Building model

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Question

How to estimate the size of the building stock of a city, including heating properties, renovations etc? The situation is followed over time, and different policies can be implemented.

Answer

For an example, see Baseline building stock.

Rationale

Calculations

+ Show code- Hide code library(OpasnetUtils) #########3# Calculate all building events (constructions, demolitions, renovations) buildings <- Ovariable("buildings", dependencies = data.frame(Name = c( "buildingStock", # Current building stock "heatingShares", # Heating types of current buildings "efficiencies", # Energy efficiencies of current buildings "construction", # Construction rate in the future "efficienciesNew", # Energy efficiencies in the future "buildingTypes", # A dummy variable to combine two different indices: Building and Building2 "heatingSharesNew", # Heating types of the buildings in the future "renovationShares", # Fraction of renovation type when renovation is done. "renovation", # Percentage of renovations per year "eventyear" # A dummy variable to combine time periods to numerical time axis. )), formula = function(...) { # Current building stock Now <- buildingStock * heatingShares * efficiencies * eventyear # Construction in ten years multiplied by fraction of energy classes and heating types. New <- construction * 10 * efficienciesNew * buildingTypes * heatingSharesNew * eventyear out <- EvalOutput(Ovariable("out", data = orbind(Now, New))) out <- unkeep(out, cols = "Building2", sources = TRUE, prevresults = TRUE) # All source indices can be unkept, because each has only one location. renovationyear <- Ovariable("renovationyear", data = data.frame( Renovationyear = c(2010, 2020, 2030, 2040, 2050), Age = NA, Result = 1 )) temp <- out * renovationyear # temp is the building stock repeated for every potential renovation decade. temp@output$Age <- temp@output$Renovationyear - temp@output$Eventyear # Floor area of renovations in ten years. Combine with continuous index Age. renovations <- continuousOps(temp, renovation, '*') * renovationShares * 10 marginals <- colnames(renovations@output)[renovations@marginal] out <- orbind(out * 1, renovations * -1) # Equal amount stops being non-renovated. out$Renovation <- "None" out <- orbind(out, renovations) out$Renovation <- as.factor(out$Renovation) out <- as.data.frame(as.table(tapply( # Remove indices that are no longer needed out$Result, INDEX = out[marginals[!marginals %in% c("Constructed", "Renovationyear", "Age")]], FUN = "sum", na.rm = TRUE ))) out$Eventyear <- as.numeric(levels(out$Eventyear)[out$Eventyear]) # tapply (and therefore oapply) changes continuous indices to factors! Must change back by hand. colnames(out)[colnames(out) == "City area"] <- "City.area" colnames(out)[colnames(out) == "Freq"] <- "Result" out <- out[!is.na(out$Result) , ] return(out) } ) ### HeatingEnergy heatingEnergy <- Ovariable("heatingEnergy", dependencies = data.frame(Name = c("energyUse", "savingPotential", "buildings")), formula = function(...) { out <- energyUse * savingPotential * buildings return(out) } ) # Calculate the cumulative impact of the events on building stock to given years timepoints <- function(X, obstimes) { # Function timepoints takes an event list and turns that into existing crosscutting situations at # timepoints defined by years. # X must be an ovariable with index Year. # obstimes must be a vector of years. out <- data.frame() for(i in obstimes) { out <- rbind(out, data.frame( Obstime = i, X@output[X@output$Year <= i , ] )) } X@output <- out X@marginal <- c(TRUE, X@marginal) # Add Obstime to marginal return(X) } objects.store(buildings, heatingEnergy, timepoints) cat("Saved ovariables buildings, heatingEnergy, timepoints\n")

See also

• Baseline building stock
• Building stock in Europe
• Building data availability in Kuopio

Urgenche research project 2011 - 2014: city-level climate change mitigation

Urgenche pages Urgenche main page · Category:Urgenche · Urgenche project page (password-protected) Relevant data Building stock data in Urgenche‎ · Building regulations in Finland · Concentration-response to PM2.5 · Emission factors for burning processes · ERF of indoor dampness on respiratory health effects · ERF of several environmental pollutions · General criteria for land use · Indoor environment quality (IEQ) factors · Intake fractions of PM · Land use in Urgenche · Land use and boundary in Urgenche · Energy use of buildings Relevant methods Building model · Energy balance · Health impact assessment · Opasnet map · Help:Drawing graphs · OpasnetUtils‎ · Recommended R functions‎ · Using summary tables‎ City Kuopio Climate change policies and health in Kuopio (assessment) · Climate change policies in Kuopio (plausible city-level climate policies) · Health impacts of energy consumption in Kuopio · Building stock in Kuopio · Cost curves for energy (prioritization of options) · Energy balance in Kuopio (energy data) · Energy consumption and GHG emissions in Kuopio by sector · Energy consumption classes (categorisation) · Energy consumption of heating of buildings in Kuopio · Energy transformations (energy production and use processes) · Fuels used by Haapaniemi energy plant · Greenhouse gas emissions in Kuopio · Haapaniemi energy plant in Kuopio · Land use in Kuopio · Building data availability in Kuopio · Password-protected pages: File:Heat use in Kuopio.csv · Kuopio housing City Basel Buildings in Basel (password-protected) Energy balances Energy balance in Basel · Energy balance in Kuopio · Energy balance in Stuttgart · Energy balance in Suzhou