A Naive Bayesian classification model for determining peak energy demand in Ontario

Abstract

In the Canadian Province of Ontario, electricity consumers pay a surcharge for electricity called the Global Adjustment (GA). For large consumers, having the ability to predict the top 5 daily energy demand hours of the year, called 5 Coincident Peaks (5CPs), can save millions of dollars in GA costs, and help decrease peak energy usage. This paper presents a Naive Bayesian classification model for predicting the 5CPs. The model classifies hourly energy demand as being a 5CP hour or not. The model was tested using hourly energy demand for the province of Ontario over a 21 year period (1995–2015). Classifying a day as a 5CP hour containing day yielded a mean precision and recall of 0.49 (0.18) and 0.88 (0.23) (Standard deviation is in brackets), respectively. Targeting the 5CP hours to within three candidate hours of potential 5CP containing days yielded a mean precision and recall of 0.47 (0.19) and 0.83 (0.22), respectively.