Building model

This page is a knowledge crystal of subtype method. The page identifier is Op_en6289
Moderator:Jouni (see all)
Upload data {{#opasnet_base_link:Op_en6289}}

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 examples of model use, see Building stock in Kuopio and Climate change policies and health in Kuopio.

The model uses the following ovariables and their inputs:

• buildings (from the model):
  • stockBuildings (case-specific data from the user) e.g. Building stock in Kuopio
    • Measure: Amount of building stock (typically in floor-m2) at given timepoints.
    • Indices: Required indices: Time (time the building was built. If not known, present year can be used for all buildings.) Typical indices: City_area, Building (building type)
    • Missing: You must give either stockBuildings, heatingShares, and efficiencyShares or changeBuildings or both. For missing data, use 0.
  • heatingShares (case-specific data from the user)
    • Measures: Fractions of heating types. Should sum up to 1 within each group defined by optional indices.
    • Indices: Required indices: Heating. Typical indices: Time, Building
    • Missing: If no data, use 1 as a placeholder.
  • efficiencyShares (case-specific data from the user)
    • Measure: Fraction of energy efficiency types. Should sum up to 1 for each group defined by other indices.
    • Indices: Required indices: Efficiency. Typical indices: Time, Building.
    • Missing: If no data, use 1 as default.
  • changeBuildings (case-specific data from the user)
    • Measure: Construction or demolition rate as floor-m2 at given timepoints.
    • Indices: Required indices: Time, Efficiency, Heating. If both stockBuildings and changeBuildings are used, changeBuildings should have all indices in stockBuildings, heatingShares, and efficiencyShares. Typical indices: Building, City_area.
    • Missing: If the data is only in stockBuildings, use 0 here.
  • renovationShares (case-specific data from the user)
    • Measure: Fraction of renovation types when renovation is done. Should sum to 1 for each group defined by other indices.
    • Indices: Required indices: Renovation, Startyear. Startyear is the time when the renovation is done, and it must be different than the Time index. Typical indices:
    • If no data, use 1 as default.
  • renovationRate (case-specific data from user. You can also use fairly generic data from Buildging stock in Kuopio.)
    • Measure: Rate of renovation (fraction per time unit).
    • Indices: Required indices: Age (the time difference between construction and renovation, i.e. Startyear - Time for each building). Typical indices:
  • obstime (assessment-specific years of interest)
    • Measure: one-column data.frame about the years to be used in output. The column name must be the same as the index name in RenovationShares (typically Startyear). E.g. Climate change policies and health in Kuopio
• heatingEnergy (from the model):
  • buildings (from the model; see above).
  • energyUse (fairly generic data for a cultural and climatic area, e.g. from Energy use of buildings)
    • Measure: Energy consumption per floor area (kWh / m2 /a)
    • Indices: Required indices: - . Typical indices: Building, Heating.
    • Missing: if this data is missing, you can only calculate building stock but nothing further.
  • efficiencyRatio (fairly generic data for a cultural and climatic area, e.g. from Energy use of buildings)
    • Measure: Relative energy consumption compared with the efficiency group Old.
    • Indices: Required indices: Efficiency. Typical indices: Time, Building.
    • Missing: If no data, use 1 as default.
  • renovationRatio (fairly generic data for a cultural and climatic area, e.g. from Energy use of buildings)
    • Measure: Relative energy consumption compared with the Renovation location None.
    • Indices: Required indices: Renovation. Typical indices: Building.
    • Missing: If no data, use 1 as default.
• emissions (from the model) (emissions in mass per time):
  • heatingEnergy (from the model; see above)
  • fuelTypes SHOULD BE fuelShares? (fairly generic knowledge from e.g. Energy production in Kuopio####
    • Measure: Tells how much of fuel is used for a certain neating energy need.
    • Indices: Required indices: Fuel_type. Typical indices:
  • emissionFactors (generic information, but may be cultural differences. E.g. Emission factors for burning processes ##
    • Measure: emissions per unit of energy produced (g / J or similar unit)
    • Indices: Required indices: Exposure_agent. Typical indices: Emission_height.
• exposure (from the model) is in ug/m3 in ambient air average concentration:
  • emissions (from the model; see above) is in ton /a
    • Indices: Required indices: - . Typical indices: Time, City_area, Exposure_agent, Emission_height.
  • iF (generic data but depends on population density, emission height etc)
    • Measure: conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure
    • Indices: Required indices: Typical indices:
  • pop
    • Measure: Amount of population exposed.
    • Indices: Required indices: - . Typical indices: Time, Area

Rationale

Causal diagram of the building model.

