Building stock in Helsinki: Difference between revisions

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<noinclude>
[[Category:Helsinki]]
[[Category:Helsinki]]
[[Category:Buildings]]
[[Category:Buildings]]
{{variable|moderator=Jouni}}
{{variable|moderator=Jouni}}
:''During [[Decision analysis and risk management 2015]] course, this page was used to collect student contributions. To see them, look at [http://en.opasnet.org/en-opwiki/index.php?title=Building_stock_in_Helsinki&oldid=37151 an archived version]. The page has since been updated for its main use. Data in the archived tables was moved: [[Helsinki energy consumption#Energy parametres of buildings| Table 2. Energy parametres of buildings]], [[Helsinki energy consumption#Energy demand|Table 4. Energy sinks]], [[Helsinki energy consumption#Energy demand| Table 5. Changes in energy efficiency]], [[Helsinki energy consumption#Heating parametres of buildings|Table 6. Important energy parametres]]. Tables 3, 7, and 8 did not contain data and were removed.
</noinclude>


== Question ==
== Question ==


What is the building stock in Helsinki?
What is the building stock in Helsinki and its projected future?


== Answer ==
== Answer ==


<gallery widths="400px" heights="350px">
File:Current building stock in Helsinki.png|Current building stock in Helsinki by heating type.
File:Building stock in Helsinki by heating.png|Projected building stock based on 2015 data and urban plans.
</gallery>


<rcode embed=1 graphics=1>
## This code is Op_en7115/ on page [[Building stock in Helsinki]].
library(OpasnetUtils)
library(ggplot2)
objects.latest("Op_en7115", code_name = "stockBuildings")
stockBuildings <- EvalOutput(stockBuildings)
ggplot(stockBuildings@output, aes(x = Time, weight = stockBuildingsResult, fill = Heating)) +
geom_bar(binwidth = 5) + theme_gray(base_size = 24) +
labs(
title = "Current building stock (floor area) by heating type \n and year of construction",
x = "Construction year",
y = expression("Floor area ( "*m^2*" )"))
ggplot(stockBuildings@output, aes(x = Time, weight = stockBuildingsResult, fill = Building)) +
geom_bar(binwidth = 5) + theme_gray(base_size = 24) +
labs(
title = "Current building stock (floor area) by building type \n and year of construction",
x = "Construction year",
y = expression("Floor area ( "*m^2*" )"))
</rcode>


== Rationale ==
== Rationale ==


=== Data ===
This part contains the data needed for calculations about the building stock in Helsinki. It shows the different building and heating types in Helsinki, and how much and what kind of renovations are done for the existing building stock in a year, including how much and how old building stock is demolished. This data is used in further calculations in the model.


==== Building stock ====
There is also some other important data that wasn't used in the model's calculations. These include more accurate renovation statistics for residential buildings, U-value changes for renovations and thermal transmittance of different parts of residential buildings. This data is found under [[Building stock in Helsinki#Data not used|Data not used]].


The structures of the tables are based on CyPT Excel file N:\YMAL\Projects\ilmastotiekartta\Helsinki Data Input Template - Building Data.xlsx.
=== Carbon neutral Helsinki 2035 ===
 
<rcode label="Push indicators to HNH2035 (for developers only)" embed=1>
# This is code Op_en/7115 on page [[Building stock in Helsinki]]
library(OpasnetUtils)
library(plotly)
 
objects.latest("Op_en6007", code_name = "miscellaneous") # [[OpasnetUtils/Drafts]] truncateIndex
objects.latest("Op_en6007", code_name = "hnh2035") # [[OpasnetUtils/Drafts]] pushIndicatorGraph
objects.latest("Op_en7237", code_name = "intermediates") # [[Helsinki energy decision 2015]] buildings etc.
 
buildings <- EvalOutput(buildings)
 
tmp <- truncateIndex(buildings,"Building",6)
colnames(tmp@output)[colnames(tmp@output)=="EnergySavingPolicy"] <- "Scenario"
 
#> unique(tmp$Scenario)
#[1] BAU                    Energy saving moderate Energy saving total 
#[4] WWF energy saving   
levels(tmp$Scenario) <- c("BAU","NA","tavoite","NA")
tmp <- tmp[tmp$Scenario!="NA",]
 
 
p_buildings_b <- plot_ly(
  oapply(tmp[tmp$Scenario=="BAU",], c("Building","Time"),sum)@output,
  x = ~Time, y = ~buildingsResult, color = ~Building,
  type = 'scatter', mode = 'lines') %>%
layout(
  title="Rakennusala talotyypeittäin",
  xaxis=list(title="Vuosi"),
  yaxis=list(title="Rakennusala (m2)")
)
 
p_buildings_h <- plot_ly(
  oapply(tmp[tmp$Scenario=="BAU",], c("Heating","Time"),sum)@output,
  x = ~Time, y = ~buildingsResult, color = ~Heating,
  type = 'scatter', mode = 'lines') %>%
  layout(
    title="Rakennusala lämmitysmuodoittain",
    xaxis=list(title="Vuosi"),
    yaxis=list(title="Rakennusala (m2)")
  )
 
pushIndicatorGraph(p_buildings_b, "https://hnh.teamy.fi/v1/indicator/12/")
pushIndicatorGraph(p_buildings_h, "https://hnh.teamy.fi/v1/indicator/13/")
</rcode>
 
=== Building stock ===


'''Table 1. Effective floor area of buildings by building type.
'''These tables are based on FACTA database classifications and their interpretation for assessments.
This data is used for modelling. The data is large and can be seen from [http://en.opasnet.org/w/Special:Opasnet_Base?id=Op_en7115.stock_details the Opasnet Base]. Technical parts on this page are hidden for readability. Building types should match [[Energy use of buildings#Baseline energy consumption]].


Type of data:
{{hidden|
* Average, City
* Example, city
* Average, city close by
* Example, city close by
* Average, national
* Example, country
Quality of data
* Statistics
* Extrapolated
* Calculated from statistics
* Calculated from stat. inc. assumptions


