Ndiaye and Gabriel [53] performed a conditional demand
analysis of 59 predictor variables to obtain a regression model with only 9 predictor variables
that gave a coefficient of determi- nation of 0.784. The model was developed to predict the
electricity consumption of housing units in Oshawa (Ontario, Canada). The 9 predictors are
number of occupants, house status (owned or rented), average number of weeks of vacation taken away
from the house each year, type of fuel for the pool heater, type of fuel for the heating system,
type of fuel for the domestic hot water heater, availability and type of an air conditioning
system, and number of air changes per hour at 50 Pa. The data was collected based on three
methods: survey, site audits, and audit of smart meters
information.
Ndiaye and Gabriel [53] performed a conditional demandanalysis of 59 predictor variables to obtain a regression model with only 9 predictor variables that gave a coefficient of determi- nation of 0.784. The model was developed to predict the electricity consumption of housing units in Oshawa (Ontario, Canada). The 9 predictors are number of occupants, house status (owned or rented), average number of weeks of vacation taken away from the house each year, type of fuel for the pool heater, type of fuel for the heating system, type of fuel for the domestic hot water heater, availability and type of an air conditioning system, and number of air changes per hour at 50 Pa. The data was collected based on three methods: survey, site audits, and audit of smart metersinformation.
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