Calculations

+ Show code- Hide code library(OpasnetUtils) ########## Calculate all building events (constructions, demolitions, renovations) buildings <- Ovariable("buildings", dependencies = data.frame(Name = c( "stockBuildings", "heatingShares", "efficiencyShares", "changeBuildings", "renovationShares", "renovationRate", "obstime" )), formula = function(...) { #### Calculate cb ie. those buildings that were built between Time-1 and Time. #### This has two parts: the stock (stockBuildings) is treated as net construction rate #### by assuming that after construction Time nothing is demolished. #### Then this is added to the direct construction data (changeBuildings). # ### This code can be used to change cumulative building stock data to construction rates of periods. # stockBuildings@output$Time <- as.numeric(as.character(stockBuildings@output$Time)) # years <- sort(unique(stockBuildings@output$Time)) # cb <- stockBuildings # cb@output$Time <- years[match(cb@output$Time, years) + 1] # Frame shift with time, years 2:n. # cb@output <- subset(cb@output, !is.na(Time)) # cb <- stockBuildings - cb # cb@output <- orbind(cb, subset(stockBuildings@output, Time = years[1])) # First year cb <- stockBuildings * heatingShares * efficiencyShares # Add buildings from stock and buildings from construction data cb <- orbind2(cb, changeBuildings) ########### Renovations of existing buildings. # Combine with (continuous) index Age. reno <- cb * renovationShares marginals <- colnames(reno@output)[reno@marginal] reno@output$Age <- as.numeric(as.character(reno@output$Startyear)) - # Startyear is the time of renovation as.numeric(as.character(reno@output$Time)) # Time is the time of construction reno@marginal <- colnames(reno@output) %in% marginals reno <- reno * renovationRate # continuousOps(reno, renovationRate, '*') FIX continuousOps before using reno <- oapply(reno, cols = c("Age", "Time"), FUN = sum) out <- reno * -1 # Equal amount stops being non-renovated. out@output$Renovation <- "None" out <- orbind2(out, reno) # Renovated buildings colnames(cb@output)[colnames(cb@output) == "Time"] <- "Startyear" out <- orbind2( out, cb * Ovariable( # Add non-renovated buildings output = data.frame(Renovation = "None", Result = 1), marginal = c(TRUE, FALSE) ) ) ######## NA in indices will ruin timepoints. Therefore must do fillna but with warning. b <- character() for(i in colnames(out@output)[out@marginal]) {if(any(is.na(out@output[[i]]))) b <- c(b, i)} if(length(b) > 0) { out@output <- fillna(out@output, b) warning("Missing values had to be filled by function fillna in indices: ", b, "\n") } out <- timepoints(out, obstime) # Accumulate over observation time. return(out) } ) ### HeatingEnergy heatingEnergy <- Ovariable("heatingEnergy", dependencies = data.frame(Name = c( "energyUse", "efficiencyRatio", "renovationRatio", "buildings", "buildingTypes" )), formula = function(...) { out <- buildings * buildingTypes * energyUse * efficiencyRatio * renovationRatio return(out) } ) ####### Calculate emissions emissions <- Ovariable("emissions", dependencies = data.frame( Name = c( "heatingEnergy", "fuelTypes", "emissionFactors" ) ), formula = function(...) { out <- oapply(heatingEnergy, cols = c("Building", "Efficiency"), FUN = sum) out <- out * fuelTypes * emissionFactors * 3.6 * 1E-9 # convert from kWh /a to MJ /a and mg to ton out <- unkeep(out * emissionLocations, sources = TRUE, prevresults = TRUE) out@output$Emission_site <- as.factor(ifelse( out@output$Emission_site == "At site of consumption", as.character(out@output$City_area), as.character(out@output$Emission_site) )) out <- oapply( out, cols = c("Heating", "Burner", "Fuel", "City_area"), FUN = sum ) } ) #"Emission_site", "Emission_height", exposure <- Ovariable("exposure", # emis is in ton /a # iF = conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure concentration dependencies = data.frame(Name = c("emis2", "iF", "pop")), formula = function(...) { BR <- 20 # Nominal breathing rate (m^3 /d) BR <- BR / 24 / 3600 # m^3 /s out <- 1E+12 / 365 / 24 / 3600 # Emission scaling from ton /a to ug /s. out <- (emis2 * out) * iF / BR / pop # the actual equation out <- unkeep(out, prevresults = TRUE, sources = TRUE) return(out) } ) objects.store(buildings, heatingEnergy, emissions, exposure) cat("Saved ovariables buildings, heatingEnergy, emissions, exposure\n")

Dependencies

• Exposure to PM2.5 in Finland
• OpasnetUtils/Drafts
• Energy use of buildings
• Building stock in Kuopio

See also

• Building stock in Kuopio
• Building stock in Europe
• Building data availability in Kuopio
• Energy balance
• Greenhouse gas emissions in Kuopio
• heande:File:2013 10 29 - Exposure-Response Functions.xlsx
• heande:Health impact assessment framework
• heande:File:2013 10 29 WP6.2 Agreed ER.doc
• heande:Urgenche: Workpackage Exposure, Health, and Well-being
• heande:File:WP6 Deliverable 6 1.doc
• Land use 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