<t2b name="Effective floor area of buildings" index="Building,Year" locations="Baseline,2020,2025,2050" unit="m2" desc="Year of baseline,Type of data,Quality,Description">
<t2b name="Building types" index="Building types in Facta" obs="Building" desc="Number of the class" unit="-">
Residential||||||||
Yhden asunnon talot|Detached and semi-detached houses|11
Public||||||||
Kahden asunnon talot|Detached and semi-detached houses|12
Industrial||||||||
Muut erilliset pientalot|Detached and semi-detached houses|13
Other||||||||
Rivitalot|Attached houses|21
Ketjutalot|Attached houses|22
Luhtitalot|Blocks of flats|32
Muut asuinkerrostalot|Blocks of flats|39
Vapaa-ajan asuinrakennukset|Free-time residential buildings|41
Myymälähallit|Commercial|111
Liike- ja tavaratalot, kauppakeskukset|Commercial|112
Myymälärakennukset|Commercial|119
Hotellit, motellit, matkustajakodit, kylpylähotellit|Commercial|121
Loma-, lepo- ja virkistyskodit|Commercial|123
Vuokrattavat lomamökit ja osakkeet (liiketoiminnallisesti)|Commercial|124
Muut majoitusliikerakennukset|Commercial|129
Asuntolat, vanhusten palvelutalot, asuntolahotellit|Commercial|131
Muut asuntolarakennukset|Commercial|139
Ravintolat, ruokalat ja baarit|Commercial|141
Toimistorakennukset|Offices|15
Toimistorakennukset|Offices|151
Rautatie- ja linja-autoasemat, lento- ja satamaterminaalit|Transport and communications buildings|161
Kulkuneuvojen suoja- ja huoltorakennukset|Transport and communications buildings|162
Pysäköintitalot|Transport and communications buildings|163
Tietoliikenteen rakennukset|Transport and communications buildings|164
Muut liikenteen rakennukset|Transport and communications buildings|169
Keskussairaalat|Buildings for institutional care|211
Muut sairaalat|Buildings for institutional care|213
Terveyskeskukset|Buildings for institutional care|214
Terveydenhuollon erityislaitokset|Buildings for institutional care|215
Muut terveydenhuoltorakennukset|Buildings for institutional care|219
Vanhainkodit|Buildings for institutional care|221
Lasten- ja koulukodit|Buildings for institutional care|222
Kehitysvammaisten hoitolaitokset|Buildings for institutional care|223
Muut huoltolaitosrakennukset|Buildings for institutional care|229
Lasten päiväkodit|Buildings for institutional care|231
Muualla luokittelemattomat sosiaalitoimen rakennukset|Buildings for institutional care|239
Vankilat|Buildings for institutional care|241
Teatterit, konsertti- ja kongressitalot, oopperat|Assembly buildings|311
Elokuvateatterit|Assembly buildings|312
Kirjastot ja arkistot|Assembly buildings|322
Museot ja taidegalleriat|Assembly buildings|323
Näyttelyhallit|Assembly buildings|324
Seurain-, nuoriso- yms. talot|Assembly buildings|331
Kirkot, kappelit, luostarit, rukoushuoneet|Assembly buildings|341
Seurakuntatalot|Assembly buildings|342
Muut uskonnollisten yhteisöjen rakennukset|Assembly buildings|349
Jäähallit|Assembly buildings|351
Uimahallit|Assembly buildings|352
Tennis-, squash- ja sulkapallohallit|Assembly buildings|353
Monitoimihallit ja muut urheiluhallit|Assembly buildings|354
Muut urheilu- ja kuntoilurakennukset|Assembly buildings|359
Muut kokoontumisrakennukset|Assembly buildings|369
Peruskoulut, lukiot ja muut|Educational buildings|511
Ammatillisten oppilaitosten rakennukset|Educational buildings|521
Korkeakoulurakennukset|Educational buildings|531
Tutkimuslaitosrakennukset|Educational buildings|532
Järjestöjen, liittojen, työnantajien yms. opetusrakennukset|Educational buildings|541
Muualla luokittelemattomat opetusrakennukset|Educational buildings|549
Voimalaitosrakennukset|Industrial buildings|611
Yhdyskuntatekniikan rakennukset|Industrial buildings|613
Teollisuushallit|Industrial buildings|691
Teollisuus- ja pienteollisuustalot|Industrial buildings|692
Muut teollisuuden tuotantorakennukset|Industrial buildings|699
Teollisuusvarastot|Warehouses|711
Kauppavarastot|Warehouses|712
Muut varastorakennukset|Warehouses|719
Paloasemat|Fire fighting and rescue service buildings|721
Väestönsuojat|Fire fighting and rescue service buildings|722
Muut palo- ja pelastustoimen rakennukset|Fire fighting and rescue service buildings|729
Navetat, sikalat, kanalat yms.|Agricultural buildings|811
Eläinsuojat, ravihevostallit, maneesit|Agricultural buildings|819
Viljankuivaamot ja viljan säilytysrakennukset|Agricultural buildings|891
Kasvihuoneet|Agricultural buildings|892
Turkistarhat|Agricultural buildings|893
Muut maa-, metsä- ja kalatalouden rakennukset|Agricultural buildings|899
Saunarakennukset|Other buildings|931
Talousrakennukset|Other buildings|941
Muualla luokittelemattomat rakennukset|Other buildings|999
Ammatilliset oppilaitokset|Educational buildings|
Kirjastot|Other buildings|
Lastenkodit, koulukodit|Other buildings|
Loma- lepo- ja virkistyskodit|Other buildings|
Monitoimi- ja muut urheiluhallit|Other buildings|
Museot, taidegalleriat|Assembly buildings|
Muut kerrostalot|Blocks of flats|
Muut majoitusrakennukset|Commercial|
Muut terveydenhoitorakennukset|Buildings for institutional care|
Terveydenhoidon erityislaitokset (mm. kuntoutuslaitokset)|Buildings for institutional care|
Vapaa-ajan asunnot|Free-time residential buildings|
Viljankuivaamot ja viljan säilytysrakennukset, siilot|Warehouses|
</t2b>
</t2b>


;Notes: Sheet 4_Input Buildings (Area Demand). Priority 1. Auxiliaries PPT. Absolute increase/decrease rate will be based on the inhabitants projected in time.
{{comment|# |Viimeiset 12 riviä (ilman numeroa) ovat tyyppejä jotka ovat datassa (tai ainakin vanhassa taulukossa) mutta puuttuvat Sonjan luokittelusta.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 12:57, 24 August 2015 (UTC)}}
: This is another list that was considered but rejected as too complex: Residential buildings, Government & public administration buildings, Commercial offices buildings, Data centers buildings, Education and K12 and universitiy buildings, Hospitals and healthcare buildings, Hotels and hospitality and leisure buildings, Exhibitions and fairs and halls buildings, Retail and stores and shops buildings, Warehouses & shopping mall buildings, Industrial buildings, Non residential buildings unspecified


'''Table 2. Existing situation of important energy parametres in the building stock.
For residential buildings (classes A and B) the classification is kept more detailed than for other buildings. This is because residential buildings are the biggest energy consumers in Helsinki and different classes of residential buildings are examined separately.<br />
Reference for the classification: http://www.stat.fi/meta/luokitukset/rakennus/001-1994/koko_luokitus.html


<t2b name="Existing situation of total stock" index="Property,Building" locations="Residential,Public,Industrial,Other" desc="Description" unit="%">
<t2b name="Heating types" index="Heating types in Facta" obs="Heating" unit="-">
Wall insulation|||||
|Other
High efficient glazing|||||
Kauko- tai aluelämpö|District heating
Efficient lighting in baseline|||||
Kevyt polttoöljy|Light oil
Demand oriented lighting|||||
Kivihiili, koksi tms.|Coal
Building Efficiency Monitoring|||||
Maalämpö tms.|Geothermal
Building Remote Monitoring|||||
Puu|Wood
Building Performance Optimization|||||
Raskas polttoöljy|Fuel oil
Demand controlled ventilation|||||
Sähkö|Electricity
Heat and Cold Recovery in ventilation|||||
Kaasu|Gas
Efficient Motors|||||
Muu|Other
Building Automation BACS Class C|||||
Building Automation BACS Class B|||||
Building Automation BACS Class A|||||
Room Automation HVAC|||||
Room Automation HVAC + lighting|||||
Building Automation HVAC + lighting + blinds|||||
</t2b>
</t2b>


;Notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries PPT.
The structures of the tables are based on CyPT Excel file N:\YMAL\Projects\ilmastotiekartta\Helsinki Data Input Template - Building Data.xlsx.
}}
 
<rcode name="stockBuildings" label="Initiate stockBuildings (developers only)" embed=1 store=1>
library(OpasnetUtils)
library(ggplot2)
 
