Building model: Difference between revisions

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<noinclude>
[[Category:Energy]]
[[Category:Energy]]
[[Category:Buildings]]
[[Category:Buildings]]
{{method|moderator=Jouni}}
{{method|moderator=Jouni}}
</noinclude>


==Question==
==Question==
Line 9: Line 11:
==Answer==
==Answer==


For examples of model use, see [[Building stock in Kuopio]] and [[Climate change policies and health in Kuopio]].
[[image:Building model causal diagram.png|thumb|400px|Causal diagram of the [[building model]]. The actual model is up to the yellow node Building stock, and the rest is an example how the result can be used in models downstream.]]


==Rationale==
The building model follows the development of a city's or area's building stock over time. The output of the model is the floor area (or volume, depending on the input data) of the building stock of a city at specified timepoints, classified by energy efficiency, heating type, and optionally by other case-specific characteristics. The model functions as part of Opasnet's modeling environment and it is coded using R. It uses specific R objects called [[ovariable]]s. The model can also be downloaded and run on one's own computer.


[[image:Building model causal diagram.png|thumb|400px|Causal diagram of the [[building model]].]]
The model is given data about the building stock of a certain city or area during a certain period of time. The data can be described with very different levels of precision depending on the situation and what kind of information is needed. Some kind of data on the energy efficiency and heating type is necessary, but even rough estimates suffice. Then again, if there is sufficient data, the model can analyse even individual buildings.  


===Calculations===
In addition to that, the model can describe changes in the building stock, i.e. construction of new buildings and demolishing
of old ones. Data on the heating- and energy efficiencies of new and demolished buildings is required at the same level of precision as that of other buildings. This data is used to calculate how construction and demolishing change the building stock's size and heating types.


<rcode name='initiate' label='Initiate variables' embed=1>
The model takes into account the energy renovation of existing buildings. They are analysed using two variables:
library(OpasnetUtils)
firstly, what fraction of the building stock is energy renovated yearly and secondly, what type of renovation it is.
This information, too, can be rough or precise and detailed. It can describe the whole building stock with a single number or be
specific data on the time, the building's age, use or other background information.


########## Calculate all building events (constructions, demolitions, renovations)
:''For examples of model use, see [[Helsinki energy decision 2015]], [[Building stock in Kuopio]] and [[Climate change policies and health in Kuopio]].


buildings <- Ovariable("buildings",
The overall equation in the model is this:
dependencies = data.frame(Name = c(
"stockBuildings", # Building stock given as an amount at given timepoints
"heatingShares", # Heating types of current buildings
"efficiencyShares", # Energy efficiency types of current buildings
"changeBuildings", # Construction or demolition rate as floor-m2. Indices Efficiency and
# Heating should be included here, if used.
# "buildingTypes", # A dummy variable to combine two different indices: Building and Building2
"renovationShares", # Fraction of renovation type when renovation is done.
"rateBuildings", # Percentage of renovations and other relative changes per year
"year" # A data.frame of years for which observations are calculated. This requires timepoints function.
)),
formula = function(...) {
# ######## Current building stock
# stock <- stockBuildings * heatingShares * efficiencyShares


<math>B_{t,h,e,r} = \int\int (Bs_{c,t,a} Hs_h Es_e + Bc_{c,h,e,t,a}) Rr_a Rs_{r,t} O)\mathrm{d}c \mathrm{d}a</math>


# #### Changes in building stock due to construction etc.
* B = buildings, floor area of buildings in specified groups
* Bs = stockBuildings, floor area of the current buildings
# change <- timepoints(changeBuildings, obsyear, sumtimecol = FALSE) # Cumulative changes at certain timepoints.
* Bc = changeBuildings, floor area of constructed and demolished (as negative areas) buildings
* Hs = heatingShares, fractions of different heating types in a group of buildings
* Es = efficiencyShares, fractions of different efficiency classes in a group of buildings
* Rr = renovationRate, fraction of buildings renovated per year
* Rs = renovationShares, fractions of different renovation types performed when buildings are renovated
* O = obstime, timepoints for which the building stock is calculated.
* Indices required (also other indices are possible)
** t = Obsyear, time of observation. This is renamed Time on the output data.
** c = Construction year (the index is named 'Time' in the input data), time when the building was built.
** a = Age, age of building at a timepoint. This is calculated as a = t - b.
** h = Heating, primary heating type of a building
** e = Efficiency, efficiency class of building when built
** r = Renovation, type of renovation done to a non-renovated building (currently, you can only renovate a building once)


