calibration of two-factor models for energy option pricing
Energy companies need efficient procedures to perform market calibration of stochastic models for commodities.
Pricing of Securities