# [[Building stock in Helsinki]], building stock, locations by city area (in A Finnish coordinate system)
#stockBuildings <- Ovariable("stockBuildings", ddata = "Op_en7115.stock_details")
#colnames(stockBuildings@data)[colnames(stockBuildings@data) == "Built"] <- "Time"
#colnames(stockBuildings@data)[colnames(stockBuildings@data) == "Postal code"] <- "City_area"
 
# [[Building stock in Helsinki]]
dat <- opbase.data("Op_en7115.stock_details")[ , c(
# "Rakennus ID",
"Sijainti",
"Valmistumisaika",
# "Julkisivumateriaali",
"Käyttötarkoitus",
# "Lämmitystapa",
"Polttoaine",
# "Rakennusaine",
# "Varusteena koneellinen ilmanvaihto",
# "Perusparannus",
# "Kunta rakennuttajana",
# "Energiatehokkuusluokka",
# "Varusteena aurinkopaneeli",
"Tilavuus",
"Kokonaisala",
"Result" # Kerrosala m2
)]
 
colnames(dat) <- c("City_area", "Time", "Building types in Facta", "Heating types in Facta", "Tilavuus", "Kokonaisala", "Kerrosala")
dat$Time <- as.numeric(substring(dat$Time, nchar(as.character(dat$Time)) - 3))
 
#dat <- dat[dat$Time != 2015 , ] # This is used to compare numbers to 2014 statistics.
 
dat$Time <- as.numeric(as.character((cut(dat$Time, breaks = c(0, 1885 + 0:26*5), labels = as.character(1885 + 0:26*5)))))
dat$Tilavuus <- as.numeric(as.character(dat$Tilavuus))
dat$Kokonaisala <- as.numeric(as.character(dat$Kokonaisala))
dat$Kerrosala <- as.numeric(as.character(dat$Kerrosala))
 
build <- tidy(opbase.data("Op_en7115.building_types"))
colnames(build)[colnames(build) == "Result"] <- "Building"


==== Energy demand ====
heat <- tidy(opbase.data("Op_en7115.heating_types"))
colnames(heat)[colnames(heat) == "Result"] <- "Heating"


'''Table 3. Total energy demand by energy type and building type.
######################
# Korjaus
########################
temp <- as.character(heat$Heating)
temp[temp == "District heating"]  <- "District"
temp[temp == "Light oil"]  <- "Oil"
temp[temp == "Fuel oil"]  <- "Oil"


<t2b name="Total energy demand by type" index="Energy type,Building" locations="Residential,Public,Industrial,Other" desc="Description" unit="kWh /m2 /a">
heat$Heating <- temp
Electricity|||||
########################################
Cooling|||||
 
Heating|||||
dat <- merge(merge(dat, build), heat)#[c("City_area", "Time", "Building", "Heating", "stockBuildingsResult")]
 
dat$Kerrosala[is.na(dat$Kerrosala)] <- dat$Kokonaisala[is.na(dat$Kerrosala)] * 0.8 # If floor area is missing, estimate from total area.
 
cat("Kerrosala ilman 2015 (m^2)\n")
oprint(aggregate(dat["Kerrosala"], by = dat["Building"], FUN = sum, na.rm = TRUE))
cat("Kokonaisala ilman 2015 (m^2)\n")
oprint(aggregate(dat["Kokonaisala"], by = dat["Building"], FUN = sum, na.rm = TRUE))
cat("Tilavuus ilman 2015 (m^3)\n")
oprint(aggregate(dat["Tilavuus"], by = dat["Building"], FUN = sum, na.rm = TRUE))
 
temp <- aggregate(dat["Kerrosala"], by = dat[c("Time", "Building", "Heating")], FUN =sum, na.rm = TRUE)
colnames(temp)[colnames(temp) == "Kerrosala"] <- "stockBuildingsResult"
 
stockBuildings <- Ovariable("stockBuildings", data = temp)
 
objects.store(stockBuildings)
cat("Ovariable stockBuildings stored.\n")
</rcode>
 
=== Construction and demolition ===
 
It is assumed that construction occurs at a constant rate so that there is an increase of 42% in 2050 compared to 2013. Energy efficiency comes from [[Energy use of buildings]].
 
<rcode name="changeBuildings" label="Initiate changeBuildings (for developers only)" embed=1>
# This code is Op_en7115/changeBuildings on page [[Building stock in Helsinki]]
library(OpasnetUtils)
 
changeBuildings <- Ovariable("changeBuildings",  
dependencies = data.frame(
Name = c(
"stockBuildings",
"efficiencyShares"
),
Ident = c(
"Op_en7115/stockBuildings", # [[Building stock in Helsinki]]
"Op_en5488/efficiencyShares" # [[Energy use of buildings]]
)
),
formula = function(...) {
 
out <- oapply(stockBuildings, cols = c("Time", "Constructed"), FUN = sum)
out <- out * 0.013125 * 5 * efficiencyShares # linear increase 42% from 2013 to 2050
out@output <- out@output[as.numeric(as.character(out@output$Time)) >= 2015 , ]
 
return(out)
}
)
 
objects.store(changeBuildings)
cat("Ovariable changeBuildings stored.\n")
 
</rcode>
 
'''Fraction of houses demolished per year.
 
<t2b name="Demolition rate" index = "Age" obs="Rate" unit="% /a">
0|0
50|1
1000|1
</t2b>
</t2b>
;Notes: Heating includes warm water. Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries PPT.


'''Table 4. Shares of different energy sinks by building type.
<rcode name="demolitionRate" label="Initiate demolitionRate (for developers only)" embed=1>
# This code is Op_en7115/demolitionRate on page [[Building stock in Helsinki]]
library(OpasnetUtils)
 
demolitionRate <- Ovariable('demolitionRate',
dependencies = data.frame(Name = "dummy"),
formula = function(...) {
temp <- tidy(opbase.data('Op_en7115', subset = 'Demolition rate'))
temp$Age <- round(as.numeric(as.character(temp$Age)))
out <- as.data.frame(approx(
temp$Age,
temp$Result,
n = (max(temp$Age) - min(temp$Age) + 1),
method = "constant"
))
colnames(out) <- c("Age", "demolitionRateResult")
out$demolitionRateResult <- out$demolitionRateResult / 100 * 10 # For ten-year intervals
out <- Ovariable("demolitionRate", output = out, marginal = c(TRUE, FALSE))
return(out)
}
)
 
objects.store(demolitionRate)
cat("Object demolitionRate stored.\n")
 
</rcode>


<t2b name="Share of energy demand by use type" index="Energy type,Use,Buiding" locations="Residential,Other,Admin,Industry" unit="kWh/m2a" desc="Description,Auxiliaries">
=== Heating type conversion ===
Cooling|Infiltration|0.0|0.0|0.0|0.0||PPT
 
Cooling|Ventilation|0.0|0.0|0.0|9.0||PPT
The fraction of heating types in the building stock reflects the situation at the moment of construction and not currently. The heating type conversion corrects this by changing a fraction of heating methods to a different one at different timepoints. Cumulative fraction, other timepoints will be interpolated.  
Cooling|Losses through walls through transmission|0.0|0.0|0.0|0.0||PPT
 