#### Calculate cb ie. those buildings that were built between Time-1 and Time.
The model is iterative across the Obsyear index so that renovations performed at one timepoint are inherited to the next timepoint, and that situation is the starting point for renovations in that timepoint.
#### 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.
==Rationale==
# 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
=== Inputs and calculations ===
# colnames(cb@output)[colnames(cb@output) == "Time"] <- "Startyear"
# Add buildings from stock and buildings from construction data


cb <- orbind2(cb, changeBuildings)
{| {{prettytable}}
|+'''Variables in the building model
! Variable || Measure || Indices || Missing data
|----
| stockBuildings (case-specific data from the user) e.g. [[Building stock in Helsinki]] or [[Building stock in Kuopio]]
| Amount of building stock (typically in floor-m2) at given timepoints.
| 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)
| You must give either stockBuildings, heatingShares, and efficiencyShares or changeBuildings or both. For missing data, use 0.
|----
| heatingShares (case-specific data from the user)
| Fractions of heating types. Should sum up to 1 within each group defined by optional indices.
| Required indices: Heating. Typical indices: Time, Building
| If no data, use 1 as a placeholder.
|----
| efficiencyShares (case-specific data from the user)
| Fraction of energy efficiency types. Should sum up to 1 for each group defined by other indices.
| Required indices: Efficiency. Typical indices: Time, Building.
| If no data, use 1 as default.
|----
| changeBuildings (case-specific data from the user)
| Construction or demolition rate as floor-m2 at given timepoints.
| Required indices: Obsyear, 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.
| If the data is only in stockBuildings, use 0 here.
|----
| renovationShares (case-specific data from the user)
| Fraction of renovation types when renovation is done. Should sum to 1 for each group defined by other indices.
| Required indices: Renovation, Obsyear. Obsyear is the time when the renovation is done
| If no data, use 1 as default.
|----
| renovationRate (case-specific data from user. You can also use fairly generic data from  [[Building stock in Helsinki]] or [[Building stock in Kuopio]].)
| Rate of renovation (fraction per time unit).
| Required indices: Age (the time difference between construction and renovation, i.e. Obsyear - Time for each building).
| If no data, use 0.
|----
| obstime (assessment-specific years of interest)
| The years to be used in output. The only index Obsyear contains the years to look at; Result is 1.
| Required indices: Obsyear. Typical indices: other indices are not allowed.
| -
|----
|}


This code defines the generic building model object called ''buildings''. Other objects needed can be found from case-specific pages, see table above.
########### Renovations of existing buildings. # Combine with (continuous) index Age.


reno <- cb * renovationShares
<rcode name='buildingstest' label='Initiate buildings (only for developers)' embed=1 store=1>
marginals <- colnames(reno@output)[reno@marginal]
### This code is Op_en6289/buildingstest on page [[Building model]].
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@output <- reno@output[result(reno) != 0 , ] # Zeros are meaningless
library(OpasnetUtils)
reno <- reno * renovationRate # continuousOps(reno, renovationRate, '*') FIX continuousOps before using
# reno@output$Startyear <- reno@output$Time # Renovation is the new event.
reno <- oapply(reno, cols = c("Age", "Time"), FUN = sum)
# reno <- timepoints(reno, obstime) # Accumulate over observation time.


out <- reno * -1 # Equal amount stops being non-renovated.
########## Calculate all building events (constructions, demolitions, renovations)
out@output$Renovation <- "None"
# out@output$Renovation <- as.factor(out@output$Renovation)
out <- orbind2(out, reno) # Temp2: Construction + renovation in data.frame


for(i in colnames(reno@output)[reno@marginal]) {
buildings <- Ovariable("buildings",  
out[[i]][is.na(out[[i]])] <- "Not known"
}
 
return(out)
}
)
 
