Buildings in Basel: Difference between revisions

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=== Calculations ===
=== Calculations ===


<rcode name="initiate" label="Initiate objects (developers only)"
This code creates ovariables that are needed to run the [[Building model]] and its ovariables buildings and heatingEnergy.
<rcode name="initiate" label="Initiate objects (developers only)" embed=1>
library(OpasnetUtils)
library(OpasnetUtils)


objects.latest("Op_en5932", code_name = "initiate")  
# [[Buildings in Basel]], building stock, locations by postal codes (in A Swiss coordinate system)
# [[Baseline building stock]] from Kuopio. Replace those that do not apply.
buildingStock <- Ovariable("buildingStock", ddata = "Op_en7044.postal_code_areas")


buildingStock <- Ovariable("buildingStock", ddata = "Op_en7044.postal_code_areas")
# Geolocations of the buildings for emission calculations.
emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7044.locations_of_postal_codes")


levels(energyUse@data$Heating)[levels(energyUse@data$Heating) == ""] <- NA
####### DEFINE DUMMIES FOR MODEL PROPERTIES THAT ARE NOT NEEDED FOR BASEL
heatingShares <- 1 # Heating types of current buildings. Exists as part of buildingStock


# Energy efficiencies of current buildings
efficiencies <- Ovariable("efficiencies", data = data.frame(Efficiency = "New", Result = 1))
construction <- Ovariable("construction", data = data.frame(
construction <- Ovariable("construction", data = data.frame(
Postal.code = 4051,
Postal.code = 4051,
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))
))


# Construction rate in the future. Only current time in Basel.
buildingTypes <- 1 # A dummy variable to combine two different indices: Building and Building2
constructionAreas <- 1 #Places where construction takes place.
constructionAreas <- 1 #Places where construction takes place.
efficienciesNew <- 0 # Energy efficiencies in the future
efficiencies <- Ovariable("efficiencies", data = data.frame(Efficiency = "New", Result = 1)) # Current efficiencies
buildingTypes <- 1 # A dummy variable to combine two different indices: Building and Building2
efficienciesNew <- 0 # Energy efficiencies in the future
heatingSharesNew <- 0 # Heating types of the buildings in the future
eventyear <- 1 # A dummy variable to combine time periods to numerical time axis.
renovationShares <- 0 # Fraction of renovation type when renovation is done. Renovations not considered.
heatingShares <- 1 # Heating types of current buildings. Exists as part of buildingStock
 
heatingSharesNew <- 0 # Heating types of the buildings in the future
# A dummy variable to combine time periods to numerical time axis.
renovation <- 0 # Percentage of renovations per year
eventyear <- 1
renovationShares <- 0 # Fraction of renovation type when renovation is done. Renovations not considered.
 
################################
 
 
 
 
 
 
 
#colnames(emis@output)[colnames(emis@output) == "Emission site"] <- "Emission.site"
#colnames(emis@output)[colnames(emis@output) == "Emission height"] <- "Emission.height"
#emis@output$Emission.site <- ifelse(
# emis@output$Emission.site == "At site of consumption",
# levels(emis@output$City.area)[emis@output$City.area],
# levels(emis@output$Emission.site)[emis@output$Emission.site]
#)
 
###### Calculate health impacts
 
#bg.mort <- 45182 / 5203826 # same values as used in PILTTI unit: deaths per person-year
 
#erf <- Ovariable("erf", data = data.frame(Pollutant = "PM2.5", Result = 0.0097))
# J. T. Tuomisto, A. Wilson, et al. Uncertainty in mortality response to airborne fine particulate matter... 2008
# unit: m^3 /ug
 
#health <- (emis * 1E+6 /365 / 24 / 3600) * iF / (20 / 24 / 3600) * (erf * 1E+6) * bg.mort
 
# emis is in ton /a
# iF = conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) => conc = iF * emis / pop / BR
# HIA = conc * erf * pop * backgroundrisk = emis (g /s) * iF / BR (m3 /s) * erf (m3 /g) * backgroundrisk
 
 
### Impact graphs
 
BS <- 18
 
ggplot(buildings@output) + geom_bar() + theme_gray(base_size = BS) +
aes(x = Building, weight = buildingsResult/1000000, fill = Heating) + labs(y = "Floor area (M m2)", title = "Building impacts of renovation policy") + coord_flip() # + facet_grid(. ~ RenovationPolicy)
 
ggplot(heatingEnergy@output) + geom_bar() + theme_gray(base_size = BS) + labs(title = "Energy impacts of renovation policy") +
aes(x = Building, weight = heatingEnergyResult/1E+6, fill = Heating) + labs(y = "Heating energy need (GWh /a)") + coord_flip() #  + facet_grid(. ~ RenovationPolicy)
 
#emis@output <- emis@output[emis@output$Renovation == "BAU" , ]


ggplot(emis@output) + geom_bar() + facet_grid(Pollutant ~ . , scales = "free_y") + theme_gray(base_size = BS) + labs(title = "Emission impacts of biofuel policy") +
# Ovariables energyUse and savingPotential must be taken from [[Energy use of buildings]].
aes(x = Heating, weight = Result, fill = Fuel) + labs(y = "Emissions to air (ton /a)") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
# obsyear, # Years for which observations are calculated. Must be given in an assessment.


#ggplot(health@output) + geom_bar() + theme_gray(base_size = BS) + labs(title = "Health impacts of fuel and renovation policy") +
objects.store(
#aes(x = Year, weight = Result, fill = Heating) + labs(y = "Premature deaths (# /a)") # + facet_grid(FuelPolicy ~ RenovationPolicy)
buildingStock, # Current building stock
buildingTypes, # A dummy variable to combine two different indices: Building and Building2
construction, # Construction rate in the future
efficiencies, # Energy efficiencies of current buildings
efficienciesNew, # Energy efficiencies in the future
emissionLocations, # Locations of buildings
eventyear, # A dummy variable to combine time periods to numerical time axis.
heatingShares, # Heating types of current buildings
heatingSharesNew, # Heating types of the buildings in the future
renovation, # Percentage of renovations per year
renovationShares # Fraction of renovation type when renovation is done.
)


cat("Objects
buildingStock, # Current building stock
buildingTypes, # A dummy variable to combine two different indices: Building and Building2
construction, # Construction rate in the future
efficiencies, # Energy efficiencies of current buildings
efficienciesNew, # Energy efficiencies in the future
emissionLocations, # Locations of buildings
eventyear, # A dummy variable to combine time periods to numerical time axis.
heatingShares, # Heating types of current buildings
heatingSharesNew, # Heating types of the buildings in the future
renovation, # Percentage of renovations per year
renovationShares # Fraction of renovation type when renovation is done.
stored.\n")


</rcode>
</rcode>

Revision as of 06:28, 12 February 2015



Question

What is the building stock in Basel?

Answer

Rationale

Detailed data can be found from heande:Buildings in Basel. This data is aggregated to the postal code level. The table uses Swiss Land Survey coordinates CH1903. [1] [2] Use this code in the ova2spat function:

proj4string = "+init=epsg:21781"
Locations of postal codes(epsg:21781)
ObsPostal codeXY
14001611296.55267316.04
24051611004.37267137.18
34052612735.06266596.60
44053611695.00265635.41
54054609694.31266598.91
64055609798.12267812.13
74056610271.78268500.37
84057611580.35268976.81
94058612781.70268225.32
104059611615.85264689.04
114125615721.84269737.40
124126617160.01268852.20

Calculations

This code creates ovariables that are needed to run the Building model and its ovariables buildings and heatingEnergy.

+ Show code

See also

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


References


Related files