Cooling|Heat input by solar radiation through windows|0.0|0.0|0.0|0.0||PPT
<t2b name='Yearly_heating_converted_factor' index='Heating_from,Heating_to,Time' unit='m2/m2'>
Cooling|Losses through windows through transmission|0.0|0.0|0.0|0.0||PPT
Oil|Geothermal|2005|0
Cooling|Other effects (e.g. people, electrical Appliances)|0.0|0.0|0.0|0.0||PPT
Oil|Geothermal|2015|0.5
Heating|Infiltration|0.0|0.0|0.0|0.0||PPT
Oil|Geothermal|2025|1
Heating|Ventilation|29.19|27.56|27.22|27.22||PPT
Heating|Walls|34.75|32.81|32.40|32.40||PPT
Heating|Windows|15.49|14.63|14.44|14.44||PPT
Heating|Floors|8.74|8.25|8.15|8.15||PPT
Heating|Roofs|9.33|8.81|8.70|8.70||PPT
Heating|Other|0.0|0.0|0.0|0.0||PPT
Heating|Warm water|1.89|0.0|1.76|1.76||Excel
Electricity|Lighting|2.82|20.05|49.68|8.67||Excel
Electricity|Appliances|24.84|18.74|14.19|13.00||Excel
Electricity|Ventilation|0|12.03|21.29|18.24||Excel
Electricity|Other|0.56|23.69|56.77|51.76||Excel
</t2b>
</t2b>


{{attack|# |The problem with your table is that you have changed the columns. You cannot do that, because the original classification is what we use in the model. Instead, you have to extrapolate from the existing numbers to those classes needed in the model.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 06:45, 29 April 2015 (UTC)}}
<rcode name="heatTypeConversion" label="Initiate heatTypeConversion(developers only)" embed=1>
library(OpasnetUtils)


{{comment|# |Use decimal points instead of decimal commas.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 06:45, 29 April 2015 (UTC)}}
heatTypeConversion <- Ovariable("heatTypeConversion",
dependencies = data.frame(
Name = c(
"buil", # stock at different timepoints
"obstime"
)
),
formula = function(...) {
dat <- opbase.data("Op_en7115", subset = "Yearly_heating_converted_factor")
colnames(dat)[colnames(dat) == "Time"] <- "Obsyear"


{{comment|# |Also include references and links so that the reader can go back to the original data and see where the numbers came from.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 06:45, 29 April 2015 (UTC)}}
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"


;Notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries: see table.
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)
#sum(out$Result)
#nrow(out1@output)*2 - nrow(out@output)
return(out)
}
)
objects.store(heatTypeConversion)
cat("Ovariable heatTypeConversionstored.\n")
</rcode>


'''Table 5. Changes in energy efficiency of different energy sinks.
=== Renovations ===


<t2b name="Efficiency increase decreasing energy demand" index="Energy type,Use,Building" locations="Residential,Public,Industrial,Other" desc="Description" unit="% /a">
Estimates from Laura Perez and Stephan Trüeb, unibas.ch N:\YMAL\Projects\Urgenche\WP9 Basel\Energy_scenarios_Basel_update.docx
Electricity|Lighting /Lamp stock|||||
 
Electricity|Appliances|||||
<t2b name='Fraction of houses renovated per year' index="Age" obs="Result" desc="Description" unit= "%">
Electricity|Ventilation|||||
0|0|Estimates from Laura Perez and Stephan Trüeb
Electricity|Other|||||
20|0|Assumption Result applies to buildings older than the value in the Age column.
Cooling|Infiltration|-2||||
25|1|
Cooling|Ventilation|-2||||
30|1|
Cooling|Other (e.g. electric appliances, people)|-2||||
50|1|
Heating|Infiltration|||||
100|1|
Heating|Ventilation|||||
1000|1|
Heating|Floors|||||
Heating|Roofs|||||
Heating|Other reasons|||||
Heating|Warm water|-1,1||||
</t2b>
</t2b>


;Notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries Excel.
<rcode name="renovationRate" label="Initiate renovationRate (developers only)" embed=1>
library(OpasnetUtils)


renovationRate <- Ovariable('renovationRate',
dependencies = data.frame(Name = "dummy"),
formula = function(...) {
temp <- tidy(opbase.data('Op_en7115', subset = 'Fraction of houses renovated per year'))
temp$Age <- round(as.numeric(as.character(temp$Age)))
out <- as.data.frame(approx(
temp$Age,
temp$Result,
n = (max(temp$Age) - min(temp$Age) + 1),
method = "constant"
))
colnames(out) <- c("Age", "renovationRateResult")
out$renovationRateResult <- out$renovationRateResult / 100
out <- Ovariable("renovationRate", output = out, marginal = c(TRUE, FALSE))
return(out)
}
)


'''Table 6. Important energy parameters.
objects.store(renovationRate)
cat("Object renovationRate stored.\n")


<t2b name="Energy parameters" index="Parameter,Unit,Building" locations="Residential,Public,Industrial,Other" unit="-" desc="Description,Auxiliary">
</rcode>
Ratio of wall area / effective area|-|0.00|0.00|0.00|0.00|0.00|PPT
 
Ratio of window/effective area|-|0.00|0.00|0.00|0.00|0.00|PPT
<t2b name='Popularity of renovation types' index='Renovation' obs='Fraction' desc='Description' unit='%'>
U-value of windows|W /m2 /K|0.00|0.00|0.00|0.00|0.00|PPT
None|0|
Solar heat gain coefficient "G-Value" of windows|%|0.0|0.0|0.0|0.0|0.0|PPT
Windows|65|
Efficiency increase of G-value of windows|% /a|0.0|0.0|0.0|0.0|0.0|Excel
Technical systems|30|
Efficiency increase of U-value of windows|% /a|0.0|0.0|0.0|0.0|0.0|Excel
Sheath reform|5|
U-value of building walls|W /m2 /K|0.00|0.00|0.00|0.00|0.00|PPT
General|0|
</t2b>
</t2b>


;Notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries: see table.
<rcode name="renovationShares" label="Initiate renovationShares (developers only)" embed=1>
library(OpasnetUtils)


==== Lighting ====
renovationShares <- Ovariable("renovationShares",
dependencies = data.frame(Name = "dummy"),
formula = function(...) {
out <- Ovariable("raw", ddata = 'Op_en7115', subset = 'Popularity of renovation types')
out <- findrest((out), cols = "Renovation", total = 100) / 100


'''Tables 7-8. Run times and shares of lamps by lamp type and building type.
renovationyear <- Ovariable("renovationyear", data = data.frame(
Obsyear = factor(c(2015, 2025, 2035, 2045, 2055, 2065)),
Result = 1
))


<t2b name="Average run time of lamps per year" index="Lamp,Building" locations="Residential,Public,Industrial,Other" desc="Description" unit="h /a">
out <- out * renovationyear # renovation shares repeated for every potential renovation year.
Incandescent lamps|||||
 
Compact fluorescent lamps|||||
out@output$Renovation <- factor(out@output$Renovation, levels = c(
Fluorescent lamps|||||
"None",
Low voltage halogen lamps|||||
"General",  
Medium voltage halogen lamps|||||
"Windows",  
</t2b>
"Technical systems",  
"Sheath reform"
), ordered = TRUE)
 
return(out)
}
)
 
objects.store(
renovationShares # Fraction of renovation type when renovation is done.
)
 
cat("Objects renovationShares stored.\n")
 
</rcode>
<noinclude>
=== Locations of city areas ===
 
;Locations of city areas (hidden for readability).
 
{{hidden|
The positions listed here are used for exposure modelling. Area code matches with stock detail data on [[Building stock in Helsinki]]. The coordinates should be visually checked from http://www.karttapaikka.fi referencing picture X.