### HeatingEnergy
 
heatingEnergy <- Ovariable("heatingEnergy",  
dependencies = data.frame(Name = c(
dependencies = data.frame(Name = c(
"energyUse", # Energy consumption per floor area
"stockBuildings",
"efficiencyRatio", # Relative energy consumption compared with the efficiency group Old.
"changeBuildings",
"renovationRatio", # Relative energy consumption compared with the Renovation
"heatingShares", # This can be indexed by building year (Time) or Observation year (Obsyear)
"buildings",
"efficiencyShares", # Same here
"buildingTypes"
"renovationShares",
"renovationRate", # Fraction of buildings renovated between timepoints
"demolitionRate", # Fraction of buildings demolished between timepoints
#"heatTypeConversion", # Is dependent on buil, which defined in the formula (must handle withihn formula). Fraction of heating types converted to other
"obstime" # In the new version obstime is ovariable with column Obsyear.
)),
)),
formula = function(...) {
formula = function(...) {
out <- buildings * buildingTypes * energyUse * efficiencyRatio * renovationRatio
stock <- stockBuildings * obstime
if( ! "Efficiency" %in% colnames(stock@output)) { # Add those shares that are missing
stock <- stock * efficiencyShares
}
if( ! "Heating" %in% colnames(stock@output)) {
stock <- stock * heatingShares
}
stock2 <- changeBuildings * obstime
if( ! "Efficiency" %in% colnames(stock2@output)) { # Add those shares that are missing
stock2 <- stock2 * efficiencyShares
}
if( ! "Heating" %in% colnames(stock2@output)) {
stock2 <- stock2 * heatingShares
}
return(out)
stock2@output$Time <- as.factor(stock2@output$Time) # Also stock@output$Time is factor
}
buil <- OpasnetUtils::combine(stock, stock2) # Buildings in the whole timeline
)
buil <- unkeep(buil, sources = TRUE)
buil@output$Age <- as.numeric(as.character(buil@output$Obsyear)) - as.numeric(as.character(buil@output$Time))
buil@marginal <- c(buil@marginal, TRUE)
buil@output <- buil@output[buil@output$Age >= 0 , ]
# Note that if stockBuildings and changeBuildings have different marginals (typically policies),
# there will be NA in these indices. This will be corrected below.
for(i in colnames(buil@output)[buil@marginal]) {
if(any(is.na(buil@output[[i]]))) {
buil@output <- fillna(buil@output, i)
print(paste("Column", i, "treated with fillna (difference between stockBuildings and changeBuildings)."))
}
}
# heatTypeConversion dependent on buil, which defined above, and hence must be handled here.
# Scope is messed up due to the dependency and the following produces error when run within EvalOutput of buildings
#objects.latest("Op_en7115", code_name = "heatTypeConversion")
#heatTypeConversion <- EvalOutput(heatTypeConversion)
heatTypeConversion <- function(){
dat <- opbase.data("Op_en7115", subset = "Yearly_heating_converted_factor")
colnames(dat)[colnames(dat) == "Time"] <- "Obsyear"
dat$Obs <- NULL
out <- data.frame()
temp <- unique(dat[c("Heating_from", "Heating_to")])
for (i in 1:nrow(temp)) {
onetype <- merge(temp[i,], dat)
tempout <- merge(obstime@output, onetype, all.x = TRUE)[c("Obsyear","Result")]
tempout <- merge(tempout, temp[i,])
for (j in (1:nrow(tempout))[is.na(tempout$Result)]) {
a <- onetype$Obsyear[which.min(abs(as.numeric(as.character(onetype$Obsyear)) - as.numeric(as.character(obstime$Obsyear[j]))))]
tempout$Result[j] <- onetype$Result[a]
}
out <- rbind(out, tempout)
}
out <- Ovariable(output = out, marginal = colnames(out) != "Result")
colnames(out@output)[colnames(out@output) == "Heating_from"] <- "Heating"
out <- buil * out
out1 <- out
out1$Result <- - out1$Result
out1$Heating_to <- NULL
out$Heating <- out$Heating_to
out$Heating_to <- NULL
out@output <- rbind(out1@output, out@output)
heatTypeConversion <- out
return(out)
}
buil <- OpasnetUtils::combine(buil, heatTypeConversion())
temp1 <- merge(renovationRate, unique(buil@output["Age"])) # Avoid redundant calculations
temp1@name <- renovationRate@name
temp2 <- renovationShares
temp2@output <- temp2@output[temp2@output$Renovation == "None" , ]
renovate <- (1 - temp1) * (1 - temp2) # Assumes that data has row for Renovation: None = 0
renovate <- OpasnetUtils::combine(renovate, temp1 * renovationShares)
out <- data.frame()
prevreno <- unkeep(buil * renovate * 0, sources = TRUE) # Previously renovated buildings for the first time point
prevreno@output <- prevreno@output[prevreno@output$Renovation != "None" , ][1,] # Just take one renovated example row
marginals <- colnames(prevreno@output)[prevreno@marginal]
demolitionRate <- unkeep(demolitionRate, sources = TRUE) # Would otherwise cause trouble in rbind.
for(i in obstime@output$Obsyear) { # Accumulate the building stock
# Take the building stock
temp <- buil
temp@output <- temp@output[temp@output$Obsyear == i , ] # Take buildings of year i
prevreno@output$Obsyear <- i # update the observation year of the previous renovation


