Building stock in Helsinki

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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 To see them, look at an archived version.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

• 

  Current building stock in Helsinki by heating type.

• 

  Projected building stock based on 2015 data and urban plans.

+ Show code- Hide code ## 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*" )"))

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- Hide code # 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/")

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.

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Building types(-) Obs Building types in Facta Building Number of the class 1 Yhden asunnon talot Detached and semi-detached houses 11 2 Kahden asunnon talot Detached and semi-detached houses 12 3 Muut erilliset pientalot Detached and semi-detached houses 13 4 Rivitalot Attached houses 21 5 Ketjutalot Attached houses 22 6 Luhtitalot Blocks of flats 32 7 Muut asuinkerrostalot Blocks of flats 39 8 Vapaa-ajan asuinrakennukset Free-time residential buildings 41 9 Myymälähallit Commercial 111 10 Liike- ja tavaratalot, kauppakeskukset Commercial 112 11 Myymälärakennukset Commercial 119 12 Hotellit, motellit, matkustajakodit, kylpylähotellit Commercial 121 13 Loma-, lepo- ja virkistyskodit Commercial 123 14 Vuokrattavat lomamökit ja osakkeet (liiketoiminnallisesti) Commercial 124 15 Muut majoitusliikerakennukset Commercial 129 16 Asuntolat, vanhusten palvelutalot, asuntolahotellit Commercial 131 17 Muut asuntolarakennukset Commercial 139 18 Ravintolat, ruokalat ja baarit Commercial 141 19 Toimistorakennukset Offices 15 20 Toimistorakennukset Offices 151 21 Rautatie- ja linja-autoasemat, lento- ja satamaterminaalit Transport and communications buildings 161 22 Kulkuneuvojen suoja- ja huoltorakennukset Transport and communications buildings 162 23 Pysäköintitalot Transport and communications buildings 163 24 Tietoliikenteen rakennukset Transport and communications buildings 164 25 Muut liikenteen rakennukset Transport and communications buildings 169 26 Keskussairaalat Buildings for institutional care 211 27 Muut sairaalat Buildings for institutional care 213 28 Terveyskeskukset Buildings for institutional care 214 29 Terveydenhuollon erityislaitokset Buildings for institutional care 215 30 Muut terveydenhuoltorakennukset Buildings for institutional care 219 31 Vanhainkodit Buildings for institutional care 221 32 Lasten- ja koulukodit Buildings for institutional care 222 33 Kehitysvammaisten hoitolaitokset Buildings for institutional care 223 34 Muut huoltolaitosrakennukset Buildings for institutional care 229 35 Lasten päiväkodit Buildings for institutional care 231 36 Muualla luokittelemattomat sosiaalitoimen rakennukset Buildings for institutional care 239 37 Vankilat Buildings for institutional care 241 38 Teatterit, konsertti- ja kongressitalot, oopperat Assembly buildings 311 39 Elokuvateatterit Assembly buildings 312 40 Kirjastot ja arkistot Assembly buildings 322 41 Museot ja taidegalleriat Assembly buildings 323 42 Näyttelyhallit Assembly buildings 324 43 Seurain-, nuoriso- yms. talot Assembly buildings 331 44 Kirkot, kappelit, luostarit, rukoushuoneet Assembly buildings 341 45 Seurakuntatalot Assembly buildings 342 46 Muut uskonnollisten yhteisöjen rakennukset Assembly buildings 349 47 Jäähallit Assembly buildings 351 48 Uimahallit Assembly buildings 352 49 Tennis-, squash- ja sulkapallohallit Assembly buildings 353 50 Monitoimihallit ja muut urheiluhallit Assembly buildings 354 51 Muut urheilu- ja kuntoilurakennukset Assembly buildings 359 52 Muut kokoontumisrakennukset Assembly buildings 369 53 Peruskoulut, lukiot ja muut Educational buildings 511 54 Ammatillisten oppilaitosten rakennukset Educational buildings 521 55 Korkeakoulurakennukset Educational buildings 531 56 Tutkimuslaitosrakennukset Educational buildings 532 57 Järjestöjen, liittojen, työnantajien yms. opetusrakennukset Educational buildings 541 58 Muualla luokittelemattomat opetusrakennukset Educational buildings 549 59 Voimalaitosrakennukset Industrial buildings 611 60 Yhdyskuntatekniikan rakennukset Industrial buildings 613 61 Teollisuushallit Industrial buildings 691 62 Teollisuus- ja pienteollisuustalot Industrial buildings 692 63 Muut teollisuuden tuotantorakennukset Industrial buildings 699 64 Teollisuusvarastot Warehouses 711 65 Kauppavarastot Warehouses 712 66 Muut varastorakennukset Warehouses 719 67 Paloasemat Fire fighting and rescue service buildings 721 68 Väestönsuojat Fire fighting and rescue service buildings 722 69 Muut palo- ja pelastustoimen rakennukset Fire fighting and rescue service buildings 729 70 Navetat, sikalat, kanalat yms. Agricultural buildings 811 71 Eläinsuojat, ravihevostallit, maneesit Agricultural buildings 819 72 Viljankuivaamot ja viljan säilytysrakennukset Agricultural buildings 891 73 Kasvihuoneet Agricultural buildings 892 74 Turkistarhat Agricultural buildings 893 75 Muut maa-, metsä- ja kalatalouden rakennukset Agricultural buildings 899 76 Saunarakennukset Other buildings 931 77 Talousrakennukset Other buildings 941 78 Muualla luokittelemattomat rakennukset Other buildings 999 79 Ammatilliset oppilaitokset Educational buildings 80 Kirjastot Other buildings 81 Lastenkodit, koulukodit Other buildings 82 Loma- lepo- ja virkistyskodit Other buildings 83 Monitoimi- ja muut urheiluhallit Other buildings 84 Museot, taidegalleriat Assembly buildings 85 Muut kerrostalot Blocks of flats 86 Muut majoitusrakennukset Commercial 87 Muut terveydenhoitorakennukset Buildings for institutional care 88 Terveydenhoidon erityislaitokset (mm. kuntoutuslaitokset) Buildings for institutional care 89 Vapaa-ajan asunnot Free-time residential buildings 90 Viljankuivaamot ja viljan säilytysrakennukset, siilot Warehouses ----#: . Viimeiset 12 riviä (ilman numeroa) ovat tyyppejä jotka ovat datassa (tai ainakin vanhassa taulukossa) mutta puuttuvat Sonjan luokittelusta. --Jouni (talk) 12:57, 24 August 2015 (UTC) (type: truth; paradigms: science: comment) 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. Reference for the classification: http://www.stat.fi/meta/luokitukset/rakennus/001-1994/koko_luokitus.html Heating types(-) Obs Heating types in Facta Heating 1 Other 2 Kauko- tai aluelämpö District heating 3 Kevyt polttoöljy Light oil 4 Kivihiili, koksi tms. Coal 5 Maalämpö tms. Geothermal 6 Puu Wood 7 Raskas polttoöljy Fuel oil 8 Sähkö Electricity 9 Kaasu Gas 10 Muu Other The structures of the tables are based on CyPT Excel file N:\YMAL\Projects\ilmastotiekartta\Helsinki Data Input Template - Building Data.xlsx.