<t2b name="Shares of lamp types" index="Lamp,Building" locations="Residential,Public,Industrial,Other" desc="Description" unit="%">
<t2b name="Locations of city areas" index="City_area,Area code,Location" locations="N,E" unit="epsg:ETRS-TM35FIN">
Incandescent lamps|||||
001|KRU|6672352|386953
Compact fluorescent lamps|||||
002|KLU|6672912|385513
Fluorescent lamps|||||
003|KAA|6671692|386153
Low voltage halogen lamps|||||
004|KAM|6671692|385233
Medium voltage halogen lamps|||||
005|PUN|6671352|385113
006|E|6670792|385573
007|UL|6670372|385973
008|KAT|6671752|387933
009|KAI|6670632|387573
010|SÖR|6674072|387453
011|KAL|6673712|385933
012|ALP|6673712|385933
013|ETU|6672552|383733
014|TAK|6673472|384753
015|ARI|6673872|382813
016|RUS|6676072|383793
017|PAS|6676092|385093
018|LAA|6675052|384433
019|MUSKOR|6672592|388413
020|LÄN|6670552|384033
021|HER|6675432|387573
022|VAL|6674912|386573
023|TOU|6676272|387793
024|KUM|6676192|386593
025|KÄP|6676932|386153
026|KOS|6677812|387393
027|VAN|6676972|387933
028|OUL|6678952|386533
029|HAA|6678032|383353
030|MUN|6674752|381733
031|LAU|6670952|381593
032|KON|6679852|380593
033|KAA|6680752|382413
034|PAK|6680392|385993
035|TUO|6682492|385653
036|VII|6676912|389553
037|PUK|6680352|388573
038|MAL|6680252|390153
039|TAP|6682632|389273
040|SUUT|6684152|389693
041|SUUR|6682512|393113
042|KUL|6674292|389193
043|HER|6675272|391093
044|TAM|6674252|392653
045|VAR|6677372|394013
046|PIT|6678172|381453
047|MEL|6679192|394193
048|VAR|6672872|393793
049|LAA|6672072|391593
050|VIL|6670412|395553
051|SAN|6668612|392993
052|SUO|6668912|388813
053|ULK|6666992|395973
054|VUO|6675252|397553
055|ÖST|6681312|399633
056|SAL|6679252|3985130
057|TAL|6679452|400333
058|KAR|6680652|401453
059|ULT|6683472|400453
401||6684572|390899
402||6680780|382563
403||6677628|383571
404||6682460|385139
405||6675756|391603
406||6679980|391731
407||6679724|380771
408||6676828|387779
409||6673900|389379
410||6675708|386355
411||6678556|387027
412||6671724|392691
413||6674156|391539
414||6668124|381699
415||6673356|380707
417||6677388|399763
418||6680476|390883
419||6678956|395043
420||6676332|381987
421||6683244|385635
422||6678972|386803
423||6680252|385939
424||6679740|388819
425||6669196|391683
426||6683676|388515
427||6684108|389843
428||6678156|381315
429||6682524|391027
430||6672204|391459
431||6681740|386131
432||6672924|385635
433||6677196|394211
434||6678188|390147
435||6676380|397027
436||6676380|384755
437||6674156|383123
438||6682316|398147
439||6679612|400499
440||6684124|402355
441||6679724|399027
442||6682076|400499
878||6678380|390771
895||6678380|390771
</t2b>
</t2b>
}}


;Notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries Excel.
=== Data not used ===


==== Other ====
This contains data that was not used in the model's calculations. This includes renovation rates, the rates of heat flowing out of buildings and total floor areas of multiple types of buildings in Helsinki. The floor area data is also found in the background data of this page, which was used in the model.
 
Sheet 4_Input Buildings (Area Demand). Priority 1. Auxiliaries PPT. Nice to have specific factor otherwise we adapt standard data or other studies.


{| {{prettytable}}
{| {{prettytable}}
|+''Possibly important information
|+'''Effective floor area of buildings by building type.
!Data!!Result
|----
|----
|Number of households||
|| Building|| Baseline|| 2020|| 2025|| 2050|| Year of baseline|| Description
|----
|----
|Number of persons per household||
|| Residential|| 27884795|| 32472388|| 34890241|| 44069914|| 2014|| Building stock of Helsinki area, 2014
|----
|| Public|| 4537025|| 4764475|| 4945952|| 5855546|| 2014|| Building stock of Helsinki area, 2014
|----
|| Industrial|| 3277271|| 3306063|| 3360467|| 3640854|| 2014|| Building stock of Helsinki area, 2014
|----
|| Other|| 10861972|| 11406505|| 11840973|| 13806423|| 2014|| Building stock of Helsinki area, 2014
|----
|}
 
;Notes:
* Estimates were based on {{#l:Siemens City Performance toolin seuraava kokous 2.2.pdf}} and some derived calculations on {{#l:BUILDING STOCK CALCULATION 2015.xlsx}}.
* How to get the numbers for the ''baseline floor area'' for residential, public, industrial and other: Residential floor area was named as residential together, public by summing the floor area of health care, education  and common  buildings, industrial buildings were as such and other buildings comprise of business, traffic, office and storage buildings.
* Ref. Helsinki master plan for 2050: there are 860 000 citizens living in Helsinki  (ref. www.yleiskaava.fi, visio2050);  Residental buildings => fast growth
* Prediction of citizen number in Helsinki in 2020, 2030, 2040 and 2050 was used for calculations (ref. Helsingin 30% päästövähennysselvitys).
* Helsinki’s climate policy: 30% reduction in emissions:  In 2010 the proportion of jobs in services and public sectors was 94%, and in industry 6%.  In 2020 the proportion of jobs in services and public sectors is estimated to be 96%, and in industry 4%. Public and other buildings => between fast growth option and basic option,  Industry=> Basic option
* Prediction of job number in Helsinki in 2020, 2030, 2040 and 2050 was used for calculations (ref. Helsingin 30% päästövähennysselvitys).
* {{#l:Tables one and two.pdf}} The presentation of Tables 1 and 2
 
 
''Technical notes'':
: Sheet 4_Input Buildings (Area Demand). Priority 1. Auxiliaries PPT. Absolute increase/decrease rate will be based on the inhabitants projected in time.
: This is another list building types that was considered but rejected as too complex: Residential buildings, Government & public administration buildings, Commercial offices buildings, Data centers buildings, Education and K12 and universitiy buildings, Hospitals and healthcare buildings, Hotels and hospitality and leisure buildings, Exhibitions and fairs and halls buildings, Retail and stores and shops buildings, Warehouses & shopping mall buildings, Industrial buildings, Non residential buildings unspecified.
* There was a problem with missing data. There is more than 400000 m^2 floor area that is missing; this is estimated from total area that is available for these buildings. For other buildings, there is more than 400000 m^2 total area missing from buildings where floor area is given. See statistical analysis [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=026ekUv81jP0A6rI]. This was corrected by inputation so that is floor area was missing, 0.8*total_area was used instead [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=oE7UXNQt3Izdo4tE].
 
