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AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models
To tackle the problem of missing information about the PV mounting configuration, we use new data that become available during operation to adapt the ensemble weights to minimize the forecasting error.
Concepts for Automated Machine Learning in Smart Grid Applications
Forecasts are elementary for sector coupling, where energy-consuming sectors are interconnected with the power-generating sector to address electricity storage challenges by adding flexibility to the power system.
Data-driven system analysis of nonlinear systems using polynomial approximation
To tackle these drawbacks, we establish a polynomial representation of nonlinear functions based on a polynomial sector by Taylor's theorem and a set-membership for Taylor polynomials.
Data-driven inference on optimal input-output properties of polynomial systems with focus on nonlinearity measures
In the context of dynamical systems, nonlinearity measures quantify the strength of nonlinearity by means of the distance of their input-output behaviour to a set of linear input-output mappings.