####### Calculate emissions
# Remove from the stock buildings that are demolished
temp <- temp * (1 - demolitionRate)


emissions <- Ovariable("emissions",  
# Subtrack from the building stock buildings that are already renovated.
dependencies = data.frame(
temp <- OpasnetUtils::combine(temp, -1 * oapply(prevreno, cols = "Renovation", FUN = sum)) # Subtract previously renovated
Name = c(
reno <- unkeep(temp * renovate, sources = TRUE) # Renovate the current non-renovated stock
"heatingEnergy",
# If renovate does not match with this timepoint, mark all buildings here as unrenovated.
"fuelTypes",
# Is there new renovation in this time point?
"emissionFactors"
if(sum(result(reno)) == 0) {
)
reno <- temp * Ovariable(
),
output = data.frame(Renovation = "None", Result = 1),  
formula = function(...) {
marginal = c(TRUE, FALSE)
 
)
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
newreno <- OpasnetUtils::combine(prevreno, reno) # Renovated buildings in this time point.
 
# Take the previous timepoints, and previously renovated and now possibly renovated together.
out <- unkeep(out * emissionLocations, sources = TRUE, prevresults = TRUE)
out <- rbind(newreno@output, out) # Add previously and now renovated together
 
prevreno <- unkeep(newreno, sources = TRUE)
out@output$Emission_site <- as.factor(ifelse(
prevreno@output <- prevreno@output[prevreno@output$Renovation != "None" , ]
out@output$Emission_site == "At site of consumption",
}
as.character(out@output$City_area),
out <- Ovariable(output = out, marginal = colnames(out) %in% marginals)
as.character(out@output$Emission_site)
))
# Note that if buil and renovate have different marginals (typically policies),
 
# there will be NA in these indices. This will be corrected below.
out <- oapply(
for(i in colnames(out@output)[out@marginal]) {
out,
if(any(is.na(out@output[[i]]))) {
cols = c("Heating", "Burner", "Fuel", "City_area"),
out@output <- fillna(out@output, i)
FUN = sum
print(paste("Column", i, "treated with fillna (difference between buil and renovate)."))
)
}
}
}
)
#"Emission_site", "Emission_height",
out <- oapply(unkeep(out, sources = TRUE), cols = c("Age", "Time"), FUN = sum)
 
colnames(out@output)[colnames(out@output) == "Obsyear"] <- "Time" # From now on, Time means the time of observation
exposure <- Ovariable("exposure",  
out@output$Time <- as.numeric(as.character(out@output$Time))
# 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)
return(out)
}
}
)
)


objects.store(buildings, heatingEnergy, emissions, exposure)
objects.store(buildings)


cat("Saved ovariables buildings, heatingEnergy, emissions, exposure\n")
cat("Saved ovariable buildings\n")
</rcode>
</rcode>