{| {{prettytable}}
|+ Renovations per year made in residental buildings owned by Helsinki city, by construction year of the buildings.<ref name="uarvo">[http://www.tut.fi/ee/en/Materiaali/HAESS_loppuraportti_TTY_14062010.pdf HAESS Final report], Tampere University of Technology, 2010</ref>
|---
! Construction year !! Balcony glasses !! Windows !! Julkisivujen peruskorjaus !! Vesikattojen peruskojaus !! Lämmönvaihtimen uusiminen !! Patteriverkoston säätö !! Kylpyhuonekalusteiden vaihto !! Patteriventtiilien vaihto !! New balcony doors !! LTO-laitteen asennus !! Water consumption measurements
|---
| -20 || 0,0 % || 1,1 % || 1,1 % || 1,1 % || 1,1 % || 1,1 % || 1,1 % || 1,1 % || 1,1 % || 1,1 % || 1,1 %
|---
| 21-25 ||0,0 % || 10,3 % || 1,2 % || 11,1 % || 10,3 % || 10,3 % || 1,2 % || 10,3 % || 1,2 % || 10,3 % || 10,3 %
|---
| 26-30 || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 9,5 % || 0,0 %
|---
| 31-35 || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 0,0 %
|---
| 36-40 || 0,0 % || 0,0 % || 0,0 % || 0,0 % || 4,2 % || 4,2 % || 0,0 % || 0,0 % || 0,0 % || 4,2 % || 0,0 %
|---
| 41-45 || 0,0 % || 16,7 % || 0,0 % || 16,7 % || 16,7 % || 16,7 % || 0,0 % || 16,7 % || 0,0 % || 16,7 % || 16,7 %
|---
| 46-50 || 0,0 % || 5,2 % || 0,0 % || 7,4 % || 7,4 % || 7,4 % || 0,0 % || 7,4 % || 0,0 % || 5,2 % || 5,2 %
|---
| 51-55 || 0,0 % || 11,3 % || 0,0 % || 8,8 % || 8,8 % || 8,8 % || 0,0 % || 8,8 % || 0,0 % || 8,8 % || 16,2 %
|---
| 56-60 || 0,0 % || 5,4 % || 0,0 % || 4,9 % || 6,2 % || 7,1 % || 0,0 % || 6,2 % || 4,5 % || 4,5 % || 5,4 %
|---
| 61-65 || 0,0 % || 1,5 % || 1,3 % || 0,8 % || 2,9 % || 2,4 % || 1,0 % || 2,4 % || 0,9 % || 0,9 % || 2,9 %
|---
| 66-70 || 0,6 % || 2,9 % || 1,2 % || 2,8 % || 1,4 % || 2,3 % || 1,1 % || 1,1 % || 0,1 % || 1,1 % || 1,1 %
|---
| 71-75 || 3,2 % || 3,1 % || 3,4 % || 2,9 % || 3,1 % || 2,6 % || 0,2 % || 1,1 % || 0,2 % || 0,2 % || 0,2 %
|---
| 76-80 || 0,1 % || 2,7 % || 0,1 % || 0,7 % || 2,0 % || 1,7 % || 1,1 % || 1,2 % || 0,2 % || 0,4 % || 0,2 %
|---
| 81-85 || 1,0 % || 2,8 % || 0,7 % || 2,3 % || 3,3 % || 4,8 % || 3,5 % || 0,0 % || 0,0 % || 0,0 % || 0,8 %
|---
| 86-90 || 0,0 % || 1,3 % || 0,0 % || 2,1 % || 6,1 % || 1,6 % || 0,7 % || 1,8 % || 0,3 % || 0,3 % || 1,0 %
|---
| 91-95 || 0,6 % || 0,3 % || 0,0 % || 3,9 % || 8,6 % || 1,9 % || 5,1 % || 0,8 % || 0,2 % || 0,0 % || 1,3 %
|---
| 96-00 || 0,1 % || 0,0 % || 0,0 % || 0,6 % || 1,2 % || 1,0 % || 1,5 % || 1,0 % || 0,0 % || 0,0 % || 4,2 %
|---
| 01-05 || 2,9 % || 0,0 % || 0,0 % || 0,0 % || 1,2 % || 1,0 % || 0,0 % || 1,0 % || 0,0 % || 0,0 % || 0,7 %
|---
| 06-10 || 1,7 % || 0,0 % || 0,0 % || 0,0 % || 0,5 % || 0,5 % || 0,0 % || 0,5 % || 0,0 % || 0,0 % || 0,0 %
|}
|}


=== Calculations ===
{{defend|# |In the document there are similar tables for total renovations from 2010 onwards to years 2016, 2020 and 2050.|--[[User:Heta|Heta]] ([[User talk:Heta|talk]]) 09:28, 16 June 2015 (UTC)}}
 
{| {{prettytable}}
|+'''Toimenpiteiden vaikutukset yksittäisessä kohteessa ja toimenpiteisiin liittyviä huomautuksia.<ref name="uarvo"/>
|---
! Action !! The feature in question !! Difference to before !! Unit !! Notes
|---
| Glass for balconies || U-value for windows || -0,3 || W/m<sup>2</sup>,K || Säästö 1-4% rakennustasolla
|--
| Changing the windows || U-value for windows || -1 || W/m<sup>2</sup>,K || Vanhoista osa kaksilasisia ja osa kolmilasisia. Uudes 1,0  W/m<sup>2</sup>,K tai alle
|---
| Julkisivun peruskorjaus || U-value of walls || -0,2 ||  W/m<sup>2</sup>,K || U-arvo puolitetaan eli n. 100 mm lisäeristys
|---
| Vesikattojen peruskorjaus || Yläpohjan U-arvo || -0,15 ||  W/m<sup>2</sup>,K || Oletetaan 50% lisäeristys U-arvo puoleen eli n. 100 mm lisäerstys
|---
| Balcony door change || U-value of doors || -0,5 ||  W/m<sup>2</sup>,K || Tiivistyminen tuo lisäsäästöä
|}
 
{| {{prettytable}}
|+'''Thermal transmittances of building components and air flow rates. Averaged values calculates from the detailed model are presented here.<ref>MK Mattinen, J Heljo, J Vihola, A Kurvinen, S Lehtoranta, A Nissinen: Modeling and visualisation of residential sector energy consumption and greenhouse gas emissions</ref>
|---
|colspan="2" rowspan="2"| Construction decade ||colspan="5"| Thermal transmittance factors for building components (W/m2K) ||colspan="3"| Ventilation and leakage air rates (1/h)
|---
| Floor || Roof || Walls || Windows || Outdoors || Supply air through the heat recovery unit || Supply air bypassing the heat recovery unit || Leakage air
|---
|rowspan="3"| Before 1980 || Single family house || 0.52 || 0.32 || 0.54 || 2.14 || 1.18 || 0.30 || 0.05 || 0.20
|---
| Row house || 0.52 || 0.36 || 0.56 || 2.15 || 1.00 || 0.3 || 0.05 || 0.20
|---
| Apartment building || 0.59 || 0.37 || 0.61 || 2.18 || 1.40 || 0.37 || 0.00 || 0.10
|---
|rowspan="3"| 1980's || Single family house || 0.30 || 0.21 || 0.28 || 1.70 || 1.00 || 0.30 || 0.05 || 0.15
|---
| Row house || 0.32 || 0.22 || 0.30 || 1.70 || 1.00 || 0.30 || 0.05 || 0.15
|---
| Apartment building || 0.34 || 0.23 || 0.29 || 1.80 || 1.40 || 0.35 || 0.00 || 0.10
|---
|rowspan="3"| 1990's || Single family house || 0.25 || 0.20 || 0.25 || 1.70 || 1.00 || 0.30 || 0.05 || 0.15
|---
| Row house || 0.32 || 0.22 || 0.28 || 1.70 || 1.00 || 0.30 || 0.05 || 0.15
|---
| Apartment building || 0.332 || 0.22 || 0.28 || 1.75 || 1.40 || 0.38 || 0.00 || 0.10
|---
|rowspan="3"| 2000's || Single family house || 0.24 || 0.17 || 0.24 || 1.40 || 1.00 || 0.30 || 0.05 || 0.13
|---
| Row house || 0.28 || 0.18 || 0.26 || 1.50 || 1.00 || 0.45 || 0.05 || 0.15
|---
| Apartment building || 0.28 || 0.18 || 0.26 || 1.50 || 1.40 || 0.55 || 0.00 || 0.10
|---
|rowspan="3"| 2010's || Single family house || 0.16 || 0.09 || 0.17 || 1.00 || 1.00 || 0.30 || 0.05 || 0.10
|---
| Row house || 0.16 || 0.09 || 0.17 || 1.00 || 1.00 || 0.50 || 0.05 || 0.15
|---
| Apartment building || 0.16 || 0.09 || 0.17 || 1.00 || 1.00 || 0.60 || 0.00 || 0.10
|}