+ Show code- Hide code 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" heat <- tidy(opbase.data("Op_en7115.heating_types")) colnames(heat)[colnames(heat) == "Result"] <- "Heating" ###################### # Korjaus ######################## temp <- as.character(heat$Heating) temp[temp == "District heating"] <- "District" temp[temp == "Light oil"] <- "Oil" temp[temp == "Fuel oil"] <- "Oil" heat$Heating <- temp ######################################## 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")

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- Hide code # 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")

Fraction of houses demolished per year.

Demolition rate(% /a)

Obs	Age	Rate
1	0	0
2	50	1
3	1000	1

+ Show code- Hide code # 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")

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)

Obs	Heating_from	Heating_to	Time	Result
1	Oil	Geothermal	2005	0
2	Oil	Geothermal	2015	0.5
3	Oil	Geothermal	2025	1

+ Show code- Hide code library(OpasnetUtils) 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" 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$Heating_to <- NULL out@output <- rbind(out1@output, out@output) #sum(out$Result) #nrow(out1@output)*2 - nrow(out@output) return(out) } ) objects.store(heatTypeConversion) cat("Ovariable heatTypeConversionstored.\n")

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(%)

Obs	Age	Result	Description
1	0	0	Estimates from Laura Perez and Stephan Trüeb
2	20	0	Assumption Result applies to buildings older than the value in the Age column.
3	25	1
4	30	1
5	50	1
6	100	1
7	1000	1

+ Show code- Hide code 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) } ) objects.store(renovationRate) cat("Object renovationRate stored.\n")

Popularity of renovation types(%)

Obs	Renovation	Fraction	Description
1	None	0
2	Windows	65
3	Technical systems	30
4	Sheath reform	5
5	General	0

+ Show code- Hide code library(OpasnetUtils) 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 renovationyear <- Ovariable("renovationyear", data = data.frame( Obsyear = factor(c(2015, 2025, 2035, 2045, 2055, 2065)), Result = 1 )) out <- out * renovationyear # renovation shares repeated for every potential renovation year. out@output$Renovation <- factor(out@output$Renovation, levels = c( "None", "General", "Windows", "Technical systems", "Sheath reform" ), ordered = TRUE) return(out) } ) objects.store( renovationShares # Fraction of renovation type when renovation is done. ) cat("Objects renovationShares stored.\n")

Locations of city areas

Locations of city areas (hidden for readability).

