A kernelized inverse-optimization approach forecasts EV-fleet charging and discharging power and derives market bid curves, outperforming support vector and ridge regression on synthetic case studies.
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Inverse Optimization with Kernel Regression: Application to the Power Forecasting and Bidding of a Fleet of Electric Vehicles
A kernelized inverse-optimization approach forecasts EV-fleet charging and discharging power and derives market bid curves, outperforming support vector and ridge regression on synthetic case studies.