== See also ==
== See also ==


{{Helsinki energy decision 2015}}
* [[Building stock in Helsinki metropolitan area]]
* [http://en.opasnet.org/en-opwiki/index.php?title=Building_stock_in_Helsinki&oldid=35248#Calculations Descriptions about the summary calculations on sheet Parameter Balance] (not needed any more).
* [http://en.opasnet.org/en-opwiki/index.php?title=Building_stock_in_Helsinki&oldid=35248#Calculations Descriptions about the summary calculations on sheet Parameter Balance] (not needed any more).
* [http://ptp.hel.fi/paikkatietohakemisto/?id=125 Rakennustietoruudukko pääkaupunkiseudulta] [https://www.hsy.fi/fi/asiantuntijalle/avoindata/Sivut/AvoinData.aspx?dataID=14]
* [http://ptp.hel.fi/paikkatietohakemisto/?id=125 Rakennustietoruudukko pääkaupunkiseudulta] [https://www.hsy.fi/fi/asiantuntijalle/avoindata/Sivut/AvoinData.aspx?dataID=14]
* [[Unit heat consumption of buildings in Finland]]
* [http://era17.fi/wp-content/uploads/2010/10/sitran_selvityksia_39.pdf Sitra, page 25]
* [http://www.teeparannus.fi/attachements/2010-12-21T11-54-1114846.pdf Tee parannus pages 8-9, also 40-45]
* [http://www.vtt.fi/inf/pdf/tiedotteet/2007/T2377.pdf VTT tiedotteet page 20-21, also 30, 80]
* [[Heating consumption of buildings]]
* [http://www.tut.fi/ee/Tutkimus/lammike.html Lämmitystapavalintojen kehitys]


== References ==
== References ==
<references/>


== Related files ==
== Related files ==
<!-- __OBI_TS:1431310608 -->
</noinclude>

Latest revision as of 08:47, 18 December 2018



During Decision analysis and risk management 2015 course, this page was used to collect student contributions. To see them, look at an archived version. The page has since been updated for its main use. Data in the archived tables was moved: Table 2. Energy parametres of buildings, Table 4. Energy sinks, Table 5. Changes in energy efficiency, Table 6. Important energy parametres. Tables 3, 7, and 8 did not contain data and were removed.


Question

What is the building stock in Helsinki and its projected future?

Answer

+ Show code

Rationale

This part contains the data needed for calculations about the building stock in Helsinki. It shows the different building and heating types in Helsinki, and how much and what kind of renovations are done for the existing building stock in a year, including how much and how old building stock is demolished. This data is used in further calculations in the model.

There is also some other important data that wasn't used in the model's calculations. These include more accurate renovation statistics for residential buildings, U-value changes for renovations and thermal transmittance of different parts of residential buildings. This data is found under Data not used.

Carbon neutral Helsinki 2035

+ Show code

Building stock

These tables are based on FACTA database classifications and their interpretation for assessments. This data is used for modelling. The data is large and can be seen from the Opasnet Base. Technical parts on this page are hidden for readability. Building types should match Energy use of buildings#Baseline energy consumption.



+ Show code

Construction and demolition

It is assumed that construction occurs at a constant rate so that there is an increase of 42% in 2050 compared to 2013. Energy efficiency comes from Energy use of buildings.

+ Show code

Fraction of houses demolished per year.

Demolition rate(% /a)
ObsAgeRate
100
2501
310001

+ Show code

Heating type conversion

The fraction of heating types in the building stock reflects the situation at the moment of construction and not currently. The heating type conversion corrects this by changing a fraction of heating methods to a different one at different timepoints. Cumulative fraction, other timepoints will be interpolated.

Yearly_heating_converted_factor(m2/m2)
ObsHeating_fromHeating_toTimeResult
1OilGeothermal20050
2OilGeothermal20150.5
3OilGeothermal20251

+ Show code

Renovations

Estimates from Laura Perez and Stephan Trüeb, unibas.ch N:\YMAL\Projects\Urgenche\WP9 Basel\Energy_scenarios_Basel_update.docx

Fraction of houses renovated per year(%)
ObsAgeResultDescription
100Estimates from Laura Perez and Stephan Trüeb
2200Assumption Result applies to buildings older than the value in the Age column.
3251
4301
5501
61001
710001

+ Show code

Popularity of renovation types(%)
ObsRenovationFractionDescription
1None0
2Windows65
3Technical systems30
4Sheath reform5
5General0

+ Show code

Locations of city areas

Locations of city areas (hidden for readability).



Data not used

This contains data that was not used in the model's calculations. This includes renovation rates, the rates of heat flowing out of buildings and total floor areas of multiple types of buildings in Helsinki. The floor area data is also found in the background data of this page, which was used in the model.

Effective floor area of buildings by building type.
Building Baseline 2020 2025 2050 Year of baseline Description
Residential 27884795 32472388 34890241 44069914 2014 Building stock of Helsinki area, 2014
Public 4537025 4764475 4945952 5855546 2014 Building stock of Helsinki area, 2014
Industrial 3277271 3306063 3360467 3640854 2014 Building stock of Helsinki area, 2014
Other 10861972 11406505 11840973 13806423 2014 Building stock of Helsinki area, 2014
Notes
  • Estimates were based on Siemens City Performance toolin seuraava kokous 2.2 and some derived calculations on BUILDING STOCK CALCULATION 2015.
  • How to get the numbers for the baseline floor area for residential, public, industrial and other: Residential floor area was named as residential together, public by summing the floor area of health care, education and common buildings, industrial buildings were as such and other buildings comprise of business, traffic, office and storage buildings.
  • Ref. Helsinki master plan for 2050: there are 860 000 citizens living in Helsinki (ref. www.yleiskaava.fi, visio2050); Residental buildings => fast growth
  • Prediction of citizen number in Helsinki in 2020, 2030, 2040 and 2050 was used for calculations (ref. Helsingin 30% päästövähennysselvitys).
  • Helsinki’s climate policy: 30% reduction in emissions: In 2010 the proportion of jobs in services and public sectors was 94%, and in industry 6%. In 2020 the proportion of jobs in services and public sectors is estimated to be 96%, and in industry 4%. Public and other buildings => between fast growth option and basic option, Industry=> Basic option
  • Prediction of job number in Helsinki in 2020, 2030, 2040 and 2050 was used for calculations (ref. Helsingin 30% päästövähennysselvitys).
  • Tables one and two The presentation of Tables 1 and 2


Technical notes:

