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. "obsyear" # Years for which observations are calculated. This requires timepoints function. )), 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 temp1 <- EvalOutput(Ovariable("temp1", data = orbind(Now, New))) # All construction temp1 <- unkeep(temp1, 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 )) reno <- temp1 * renovationyear # reno is the building stock repeated for every potential renovation decade. reno@output$Age <- reno@output$Renovationyear - reno@output$Eventyear # Floor area of renovations in ten years. Combine with continuous index Age. reno <- continuousOps(reno, renovation, '*') * renovationShares * 10 temp2 <- orbind(temp1 * 1, reno * -1) # Equal amount stops being non-renovated. temp2$Renovation <- "None" temp2 <- orbind(temp2, reno) # Temp2: Construction + renovation in data.frame temp2$Renovation <- as.factor(temp2$Renovation) temp3 <- EvalOutput(Ovariable("buildings", data = temp2)) # Temp3: Like Temp2 but ovariable temp3 <- unkeep(temp3, sources = TRUE, prevresults = TRUE) temp3@output <- fillna(temp3@output, marginals = colnames(temp3@output)[temp3@marginal]) # temp4: Drop redundant time indices temp4 <- CollapseMarginal(temp3, cols = c("Constructed", "Renovationyear", "Age"), FUN = "sum") temp4@output$Eventyear <- as.numeric(levels(temp4@output$Eventyear)[temp4@output$Eventyear]) # tapply (and therefore oapply) changes continuous indices to factors! Must change back by hand. colnames(temp4@output)[colnames(temp4@output) == "City area"] <- "City.area" temp4@output <- temp4@output[!is.na(result(temp4)) , ] # Calculate cumulative events at timepoints defined by obsyear. out <- timepoints(temp4, obsyear = obsyear) return(out) } ) ### HeatingEnergy heatingEnergy <- Ovariable("heatingEnergy", dependencies = data.frame(Name = c("energyUse", "savingPotential", "buildings")), formula = function(...) { out <- energyUse * savingPotential * buildings return(out) } ) # Calculate the cumulative impact of the events on building stock to given years timepoints <- function(X, obsyear) { # Function timepoints takes an event list and turns that into existing crosscutting situations at # timepoints defined by years. # X must be an ovariable with index Eventyear. # obsyear must be a vector of years. out <- data.frame() for(i in obsyear) { out <- rbind(out, data.frame( Year = i, X@output[X@output$Eventyear <= i , ] )) } X@output <- out X@marginal <- c(TRUE, X@marginal) # Add Year to marginal X <- CollapseMarginal(X, cols = "Eventyear", FUN = "sum") X@output <- X@output[!is.na(result(X)) , ] 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