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 To see them, look at an archived version.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

+ Show code- Hide code library(OpasnetUtils) library(ggplot2) objects.latest("Op_en7115", code_name = "initiate") 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

Data

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 {{#opasnet_base_link:Op_en7115}}

(Stock details). Technical parts on this page are hidden for readability.

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Building types(-) Obs Building types in Facta Building 1 Ammatilliset oppilaitokset City-owned 2 Asuntolat, vanhusten palvelutalot, asuntolahotellit Residental 3 Elokuvateatterit Other 4 Hotellit, motellit, matkustajakodit, kylpylähotellit Other 5 Kahden asunnon talot Residential 6 Kasvihuoneet Industrial 7 Kauppavarastot Industrial 8 Kirjastot City-owned 9 Kirkot, kappelit, luostarit, rukoushuoneet Other 10 Korkeakoulurakennukset City-owned 11 Kulkuneuvojen suoja- ja huoltorakennukset Industrial 12 Lasten päiväkodit City-owned 13 Lastenkodit, koulukodit Residential 14 Liike- ja tavaratalot, kauppakeskukset Other 15 Monitoimi- ja muut urheiluhallit Other 16 Museot, taidegalleriat City-owned 17 Muut erilliset pientalot Residential 18 Muut kerrostalot Residential 19 Muut liikenteen rakennukset Industrial 20 Muut palo- ja pelastustoimen rakennukset Other 21 Muut sairaalat City-owned 22 Muut teollisuuden tuotantorakennukset Industrial 23 Muut terveydenhoitorakennukset Other/City-owned 24 Muut urheilu- ja kuntoilurakennukset Other 25 Muut uskonnollisten yhteisöjen rakennukset Other 26 Muut varastorakennukset Other 27 Myymälärakennukset Other 28 Peruskoulut, lukiot ja muut City-owned 29 Pysäköintitalot Other 30 Rautatie- ja linja-autoasemat, lento- ja satamaterminaalit Other 31 Ravintolat, ruokalat ja baarit Other 32 Rivitalot Residential 33 Saunarakennukset Other 34 Seurakuntatalot Other 35 Talousrakennukset Industrial 36 Teollisuus- ja pienteollisuustalot Industrial 37 Teollisuushallit Industrial 38 Toimistorakennukset Other/City-owned 39 Tutkimuslaitosrakennukset Other/City-owned 40 Vapaa-ajan asunnot Residential 41 Voimalaitosrakennukset Industrial 42 Väestönsuojat City-owned 43 Yhden asunnon talot Residential 44 Muualla luokittelemattomat opetusrakennukset Other 45 Muut kokoontumisrakennukset Other 46 Muut rakennukset Other 47 Muut sosiaalitoimen rakennukset City-owned 48 Myymälähallit Other 49 Teollisuusvarastot Industrial 50 Yhdyskuntatekniikan rakennukset Other 51 Järjestöjen, liittojen, työnantajien yms. opetusrakennukset Other 52 Muut huoltolaitosrakennukset Industrial 53 Tietoliikenteen rakennukset Industrial 54 Seurain-, nuoriso- yms. talot City-owned 55 Terveyskeskukset City-owned 56 Vanhainkodit Residential 57 Luhtitalot Residential 58 Navetat, sikalat, kanalat yms. Industrial 59 Muut majoitusliikerakennukset Other 60 Muut majoitusrakennukset Other 61 Muut maa-, metsä- ja kalatalouden rakennukset Industrial 62 Kehitysvammaisten hoitolaitokset Residential 63 Loma- lepo- ja virkistyskodit Other 64 Eläinsuojat, ravihevostallit, maneesit Other 65 Paloasemat Other 66 Vuokrattavat lomamökit ja osakkeet (liiketoiminnallisesti) Other 67 Teatterit, konsertti- ja kongressitalot, oopperat Other 68 Jäähallit City-owned? 69 Keskussairaalat City-owned 70 Näyttelyhallit Other 71 Terveydenhoidon erityislaitokset (mm. kuntoutuslaitokset) City-owned 72 Viljankuivaamot ja viljan säilytysrakennukset, siilot Industrial 73 Uimahallit Other 74 Vankilat Other 75 Ketjutalot Residential 76 Tennis-, squash- ja sulkapallohallit Other 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.

