Conformal Prediction for Stochastic Decision-Making of PV Power in
  Electricity Markets
Yvet Renkema Nico Brinkel Tarek Alskaif
This paper studies the use of conformal prediction (CP), an emerging
probabilistic forecasting method, for day-ahead photovoltaic power predictions
to enhance participation in electricity markets. First, machine learning models
are used to construct point predictions. Thereafter, several variants of CP are
implemented to quantify the uncertainty of those predictions by creating CP
intervals and cumulative distribution functions. Optimal quantity bids for the
electricity market are estimated using several bidding strategies under
uncertainty, namely: trust-the-forecast, worst-case, Newsvendor and expected
utility maximization (EUM). Results show that CP in combination with k-nearest
neighbors and/or Mondrian binning outperforms its corresponding linear quantile
regressors. Using CP in combination with certain bidding strategies can yield
high profit with minimal energy imbalance. In concrete, using conformal
predictive systems with k-nearest neighbors and Mondrian binning after random
forest regression yields the best profit and imbalance regardless of the
decision-making strategy. Combining this uncertainty quantification method with
the EUM strategy with conditional value at risk (CVaR) can yield up to 93\% of
the potential profit with minimal energy imbalance.