Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning. (arXiv:2205.08698v1 [stat.AP])

Prediction intervals offer an effective tool for quantifying the uncertainty
of loads in distribution systems. The traditional central PIs cannot adapt well
to skewed distributions, and their offline training fashion is vulnerable to
unforeseen changes in future load patterns. Therefore, we propose an optimal PI
estimation approach, which is online and adaptive to different data
distributions by adaptively determining symmetric or asymmetric probability
proportion pairs for quantiles. It relies on the online learning ability of
reinforcement learning to integrate the …

arxiv distribution forecasting learning prediction reinforcement reinforcement learning systems