Building stock in Helsinki

This page is a knowledge crystal of subtype variable. The page identifier is Op_en7115
Moderator:Jouni (see all)
Upload data {{#opasnet_base_link:Op_en7115}}

During Decision analysis and risk management 2015 course, this page was used to collect student contributions. To see them, look at a previous version. The page has since been updated for its main use. Table 2. Existing situation of important energy parametres in the building stock was moved to Helsinki energy consumption#Energy parametres of buildings.

Question

What is the building stock in Helsinki and what are some of the key parameters of energy use?

Answer

Data

The structures of the tables are based on CyPT Excel file N:\YMAL\Projects\ilmastotiekartta\Helsinki Data Input Template - Building Data.xlsx.

Table 1. 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

Facta database building categories and heating types

These tables are based on FACTA database classifications and their interpretation for assessments.

----#: . The final classification to four types is different from what is shown in this table. We classified each building first to Residential, Industrial or Miscellaneous. From the Miscellaneous we substracted the city-owned buildings to get the classes "Other" and "Public". Public means only buildings owned by the city in this case. Industrial is only the class "Teollisuusrakennukset". Residential is "Asuinrakennukset" ja "Vapaa-ajanrakennukset. --Signatiu (talk) 09:33, 3 June 2015 (UTC) (type: truth; paradigms: science: comment) ←--#: . Good point. Can you update the table? --Jouni (talk) 14:25, 8 June 2015 (UTC) (type: truth; paradigms: science: defence)

Show details

Building types(-) Obs Building types in Facta Building 1 Ammatilliset oppilaitokset Public 2 Asuntolat, vanhusten palvelutalot, asuntolahotellit Other 3 Elokuvateatterit Public 4 Hotellit, motellit, matkustajakodit, kylpylähotellit Public 5 Kahden asunnon talot Residential 6 Kasvihuoneet Industrial 7 Kauppavarastot Industrial 8 Kirjastot Public 9 Kirkot, kappelit, luostarit, rukoushuoneet Public 10 Korkeakoulurakennukset Public 11 Kulkuneuvojen suoja- ja huoltorakennukset Industrial 12 Lasten päiväkodit Public 13 Lastenkodit, koulukodit Residential 14 Liike- ja tavaratalot, kauppakeskukset Other 15 Monitoimi- ja muut urheiluhallit Other 16 Museot, taidegalleriat Public 17 Muut erilliset pientalot Residential 18 Muut kerrostalot Residential 19 Muut liikenteen rakennukset Industrial 20 Muut palo- ja pelastustoimen rakennukset Other 21 Muut sairaalat Other 22 Muut teollisuuden tuotantorakennukset Industrial 23 Muut terveydenhoitorakennukset Other 24 Muut urheilu- ja kuntoilurakennukset Other 25 Muut uskonnollisten yhteisöjen rakennukset Public 26 Muut varastorakennukset Other 27 Myymälärakennukset Other 28 Peruskoulut, lukiot ja muut Public 29 Pysäköintitalot Other 30 Rautatie- ja linja-autoasemat, lento- ja satamaterminaalit Public 31 Ravintolat, ruokalat ja baarit Public 32 Rivitalot Residential 33 Saunarakennukset Other 34 Seurakuntatalot Public 35 Talousrakennukset Other 36 Teollisuus- ja pienteollisuustalot Industrial 37 Teollisuushallit Industrial 38 Toimistorakennukset Other 39 Tutkimuslaitosrakennukset Other 40 Vapaa-ajan asunnot Residential 41 Voimalaitosrakennukset Industrial 42 Väestönsuojat Other 43 Yhden asunnon talot Residential 44 Muualla luokittelemattomat opetusrakennukset Other 45 Muut kokoontumisrakennukset Public 46 Muut rakennukset Other 47 Muut sosiaalitoimen rakennukset Public 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 Public 55 Terveyskeskukset Public 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 Other 62 Kehitysvammaisten hoitolaitokset Residential 63 Loma- lepo- ja virkistyskodit Other 64 Eläinsuojat, ravihevostallit, maneesit Other 65 Other 66 Paloasemat Other 67 Vuokrattavat lomamökit ja osakkeet (liiketoiminnallisesti) Other 68 Teatterit, konsertti- ja kongressitalot, oopperat Public 69 Jäähallit Public 70 Keskussairaalat Other 71 Näyttelyhallit Other 72 Terveydenhoidon erityislaitokset (mm. kuntoutuslaitokset) Other 73 Viljankuivaamot ja viljan säilytysrakennukset, siilot Industrial 74 Uimahallit Public 75 Vankilat Other 76 Ketjutalot Residential 77 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

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)

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)

Emission locations

Where and how do the emissions of heating take place?

