  Battery energy storage systems can be used for peak demand reduction in power
systems, leading to significant economic benefits. Two practical challenges are
1) accurately determining the peak load days and hours and 2) quantifying and
reducing uncertainties associated with the forecast in probabilistic risk
measures for dispatch decision-making. In this study, we develop a supervised
machine learning approach to generate 1) the probability of the next operation
day containing the peak hour of the month and 2) the probability of an hour to
be the peak hour of the day. Guidance is provided on the preparation and
augmentation of data as well as the selection of machine learning models and
decision-making thresholds. The proposed approach is applied to the Duke Energy
Progress system and successfully captures 69 peak days out of 72 testing months
with a 3% exceedance probability threshold. On 90% of the peak days, the actual
peak hour is among the 2 hours with the highest probabilities.
