Building stock in Helsinki

This page is a knowledge crystal of subtype variable. The page identifier is Op_en7115
Moderator:Jouni (see all)

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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 To see them, look at an archived version.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.

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 details

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
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).

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	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.^[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)
Construction decade		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

References

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

Related files

[uarvo-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

[1]

[2]

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

Building stock in Helsinki

Contents

Question

Answer

Rationale

Building stock

Construction and demolition

Heating type conversion

Renovations

Locations of city areas

Data not used

See also

References

Related files

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