Residential Household Energy Consumption prediction in France.
Using this Shiny App, one can model and predict the household yeary energy consumption in each administrative division called "département" agregated (level-2) by tuning several level-1 -indvidual- variables. The model is based on a random-intercept multilevel model using nlme package in R.
Is there a contextual effect influencing the residential household energy consumption in France? How much of the variation of the household energy consumption is due to the differences between the 81 french administrative division so- called “départements”?
Multilevel regression modelling introduces simultaneously two levels of aggregation in the model, and offers the possibility of extracting contextual effects from total variation of the household energy consumption. The remaining variation is explained by the model using relevant level-1 (households) explanatory variables. The random-intercept multilevel model that can be transposed with the following equation:
Where: 𝑌ij is the annual energy consumption of household i in geographical division j (DEP). 𝑋ij is the matrix of level-1 explanatory variables. 𝑍j is the matrix of level-2 explanatory variables. Other parameters in the equation need to be estimated. For fixed effects, 𝛾00 is the intercept for fixed effects, 𝛽k0 is the slope for level-1 explanatory variables, and 𝛽0q is the slope for level-2 explanatory variables. For random effects, 𝑒ij is the residual term at level-1 (households) and 𝑢0j is the residual term at level-2 (DEP). The variance of the residual error 𝑢0j is the variance of the intercepts between DEPs.
model <- lme(LOGCONSTOT ~ LOGHDDDEP + LOGREV+ AREA3G+ INSULHOUS+ YEARCONST+ ROOMNBR+ HEATSYST+ HEATSOURCE+ RURAL+ HEATTEMP+ ECS+ UNOCCWEEK+ PCS+ NBRPERS, random = ~ 1 | DEP, data = phebus)
LOGCONSTOT is the variable response indicating the logarithm of the energy consumption in 2012, given by each household interviewed during Phebus survey. This value is taking in account the energy used for space-heating, space cooling, and electrical appliance (source of data INSEE).
LOGREVDEP is a numerical variable indicating the logarithm of the average per capita disposable household income, per DEP in 2012 (source of data INSEE).
LOGHDDDEP is a numerical variable indicating logarithm of the heating degree days in 2012 for each department (source of data ADEME).
LOGREV is a numerical variable indicating the gross household income disposed in 2012, with a log transformation to reduce a right skewed distribution.
AREA3G is a categorical variable indicating the area of the housing, divided in three groups: 0-40m2 ;40m2-100m2;100m2 and above.
INSULHOUS is a binary indicator measuring whether the housing is insulated (=1) or adjoining to another housing unit (=0).
YEARCONST is a numerical variable indicating the year when the house was built.
ROOMNBR is a numerical variable indicating the number of bedrooms in the housing.
HEATSYST is a categorical variable indicating whether the space heating system is dedicated only for heating the housing, for heating a cluster of housing (collective space heating system), or a mixed system (individual and collective).
HEATSOURCE indicates the type of energy used for the space heating system. Three categories are defined: electricity ; gas ; other.
RURAL is a binary indicator showing if the housing is located in a rural area (i.e. less than 2000 hab). 1= Yes, 0 = No.
HEATTEMP is another binary variable indicating the heating temperature selected by the households to heat their housing is above 21°C (included). 1= Yes, 0= No.
ECS is a categorical variable indicating how is produced hot water. Three categories are defined: Electricity, boiler (using gas, fuel, or wood as energy source), and others.
UNOCCWEEK is a binary variable indicating whether the housing is unoccupied less than four hours during weekdays. 1= Yes, 0= No.
PCS is a categorical variable indicating the household employment status. Three categories are defined: Executive status, Middle-level status, and other status.
NBRPERS is a numerical variable indicating the number of persons living in housing.