Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Markets

arXiv:2403.20149v1 Announce Type: new
Abstract: 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, …

abstract arxiv construct cs.lg cs.sy decision eess.sy electricity forecasting machine machine learning machine learning models making markets paper power prediction predictions stat.ml stochastic studies type variants