PhD candidate Jinxiang Liu, Computer Science, will present his PhD Defense on Monday, April 12, 2021, from 1:00 to 3:00 p.m.
The title of Liu’s dissertation is, “Prediction of Coincident Peak Days in Electricity System: A Case Study for Classification on Imbalanced Data.”
To guarantee sufficient electricity supply for its highest demands, many regional organizations surcharge their customers during coincident peaks (CPs), a time of highest demand across the system or region of interest. Therefore, the accurate prediction of these coincident peaks would be helpful not only for companies to ensure sufficient generation is available, but also for customers who may try to avoid electricity consumption and consequent additional cost.
This dissertation focuses on the prediction of the top five coincident peak days (5CPs) in a year. We used classification models to solve this imbalanced prediction problem (around 1.3\% for positive cases) by classifying the next day as 5CP days or non-5CP days.
We analyze six sets of actual historical data from different regions of Canada and the United States. We explore the effect of forecast accuracy on 5CP days prediction through four cases: I – knowing tomorrow’s power demand and weather condition exactly (an oracle), II & III – knowing some information about tomorrow (an oracle + increasing noise), and IV – no knowledge of future.
We proposed a three-phase model to predict 5CP days: first, clustering is applied to filter some negative cases, second, an all convolutional neural network that estimates the probability of being a 5CP day for the remaining cases is learned, and third, an adaptive method is used determines thresholds.
This three-phase model exhibits promising performances with the highest mean recall of 1.00, mean precision of 0.56, and mean F1 score of 0.72. Finally, we explored the use of a few-short learning framework to this problem. A triplet network is implemented for the 2-way-5-shot classifications. The prediction results have the highest mean recall of 1.00, mean precision of 0.67, and mean F1 score of 0.79.