Building stock in Helsinki: Difference between revisions

Revision as of 11:28, 9 June 2015

This page is a knowledge crystal of subtype variable. The page identifier is Op_en7115
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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 To see them, look at an archived version.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.

----#: . 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

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.

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
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 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.

You have error(s) in your data:

You have invalid number of data cells in row 60
You have invalid number of data cells in row 64
You have invalid number of data cells in row 65

Locations of city areas(epsg:ETRS-TM35FIN)
Obs	Area number	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
61	402		6680780	382563
62	403		6677628	383571
63	404		6682460	385139
64	405
65	406
66	407		6679724	380771
67	408	0	0
68	409	0	0
69	410		6675708	386355
70	411	0	0
71	412	0	0
72	413	0	0
73	414		6668124	381699
74	415		6673356	380707
75	417	0	0
76	418	0	0
77	419	0	0
78	420		6676332	381987
79	421		6683244	385635
80	422	0	0
81	423	0	0
82	424	0	0
83	425	0	0
84	426	0	0
85	427	0	0
86	428	0	0
87	429		6678156	381315
88	430	0	0
89	431	0	0
90	432		6669116	385971
91	433	0	0
92	434	0	0
93	435	0	0
94	436		6676380	384755
95	437		6674156	383123
96	438	0	0
97	439	0	0
98	440	0	0
99	441	0	0
100	442	0	0
101	878	0	0
102	895	0	0

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)

# [[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

# 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.

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")

References

Related files

@@ Line 220: / Line 220: @@
 {{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="Locations of city areas" index="Area number,Area code,N" obs="E" unit="epsg:ETRS-TM35FIN">
 | KRU | 6672352 | 386953
 | KLU | 6672912 | 385513
 | KAA | 6671692 | 386153
 | KAM | 6671692 | 385233
 | PUN | 6671352 | 385113
 | E | 6670792 | 385573
 | UL | 6670372 | 385973
 | KAT | 6671752 | 387933
 | KAI | 6670632 | 387573
 | SÖR | 6674072 | 387453
 | KAL | 6673712 | 385933
 | ALP | 6673712 | 385933
 | ETU | 6672552 | 383733
 | TAK | 6673472 | 384753
 | ARI | 6673872 | 382813
 | RUS | 6676072 | 383793
 | PAS | 6676092 | 385093
 | LAA | 6675052 | 384433
 | MUS KOR | 6672592 | 388413
 | LÄN | 6670552 | 384033
 | HER | 6675432 | 387573
 | VAL | 6674912 | 386573
 | TOU | 6676272 | 387793
 | KUM | 6676192 | 386593
 | KÄP | 6676932 | 386153
 | KOS | 6677812 | 387393
 | VAN | 6676972 | 387933
 | OUL | 6678952 | 386533
 | HAA | 6678032 | 383353
 | MUN | 6674752 | 381733
 | LAU | 6670952 | 381593
 | KON | 6679852 | 380593
 | KAA | 6680752 | 382413
 | PAK | 6680392 | 385993
 | TUO | 6682492 | 385653
 | VII | 6676912 | 389553
 | PUK | 6680352 | 388573
 | MAL | 6680252 | 390153
 | TAP | 6682632 | 389273
 | SUUT | 6684152 | 389693
 | SUUR | 6682512 | 393113
 | KUL | 6674292 | 389193
 | HER | 6675272 | 391093
 | TAM | 6674252 | 392653
 | VAR | 6677372 | 394013
 | PIT | 6678172 | 381453
 | MEL | 6679192 | 394193
 | VAR | 6672872 | 393793
 | LAA | 6672072 | 391593
 | VIL | 6670412 | 395553
 | SAN | 6668612 | 392993
 | SUO | 6668912 | 388813
 | ULK | 6666992 | 395973
 | VUO | 6675252 | 397553
 | ÖST | 6681312 | 399633
-| SAL | 6679252 | 398513
+| SAL | 6679252 | 3985130
 | TAL | 6679452 | 400333
 | KAR | 6680652 | 401453
 | ULT | 6683472 | 400453
+| |
+| | 6680780 | 382563
+| | 6677628 | 383571
+| | 6682460 | 385139
+| |
+| |
+| | 6679724 | 380771
+|0|0|
+|0|0|
+| | 6675708 | 386355
+|0|0|
+|0|0|
+|0|0|
+| | 6668124 | 381699
+| | 6673356 | 380707
+|0|0|
+|0|0|
+|0|0|
+| | 6676332 | 381987
+| | 6683244 | 385635
+|0|0|
+|0|0|
+|0|0|
+|0|0|
+|0|0|
+|0|0|
+|0|0|
+| | 6678156 | 381315
+|0|0|
+|0|0|
+| | 6669116 | 385971
+|0|0|
+|0|0|
+|0|0|
+| | 6676380 | 384755
+| | 6674156 | 383123
+|0|0|
+|0|0|
+|0|0|
+|0|0|
+|0|0|
+|0|0|
+|0|0|
 </t2b>
 }}

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: Difference between revisions

Revision as of 11:28, 9 June 2015

Contents

Question

Answer

Rationale

Data

Building stock

Renovations

Emission locations

Calculations

See also

References

Related files

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