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
• Estimates for floor area development are asked from Alpo Tani (KSV) and Olli-Pekka Pietiläinen (SYKE) as well (26.5.). No answers yet.

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.

+ Show code- Hide code library(OpasnetUtils) # [[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", "Result" # Rakennusala m2 )] colnames(dat) <- c("City_area", "Time", "Building types in Facta", "Heating types in Facta", "stockBuildingsResult") dat$Time <- as.numeric(substring(dat$Time, nchar(as.character(dat$Time)) - 3)) dat$Time <- as.numeric(as.character((cut(dat$Time, breaks = c(0, 1885 + 0:26*5), labels = as.character(1885 + 0:26*5))))) dat$stockBuildingsResult <- as.numeric(as.character(dat$stockBuildingsResult)) build <- tidy(opbase.data("Op_en7115.building_types")) colnames(build)[colnames(build) == "Result"] <- "Building" heat <- tidy(opbase.data("Op_en7115.heating_types")) colnames(heat)[colnames(heat) == "Result"] <- "Heating" ###################### # Korjaus ######################## temp <- as.character(heat$Heating) temp[temp == "District heating"] <- "District" temp[temp == "Light oil"] <- "Oil" temp[temp == "Fuel oil"] <- "Oil" heat$Heating <- temp ######################################## dat <- merge(merge(dat, build), heat)[c("City_area", "Time", "Building", "Heating", "stockBuildingsResult")] temp <- aggregate(dat["stockBuildingsResult"], by = dat[c("Time", "Building", "Heating")], FUN =sum) temp <- temp[!is.na(temp$stockBuildingsResult) , ] stockBuildings <- Ovariable("stockBuildings", data = temp) # Replace Basel building data with Helsinki data objects.store(stockBuildings) cat("Ovariable stockBuildings stored.\n")

Construction and demolition

It is assumed that construction equals 2 % of the existing building stock per year. All new buildings are assumed to be energy efficiency "New". It is assumed that no demolition occurs.

+ Show code- Hide code library(OpasnetUtils) # Construction rate is assumed to be 2 % /a from the year 2010 building stock. # changeBuildings is defined as in Basel but only created now to match Helsinki data. objects.latest("Op_en7115", code_name = "stockBuildings") # [[Building stock in Helsinki]] changeBuildings <- stockBuildings changeBuildings@name <- "changeBuildings" colnames(changeBuildings@data)[colnames(changeBuildings@data) == "stockBuildingsResult"] <- "changeBuildingsResult" changeBuildings@data$changeBuildingsResult <- changeBuildings@data$changeBuildingsResult * 0.02 changeBuildings@data$Time <- NULL changeBuildings@data$Efficiency <- "New" changeBuildings@data <- merge(changeBuildings@data, data.frame(Time = 2015 + 0:7 * 5)) objects.store(changeBuildings) cat("Ovariable changeBuildings stored.\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

⇤--#: . Basel's data --Heta (talk) 12:30, 4 June 2015 (UTC) (type: truth; paradigms: science: attack)

+ 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, marginals = 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

⇤--#: . Basel's data --Heta (talk) 12:30, 4 June 2015 (UTC) (type: truth; paradigms: science: attack)

+ 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( Startyear = factor(c(2030)), 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).

Show details

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 MUS KOR 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

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

+ Show code- Hide code library(OpasnetUtils) emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7115", subset = "Emission locations") colnames(emissionLocations@data) <- gsub("[ \\.]", "_", colnames(emissionLocations@data)) emissionLocations@data$emissionLocationsResult <- 1 heatingShares <- 1 # This is already in the Basel data. objects.store(emissionLocations, heatingShares) cat("Objects emissionsLocations, heatingShares stored.\n")

See also

• Descriptions about the summary calculations on sheet Parameter Balance (not needed any more).
• Rakennustietoruudukko pääkaupunkiseudulta [1]
• 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