Emission locations(-)

Obs	Heating	Emission site	Emission height	Dummy
1	District	4056	High	4056 is the postal code of the heat and power plant IWB, Hagenaustrasse 40/70 4056 Basel.
2	Long-distance heating	4056	High
3	Electricity	4056	High
4	Geothermal	4056	High
5	Centrifuge, hydro-extractor	4056	High
6	Heating oil	At site of consumption	Ground
7	Wood	At site of consumption	Ground
8	Gas	At site of consumption	Ground
9	Coal	At site of consumption	Ground
10	Solar heater/ collector	At site of consumption	Ground
11	No energy source	At site of consumption	Ground
12	Other sources	At site of consumption	Ground

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

Locations of postal codes(epsg:ETRS-TM35FIN)

Obs	Area number	Area code	N	E
1	1	KRU	6672352	386953
2	2	KLU	6672912	385513
3	3	KAA	6671692	386153
4	4	KAM	6671692	385233
5	5	PUN	6671352	385113
6	6	E	6670792	385573
7	7	UL	6670372	385973
8	8	KAT	6671752	387933
9	9	KAI	6670632	387573
10	10	SÖR	6674072	387453
11	11	KAL	6673712	385933
12	12	ALP	6673712	385933
13	13	ETU	6672552	383733
14	14	TAK	6673472	384753
15	15	ARI	6673872	382813
16	16	RUS	6676072	383793
17	17	PAS	6676092	385093
18	18	LAA	6675052	384433
19	19	MUS KOR	6672592	388413
20	20	LÄN	6670552	384033
21	21	HER	6675432	387573
22	22	VAL	6674912	386573
23	23	TOU	6676272	387793
24	24	KUM	6676192	386593
25	25	KÄP	6676932	386153
26	26	KOS	6677812	387393
27	27	VAN	6676972	387933
28	28	OUL	6678952	386533
29	29	HAA	6678032	383353
30	30	MUN	6674752	381733
31	31	LAU	6670952	381593
32	32	KON	6679852	380593
33	33	KAA	6680752	382413
34	34	PAK	6680392	385993
35	35	TUO	6682492	385653
36	36	VII	6676912	389553
37	37	PUK	6680352	388573
38	38	MAL	6680252	390153
39	39	TAP	6682632	389273
40	40	SUUT	6684152	389693
41	41	SUUR	6682512	393113
42	42	KUL	6674292	389193
43	43	HER	6675272	391093
44	44	TAM	6674252	392653
45	45	VAR	6677372	394013
46	46	PIT	6678172	381453
47	47	MEL	6679192	394193
48	48	VAR	6672872	393793
49	49	LAA	6672072	391593
50	50	VIL	6670412	395553
51	51	SAN	6668612	392993
52	52	SUO	6668912	388813
53	53	ULK	6666992	395973
54	54	VUO	6675252	397553
55	55	ÖST	6681312	399633
56	56	SAL	6679252	398513
57	57	TAL	6679452	400333
58	58	KAR	6680652	401453
59	59	ULT	6683472	400453

Calculations

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

+ Show code- Hide code library(OpasnetUtils) # [[Buildings in Basel]], building stock, locations by postal codes (in A Swiss 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" # Construction rate is assumed to be 2 % /a from the year 2010 building stock. changeBuildings <- stockBuildings changeBuildings@name <- "changeBuildings" colnames(changeBuildings@data)[colnames(changeBuildings@data) == "stockBuildingsResult"] <- "changeBuildingsResult" changeBuildings@data$changeBuildingsResult <- changeBuildings@data$changeBuildingsResult * 0.02 changeBuildings@data$Time <- NULL changeBuildings@data <- merge(changeBuildings@data, data.frame(Time = 2015 + 0:7 * 5)) # Geolocations of the buildings for emission calculations. OLD VERSION # emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7115.locations_of_postal_codes") # colnames(emissionLocations@data)[colnames(emissionLocations@data) == "emissionLocationsResult"] <- "Y" # emissionLocations@data$emissionLocationsResult <- 1 emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7115", subset = "Emission locations") colnames(emissionLocations@data) <- gsub("[ \\.]", "_", colnames(emissionLocations@data)) emissionLocations@data$emissionLocationsResult <- 1 heatingShares <- 1 # This is already in the Basel data. renovationRate <- Ovariable('renovationRate', dependencies = data.frame(Name = "dummy"), formula = function(...) { temp <- tidy(opbase.data('Op_en7115', subset = 'Fraction of houses renovated per year')) temp$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$renovationRateResult / 100 out <- Ovariable("renovationRate", output = out, marginals = c(TRUE, FALSE)) return(out) } ) renovationShares <- Ovariable("renovationShares", dependencies = data.frame(Name = "dummy"), formula = function(...) { out <- Ovariable("raw", ddata = 'Op_en7115', subset = 'Popularity of renovation types') out <- findrest((out), cols = "Renovation", total = 100) / 100 renovationyear <- Ovariable("renovationyear", data = data.frame( Startyear = factor(c(2030)), Result = 1 )) out <- out * renovationyear # renovation shares repeated for every potential renovation year. out@output$Renovation <- factor(out@output$Renovation, levels = c( "None", "General", "Windows", "Technical systems", "Sheath reform" ), ordered = TRUE) return(out) } ) objects.store( stockBuildings, # Current building stock changeBuildings, # Building stock change per year emissionLocations, # Locations of buildings and emissions heatingShares, # Heating types of current buildings renovationRate, # Percentage of renovations per year renovationShares # Fraction of renovation type when renovation is done. From [[Building stock in Kuopio]] ) cat("Objects stockBuildings, changeBuildings, emissionLocations, heatingShares, renovationRate, renovationShares 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