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Battery energy storage systems can be used for peak demand reduction in powersystems, leading to significant economic benefits. Two practical challenges are1) accurately determining the peak load days and hours and 2) quantifying andreducing uncertainties associated with the forecast in probabilistic riskmeasures for dispatch decision-making. In this study, we develop a supervisedmachine learning approach to generate 1) the probability of the next operationday containing the peak hour of the month and 2) the probability of an hour tobe the peak hour of the day. Guidance is provided on the preparation andaugmentation of data as well as the selection of machine learning models anddecision-making thresholds. The proposed approach is applied to the Duke EnergyProgress system and successfully captures 69 peak days out of 72 testing monthswith a 3% exceedance probability threshold. On 90% of the peak days, the actualpeak hour is among the 2 hours with the highest probabilities.

Predicting Peak Day and Peak Hour of Electricity Demand with Ensemble Machine Learning