Sheet 4_Input Buildings (Area Demand). Priority 1. Auxiliaries PPT. Absolute increase/decrease rate will be based on the inhabitants projected in time.
This is another list building types that was considered but rejected as too complex: Residential buildings, Government & public administration buildings, Commercial offices buildings, Data centers buildings, Education and K12 and universitiy buildings, Hospitals and healthcare buildings, Hotels and hospitality and leisure buildings, Exhibitions and fairs and halls buildings, Retail and stores and shops buildings, Warehouses & shopping mall buildings, Industrial buildings, Non residential buildings unspecified.
  • There was a problem with missing data. There is more than 400000 m^2 floor area that is missing; this is estimated from total area that is available for these buildings. For other buildings, there is more than 400000 m^2 total area missing from buildings where floor area is given. See statistical analysis [1]. This was corrected by inputation so that is floor area was missing, 0.8*total_area was used instead [2].
Renovations per year made in residental buildings owned by Helsinki city, by construction year of the buildings.[1]
Construction year Balcony glasses Windows Julkisivujen peruskorjaus Vesikattojen peruskojaus Lämmönvaihtimen uusiminen Patteriverkoston säätö Kylpyhuonekalusteiden vaihto Patteriventtiilien vaihto New balcony doors LTO-laitteen asennus Water consumption measurements
-20 0,0 % 1,1 % 1,1 % 1,1 % 1,1 % 1,1 % 1,1 % 1,1 % 1,1 % 1,1 % 1,1 %
21-25 0,0 % 10,3 % 1,2 % 11,1 % 10,3 % 10,3 % 1,2 % 10,3 % 1,2 % 10,3 % 10,3 %
26-30 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 9,5 % 0,0 %
31-35 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 0,0 % 0,0 %
36-40 0,0 % 0,0 % 0,0 % 0,0 % 4,2 % 4,2 % 0,0 % 0,0 % 0,0 % 4,2 % 0,0 %
41-45 0,0 % 16,7 % 0,0 % 16,7 % 16,7 % 16,7 % 0,0 % 16,7 % 0,0 % 16,7 % 16,7 %
46-50 0,0 % 5,2 % 0,0 % 7,4 % 7,4 % 7,4 % 0,0 % 7,4 % 0,0 % 5,2 % 5,2 %
51-55 0,0 % 11,3 % 0,0 % 8,8 % 8,8 % 8,8 % 0,0 % 8,8 % 0,0 % 8,8 % 16,2 %
56-60 0,0 % 5,4 % 0,0 % 4,9 % 6,2 % 7,1 % 0,0 % 6,2 % 4,5 % 4,5 % 5,4 %
61-65 0,0 % 1,5 % 1,3 % 0,8 % 2,9 % 2,4 % 1,0 % 2,4 % 0,9 % 0,9 % 2,9 %
66-70 0,6 % 2,9 % 1,2 % 2,8 % 1,4 % 2,3 % 1,1 % 1,1 % 0,1 % 1,1 % 1,1 %
71-75 3,2 % 3,1 % 3,4 % 2,9 % 3,1 % 2,6 % 0,2 % 1,1 % 0,2 % 0,2 % 0,2 %
76-80 0,1 % 2,7 % 0,1 % 0,7 % 2,0 % 1,7 % 1,1 % 1,2 % 0,2 % 0,4 % 0,2 %
81-85 1,0 % 2,8 % 0,7 % 2,3 % 3,3 % 4,8 % 3,5 % 0,0 % 0,0 % 0,0 % 0,8 %
86-90 0,0 % 1,3 % 0,0 % 2,1 % 6,1 % 1,6 % 0,7 % 1,8 % 0,3 % 0,3 % 1,0 %
91-95 0,6 % 0,3 % 0,0 % 3,9 % 8,6 % 1,9 % 5,1 % 0,8 % 0,2 % 0,0 % 1,3 %
96-00 0,1 % 0,0 % 0,0 % 0,6 % 1,2 % 1,0 % 1,5 % 1,0 % 0,0 % 0,0 % 4,2 %
01-05 2,9 % 0,0 % 0,0 % 0,0 % 1,2 % 1,0 % 0,0 % 1,0 % 0,0 % 0,0 % 0,7 %
06-10 1,7 % 0,0 % 0,0 % 0,0 % 0,5 % 0,5 % 0,0 % 0,5 % 0,0 % 0,0 % 0,0 %

←--#: . In the document there are similar tables for total renovations from 2010 onwards to years 2016, 2020 and 2050. --Heta (talk) 09:28, 16 June 2015 (UTC) (type: truth; paradigms: science: defence)

Toimenpiteiden vaikutukset yksittäisessä kohteessa ja toimenpiteisiin liittyviä huomautuksia.[1]
Action The feature in question Difference to before Unit Notes
Glass for balconies U-value for windows -0,3 W/m2,K Säästö 1-4% rakennustasolla
Changing the windows U-value for windows -1 W/m2,K Vanhoista osa kaksilasisia ja osa kolmilasisia. Uudes 1,0 W/m2,K tai alle
Julkisivun peruskorjaus U-value of walls -0,2 W/m2,K U-arvo puolitetaan eli n. 100 mm lisäeristys
Vesikattojen peruskorjaus Yläpohjan U-arvo -0,15 W/m2,K Oletetaan 50% lisäeristys U-arvo puoleen eli n. 100 mm lisäerstys
Balcony door change U-value of doors -0,5 W/m2,K Tiivistyminen tuo lisäsäästöä
Thermal transmittances of building components and air flow rates. Averaged values calculates from the detailed model are presented here.[2]
Construction decade Thermal transmittance factors for building components (W/m2K) Ventilation and leakage air rates (1/h)
Floor Roof Walls Windows Outdoors Supply air through the heat recovery unit Supply air bypassing the heat recovery unit Leakage air
Before 1980 Single family house 0.52 0.32 0.54 2.14 1.18 0.30 0.05 0.20
Row house 0.52 0.36 0.56 2.15 1.00 0.3 0.05 0.20
Apartment building 0.59 0.37 0.61 2.18 1.40 0.37 0.00 0.10
1980's Single family house 0.30 0.21 0.28 1.70 1.00 0.30 0.05 0.15
Row house 0.32 0.22 0.30 1.70 1.00 0.30 0.05 0.15
Apartment building 0.34 0.23 0.29 1.80 1.40 0.35 0.00 0.10
1990's Single family house 0.25 0.20 0.25 1.70 1.00 0.30 0.05 0.15
Row house 0.32 0.22 0.28 1.70 1.00 0.30 0.05 0.15
Apartment building 0.332 0.22 0.28 1.75 1.40 0.38 0.00 0.10
2000's Single family house 0.24 0.17 0.24 1.40 1.00 0.30 0.05 0.13
Row house 0.28 0.18 0.26 1.50 1.00 0.45 0.05 0.15
Apartment building 0.28 0.18 0.26 1.50 1.40 0.55 0.00 0.10
2010's Single family house 0.16 0.09 0.17 1.00 1.00 0.30 0.05 0.10
Row house 0.16 0.09 0.17 1.00 1.00 0.50 0.05 0.15
Apartment building 0.16 0.09 0.17 1.00 1.00 0.60 0.00 0.10

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

References

  1. 1.0 1.1 HAESS Final report, Tampere University of Technology, 2010
  2. MK Mattinen, J Heljo, J Vihola, A Kurvinen, S Lehtoranta, A Nissinen: Modeling and visualisation of residential sector energy consumption and greenhouse gas emissions

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