===Dependencies===
<noinclude>
 
* [[Exposure to PM2.5 in Finland]]
* [[OpasnetUtils/Drafts]]
* [[Energy use of buildings]]
* Ovariables used or produced and example pages where they are used:
** [[Building stock in Kuopio]]
*** buildingStock, # Current building stock
*** renovation, # Percentage of renovations per year
*** renovationShares, # Fraction of renovation type when renovation is done.
*** construction, # Construction rate in the future
*** constructionAreas,
*** buildingTypes, # A dummy variable to combine two different indices: Building and Building2
*** heatingShares, # Heating types of current buildings
*** heatingSharesNew, # Heating types of the buildings in the future
*** buildings,
** [[Energy use of  buildings]]
*** energyUse,
*** efficienciesNew, # Energy efficiencies in the future
*** savingPotential,
** [[Climate change policies and health in Kuopio]]
*** eventyear, # A dummy variable to combine time periods to numerical time axis.
*** efficiencies, # Energy efficiencies of current buildings
*** obsyear # Years for which observations are calculated. This requires timepoints function.


==See also==


==See also==
{{Helsinki energy decision 2015}}


* [[Building stock in Kuopio]]
* [[Building stock in Kuopio]]
Line 193: Line 265:
* [[Building data availability in Kuopio]]
* [[Building data availability in Kuopio]]
* [[Energy balance]]
* [[Energy balance]]
* [[Exposure to PM2.5 in Finland]]
* [[Energy use of buildings]]
* [[Building stock in Kuopio]]
* [[Intake fractions of PM]]
* [[Greenhouse gas emissions in Kuopio]]
* [[Greenhouse gas emissions in Kuopio]]
* [[:heande:File:2013 10 29 - Exposure-Response Functions.xlsx]]
* [[:heande:File:2013 10 29 - Exposure-Response Functions.xlsx]]
Line 200: Line 276:
* [[:heande:File:WP6 Deliverable 6 1.doc]]
* [[:heande:File:WP6 Deliverable 6 1.doc]]
* [[Land use in Kuopio]]
* [[Land use in Kuopio]]
* City and energy solutions from Siemens:
** [http://www.businesswire.com/news/home/20140710006170/en/Siemens-Mobile-Web-App-Enables-Utilities-Maximize-Customer#.VSORBHcqonh Mobile web app]
** [http://w3.usa.siemens.com/smartgrid/us/en/Pages/smart-grid.aspx Smart grid]
** [http://www.siemens.com/innovation/en/home/pictures-of-the-future/digitalization-and-software/from-big-data-to-smart-data-aspern-city-research-project.html Smart data in Aspern]
** [http://www.siemens.com/annual/13/en/company-report/report-ic-solutions/article/ Environmental awareness in Vienna]
** [http://w3.siemens.com/topics/global/en/sustainable-cities/pages/home.aspx Transforming cities through sustainable technology]


{{urgenche}}
{{urgenche}}
Line 206: Line 288:


<references/>
<references/>
* Sundell, J., Levin, H., Nazaroff, W. W., Cain, W. S., Fisk, W. J., Grimsrud, D. T., Gyntelberg, F., Li, Y., Persily, A. K., Pickering, A. C., Samet, J. M., Spengler, J. D., Taylor, S. T., Weschler, C. J., Ventilation rates and health: multidisciplinary review of the scientific literature. INDOOR AIR 21 (2011) 3: 191 - 204. {{doi|10.1111/j.1600-0668.2010.00703.x}} ISSN 0905-6947
* Brightman, H. S., Milton, D. K., Wypij, D., Burge, H. A., Spengler, J. D. Evaluating building-related symptoms using the US EPA BASE study results. INDOOR AIR 18 (2008) 4: 335-345. {{doi|10.1111/j.1600-0668.2008.00557.x}}
* Nishioka, Y, Levy, JI, Norris, GA, Bennett, DH, Spengler, JD. A risk-based approach to health impact assessment for input-output analysis - Part 2: Case study of insulation. INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT 10 (2005) 4: 255-262. {{doi|10.1065/lca2004.10.186.2}} ISSN 0948-3349
* Samet, JM, Spengler, JD. Indoor environments and health: Moving into the 21st century. AMERICAN JOURNAL OF PUBLIC HEALTH 93 (2003) 9: 1489-1493. {{doi|DI 10.2105/AJPH.93.9.1489}} ISSN 0090-0036
* Nishioka, Y, Levy, JI, Norris, GA, Wilson, A, Hofstetter, P, Spengler, JD. Integrating risk assessment and life cycle assessment: A case study of insulation RISK ANALYSIS 22 (2002) 5: 1003-1017. {{doi|10.1111/1539-6924.00266}} ISSN 0272-4332
</noinclude>

Latest revision as of 03:48, 10 April 2019



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

Causal diagram of the building model. The actual model is up to the yellow node Building stock, and the rest is an example how the result can be used in models downstream.

The building model follows the development of a city's or area's building stock over time. The output of the model is the floor area (or volume, depending on the input data) of the building stock of a city at specified timepoints, classified by energy efficiency, heating type, and optionally by other case-specific characteristics. The model functions as part of Opasnet's modeling environment and it is coded using R. It uses specific R objects called ovariables. The model can also be downloaded and run on one's own computer.

The model is given data about the building stock of a certain city or area during a certain period of time. The data can be described with very different levels of precision depending on the situation and what kind of information is needed. Some kind of data on the energy efficiency and heating type is necessary, but even rough estimates suffice. Then again, if there is sufficient data, the model can analyse even individual buildings.

In addition to that, the model can describe changes in the building stock, i.e. construction of new buildings and demolishing of old ones. Data on the heating- and energy efficiencies of new and demolished buildings is required at the same level of precision as that of other buildings. This data is used to calculate how construction and demolishing change the building stock's size and heating types.

The model takes into account the energy renovation of existing buildings. They are analysed using two variables: firstly, what fraction of the building stock is energy renovated yearly and secondly, what type of renovation it is. This information, too, can be rough or precise and detailed. It can describe the whole building stock with a single number or be specific data on the time, the building's age, use or other background information.

For examples of model use, see Helsinki energy decision 2015, Building stock in Kuopio and Climate change policies and health in Kuopio.

The overall equation in the model is this:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle B_{t,h,e,r} = \int\int (Bs_{c,t,a} Hs_h Es_e + Bc_{c,h,e,t,a}) Rr_a Rs_{r,t} O)\mathrm{d}c \mathrm{d}a}

  • B = buildings, floor area of buildings in specified groups
  • Bs = stockBuildings, floor area of the current buildings
  • Bc = changeBuildings, floor area of constructed and demolished (as negative areas) buildings
  • Hs = heatingShares, fractions of different heating types in a group of buildings
  • Es = efficiencyShares, fractions of different efficiency classes in a group of buildings
  • Rr = renovationRate, fraction of buildings renovated per year
  • Rs = renovationShares, fractions of different renovation types performed when buildings are renovated
  • O = obstime, timepoints for which the building stock is calculated.
  • Indices required (also other indices are possible)
    • t = Obsyear, time of observation. This is renamed Time on the output data.
    • c = Construction year (the index is named 'Time' in the input data), time when the building was built.
    • a = Age, age of building at a timepoint. This is calculated as a = t - b.
    • h = Heating, primary heating type of a building
    • e = Efficiency, efficiency class of building when built
    • r = Renovation, type of renovation done to a non-renovated building (currently, you can only renovate a building once)

The model is iterative across the Obsyear index so that renovations performed at one timepoint are inherited to the next timepoint, and that situation is the starting point for renovations in that timepoint.