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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. Locations of city areas(epsg:ETRS-TM35FIN) Obs City_area Area code N E 1 001 KRU 6672352 386953 2 002 KLU 6672912 385513 3 003 KAA 6671692 386153 4 004 KAM 6671692 385233 5 005 PUN 6671352 385113 6 006 E 6670792 385573 7 007 UL 6670372 385973 8 008 KAT 6671752 387933 9 009 KAI 6670632 387573 10 010 SÖR 6674072 387453 11 011 KAL 6673712 385933 12 012 ALP 6673712 385933 13 013 ETU 6672552 383733 14 014 TAK 6673472 384753 15 015 ARI 6673872 382813 16 016 RUS 6676072 383793 17 017 PAS 6676092 385093 18 018 LAA 6675052 384433 19 019 MUSKOR 6672592 388413 20 020 LÄN 6670552 384033 21 021 HER 6675432 387573 22 022 VAL 6674912 386573 23 023 TOU 6676272 387793 24 024 KUM 6676192 386593 25 025 KÄP 6676932 386153 26 026 KOS 6677812 387393 27 027 VAN 6676972 387933 28 028 OUL 6678952 386533 29 029 HAA 6678032 383353 30 030 MUN 6674752 381733 31 031 LAU 6670952 381593 32 032 KON 6679852 380593 33 033 KAA 6680752 382413 34 034 PAK 6680392 385993 35 035 TUO 6682492 385653 36 036 VII 6676912 389553 37 037 PUK 6680352 388573 38 038 MAL 6680252 390153 39 039 TAP 6682632 389273 40 040 SUUT 6684152 389693 41 041 SUUR 6682512 393113 42 042 KUL 6674292 389193 43 043 HER 6675272 391093 44 044 TAM 6674252 392653 45 045 VAR 6677372 394013 46 046 PIT 6678172 381453 47 047 MEL 6679192 394193 48 048 VAR 6672872 393793 49 049 LAA 6672072 391593 50 050 VIL 6670412 395553 51 051 SAN 6668612 392993 52 052 SUO 6668912 388813 53 053 ULK 6666992 395973 54 054 VUO 6675252 397553 55 055 ÖST 6681312 399633 56 056 SAL 6679252 3985130 57 057 TAL 6679452 400333 58 058 KAR 6680652 401453 59 059 ULT 6683472 400453 60 401 6684572 390899 61 402 6680780 382563 62 403 6677628 383571 63 404 6682460 385139 64 405 6675756 391603 65 406 6679980 391731 66 407 6679724 380771 67 408 6676828 387779 68 409 6673900 389379 69 410 6675708 386355 70 411 6678556 387027 71 412 6671724 392691 72 413 6674156 391539 73 414 6668124 381699 74 415 6673356 380707 75 417 6677388 399763 76 418 6680476 390883 77 419 6678956 395043 78 420 6676332 381987 79 421 6683244 385635 80 422 6678972 386803 81 423 6680252 385939 82 424 6679740 388819 83 425 6669196 391683 84 426 6683676 388515 85 427 6684108 389843 86 428 6678156 381315 87 429 6682524 391027 88 430 6672204 391459 89 431 6681740 386131 90 432 6672924 385635 91 433 6677196 394211 92 434 6678188 390147 93 435 6676380 397027 94 436 6676380 384755 95 437 6674156 383123 96 438 6682316 398147 97 439 6679612 400499 98 440 6684124 402355 99 441 6679724 399027 100 442 6682076 400499 101 878 6678380 390771 102 895 6678380 390771

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.

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.

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.

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

• Building stock in Helsinki metropolitan area
• Descriptions about the summary calculations on sheet Parameter Balance (not needed any more).
• Rakennustietoruudukko pääkaupunkiseudulta [3]
• Unit heat consumption of buildings in Finland
• Sitra, page 25
• Tee parannus pages 8-9, also 40-45
• VTT tiedotteet page 20-21, also 30, 80
• Heating consumption of buildings
• Lämmitystapavalintojen kehitys