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Principal component analysis of the electricity consumption in residential dwellings
Abstract Data gathered from energy audits, phone surveys and smart meter readings are used to derive regression models of the electricity consumption of housing units in Oshawa (Ontario, Canada). The database used comprises 59 predictors, for 62 observations. To address the problem of multi-collinearities among the predictors and at the same time reduce the number of needed predictors, a methodology is developed based on the latent root regression technique of Hawkins . Contrary to other variable selection techniques such as the stepwise method, the technique used in this paper allows an easy identification of alternative subsets. Using this technique, a reduction of 85% in the number of predictors is obtained, leaving only nine of them in the final subset. These nine variables are the number of occupants, the house status (owned or rented), the number of weeks of vacation per year, the type of fuel used in the pool heater, the type of fuel used in the heating system, the type of fuel used in the domestic hot water heater, the existence or not of an air conditioning system, the type of air conditioning system, and the number of air changes per hour at 50Pa. A regression with these nine predictors leads to an R 2 of 0.79, with an adjusted R 2 of 0.75 and all regression coefficients statistically significant at the 95% confidence level.
Principal component analysis of the electricity consumption in residential dwellings
Abstract Data gathered from energy audits, phone surveys and smart meter readings are used to derive regression models of the electricity consumption of housing units in Oshawa (Ontario, Canada). The database used comprises 59 predictors, for 62 observations. To address the problem of multi-collinearities among the predictors and at the same time reduce the number of needed predictors, a methodology is developed based on the latent root regression technique of Hawkins . Contrary to other variable selection techniques such as the stepwise method, the technique used in this paper allows an easy identification of alternative subsets. Using this technique, a reduction of 85% in the number of predictors is obtained, leaving only nine of them in the final subset. These nine variables are the number of occupants, the house status (owned or rented), the number of weeks of vacation per year, the type of fuel used in the pool heater, the type of fuel used in the heating system, the type of fuel used in the domestic hot water heater, the existence or not of an air conditioning system, the type of air conditioning system, and the number of air changes per hour at 50Pa. A regression with these nine predictors leads to an R 2 of 0.79, with an adjusted R 2 of 0.75 and all regression coefficients statistically significant at the 95% confidence level.
Principal component analysis of the electricity consumption in residential dwellings
Ndiaye, Demba (author) / Gabriel, Kamiel (author)
Energy and Buildings ; 43 ; 446-453
2010-10-03
8 pages
Article (Journal)
Electronic Resource
English
<italic>ACSyst</italic> , presence or not of an air conditioning system , <italic>ACType</italic> , type of air conditioning system , <italic>AgChild1</italic> , age of the eldest among children still living in the house , <italic>AgeRange</italic> , age range of the head of household , CDM , Conservation and Demand Management , <italic>CeilArea</italic> , ceiling area , <italic>CompSoft</italic> , interest in using eventual computer software that could help save energy , <italic>DHWFuel</italic> , type of fuel for the domestic hot water heater , <italic>EastOVH</italic> , presence of windows overhangs at the east side of the house , <italic>ElecKFt2</italic> , annual electricity consumption per square foot of floor area , <italic>HeatType</italic> , type of fuel for the heating system , <italic>HomState</italic> , house status (owned or rented) , <italic>HSysAge</italic> , age of the heating system , <italic>HsysEffi</italic> , heating system efficiency , <italic>HSysType</italic> , heating system type , <italic>Incand</italic> , number of incandescent light bulbs used outside , <italic>LearnMor</italic> , interest in learning more about ways to save energy in the house , <italic>LIMIT</italic> , parameter , <italic>NbACH</italic> , number of air changes per hour at 50<hsp></hsp>Pa , <italic>NbNewApp</italic> , number of new major energy efficient appliances purchased recently , <italic>NbOccup</italic> , number of household occupants , <italic>NbWkVaca</italic> , number of weeks of vacation taken away from the house each year , <italic>NorthOVH</italic> , presence of windows overhangs at the north side of the house , OCE , Ontario Centre of Excellence for Energy, Canada , OPUC , Oshawa Power and Utilities Corporation, Oshawa, Canada , <italic>ParTime</italic> , number of occupants working part-time , PC , principal component , PCA , principal component analysis , <italic>PHeatrFl</italic> , type of fuel for the pool heater , <italic>RecUpgd</italic> , upgrades or renovations in the house over the last ten years , <italic>SouthOVH</italic> , presence of windows overhangs at the south side of the house , <italic>TWdArea</italic> , total window area , UOIT , University of Ontario Institute of Technology, Canada , <italic>WestOVH</italic> , presence of windows overhangs at the west side of the house , <italic>WlgSpend</italic> , amount willing to spend on an energy device that would help save energy , <italic>WlUvalue</italic> , effective <italic>U</italic>-value of the walls , Principal component analysis , Latent root regression , Subset selection , Electricity consumption , Residential , Socio-economic factors
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