Rationale

Inputs and calculations

Variables in the building model
Variable Measure Indices Missing data
stockBuildings (case-specific data from the user) e.g. Building stock in Helsinki or Building stock in Kuopio Amount of building stock (typically in floor-m2) at given timepoints. 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) You must give either stockBuildings, heatingShares, and efficiencyShares or changeBuildings or both. For missing data, use 0.
heatingShares (case-specific data from the user) Fractions of heating types. Should sum up to 1 within each group defined by optional indices. Required indices: Heating. Typical indices: Time, Building If no data, use 1 as a placeholder.
efficiencyShares (case-specific data from the user) Fraction of energy efficiency types. Should sum up to 1 for each group defined by other indices. Required indices: Efficiency. Typical indices: Time, Building. If no data, use 1 as default.
changeBuildings (case-specific data from the user) Construction or demolition rate as floor-m2 at given timepoints. Required indices: Obsyear, 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. If the data is only in stockBuildings, use 0 here.
renovationShares (case-specific data from the user) Fraction of renovation types when renovation is done. Should sum to 1 for each group defined by other indices. Required indices: Renovation, Obsyear. Obsyear is the time when the renovation is done If no data, use 1 as default.
renovationRate (case-specific data from user. You can also use fairly generic data from Building stock in Helsinki or Building stock in Kuopio.) Rate of renovation (fraction per time unit). Required indices: Age (the time difference between construction and renovation, i.e. Obsyear - Time for each building). If no data, use 0.
obstime (assessment-specific years of interest) The years to be used in output. The only index Obsyear contains the years to look at; Result is 1. Required indices: Obsyear. Typical indices: other indices are not allowed. -

This code defines the generic building model object called buildings. Other objects needed can be found from case-specific pages, see table above.

+ Show code


See also

Helsinki energy decision 2015
In English
Assessment Main page | Helsinki energy decision options 2015
Helsinki data Building stock in Helsinki | Helsinki energy production | Helsinki energy consumption | Energy use of buildings | Emission factors for burning processes | Prices of fuels in heat production | External cost
Models Building model | Energy balance | Health impact assessment | Economic impacts
Related assessments Climate change policies in Helsinki | Climate change policies and health in Kuopio | Climate change policies in Basel
In Finnish
Yhteenveto Helsingin energiapäätös 2015 | Helsingin energiapäätöksen vaihtoehdot 2015 | Helsingin energiapäätökseen liittyviä arvoja | Helsingin energiapäätös 2015.pptx
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


  • Sundell, J., Levin, H., Nazaroff, W. W., Cain, W. S., Fisk, W. J., Grimsrud, D. T., Gyntelberg, F., Li, Y., Persily, A. K., Pickering, A. C., Samet, J. M., Spengler, J. D., Taylor, S. T., Weschler, C. J., Ventilation rates and health: multidisciplinary review of the scientific literature. INDOOR AIR 21 (2011) 3: 191 - 204. doi:10.1111/j.1600-0668.2010.00703.x ISSN 0905-6947
  • Brightman, H. S., Milton, D. K., Wypij, D., Burge, H. A., Spengler, J. D. Evaluating building-related symptoms using the US EPA BASE study results. INDOOR AIR 18 (2008) 4: 335-345. doi:10.1111/j.1600-0668.2008.00557.x
  • Nishioka, Y, Levy, JI, Norris, GA, Bennett, DH, Spengler, JD. A risk-based approach to health impact assessment for input-output analysis - Part 2: Case study of insulation. INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT 10 (2005) 4: 255-262. doi:10.1065/lca2004.10.186.2 ISSN 0948-3349
  • Samet, JM, Spengler, JD. Indoor environments and health: Moving into the 21st century. AMERICAN JOURNAL OF PUBLIC HEALTH 93 (2003) 9: 1489-1493. 10.2105/AJPH.93.9.1489 doi:DI 10.2105/AJPH.93.9.1489 ISSN 0090-0036
  • Nishioka, Y, Levy, JI, Norris, GA, Wilson, A, Hofstetter, P, Spengler, JD. Integrating risk assessment and life cycle assessment: A case study of insulation RISK ANALYSIS 22 (2002) 5: 1003-1017. doi:10.1111/1539-6924.00266 ISSN 0272-4332
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