Building stock in Helsinki

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Question

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

Answer

Data

• Unit heat consumption of buildings in Finland
• http://era17.fi/wp-content/uploads/2010/10/sitran_selvityksia_39.pdf page 25
• http://www.teeparannus.fi/attachements/2010-12-21T11-54-1114846.pdf pages 8-9, also 40-45
• http://www.vtt.fi/inf/pdf/tiedotteet/2007/T2377.pdf page 20-21, also 30, 80
• Heating consumption of buildings

Building stock

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.

Effective floor area of buildings(m2)

Obs	Building	Baseline	2020	2025	2050	Year of baseline	Type of data	Quality	Description
1	Residential	27884795	32472388	34890241	44069914	2014			Building stock of Helsinki area, 2014
2	Public	4537025	4764475	4945952	5855546	2014			Building stock of Helsinki area, 2014
3	Industrial	3277271	3306063	3360467	3640854	2014			Building stock of Helsinki area, 2014
4	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

Types of data used in CyPT:

• Average, City
• Example, city
• Average, city close by
• Example, city close by
• Average, national
• Example, country

Qualities of data used in CyPT

• Statistics
• Extrapolated
• Calculated from statistics
• Calculated from stat. inc. assumptions

Table 2. Existing situation of important energy parametres in the building stock.

Existing situation of total stock(%)

Obs	Property	Residential	Public	Industrial	Other	Description
1	Wall insulation	17				Default building data - Helsinki.xlsx and CyPT Data collection - To Jouni.pptx
2	High efficient glazing	35				Energy performance class A in building automation and www.siemens.com
3	Efficient lighting in baseline	1.4				Default building data - Helsinki.xlsx
4	Demand oriented lighting	26.9				Default building data - Helsinki.xlsx
5	Building Efficiency Monitoring
6	Building Remote Monitoring
7	Building Performance Optimization
8	Demand controlled ventilation	16.2 - 22.4				Default building data - Helsinki.xlsx: Non-residential, cell C35
9	Heat and Cold Recovery in ventilation	17.8
10	Efficient Motors
11	Building Automation BACS Class C
12	Building Automation BACS Class B
13	Building Automation BACS Class A
14	Room Automation HVAC	30				www.siemens.com
15	Room Automation HVAC + lighting
16	Building Automation HVAC + lighting + blinds	60				www.siemens.com

Description of parametres:

• Wall Insulation: Building outside walls with a thermal transmission coefficient < 0.5(W/(m²*k)
• High efficient glazing: Building outside windows with a thermal transmission coefficient < 1(W/(m²*k
• Efficient lighting: Building with lighting technology with an efficiency of >75lumen/Watt
• Demand oriented lighting: Buildings with a lighting regulation that controls the presents of users and/or the incident sun light to dim or shut off the lighting to save electricity.
• Efficient Motors: Buildings that are equipped with pressure regulated variable speed drives for heating circulation pumps or ventilation drives.
• Building Remote Monitoring: Buildings with metering and remote evaluation of energy demand, so that an professional engineer can evaluate the energy demand and the need for maintenance and improvement measures
• Building Performance Optimization: Buildings with an optimization contract, where an professional engineer can start maintenance and improvement measures
• Demand controlled ventilation: Buildings with a ventilation regulation that controls the air quality (e.B.CO2) to slow down or shut off the ventilation when it is not needed to save electricity.
• Heat and Cold Recovery in ventilation: Buildings where the heat/cold of the exhaust air is recovered by a heat exchanger to precondition the fresh air to save energy for cooling/heating.
• Room Automation HVAC: Buildings where the heating/ventilation/Air Condition demand is regulated to the demand of every single room to prevent energy demand for not used rooms.

Notes

Data from:

• CyPT Data collection - To Jouni
• Default Building Data - Helsinki
• Energy performance class A in building automation www.siemens.com [1]

The numbers found are not reliable but most of the high technology buildings such as insulated walls, windows.... are started to build from 10 to 15 years ago, so if we could find the building area in the year 2000, we could subtract that from the building area in Helsinki at the moment and get the building area which is built during these 10-15 years and they are high tech building areas with regard to 2 % renovation rate and rebuilding which is on going every year which should be added to the total amount.

Technical notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries PPT.

Energy demand

Table 3. Total energy demand by energy type and building type.

Total energy demand by type(kWh /m2 /a)

Obs	Energy type	Residential	Public	Industrial	Other	Description
1	Electricity
2	Cooling
3	Heating

Notes

Heating includes warm water.

----#: . See Helsinki energy decision 2015. More of relevant data may be collected there. --Jouni (talk) 14:16, 19 May 2015 (UTC) (type: truth; paradigms: science: comment)

----#: . These numbers could be estimated from the Building model when teh Facta data is applied. We are planning to do that in early June, 2015. --Jouni (talk) 08:22, 21 May 2015 (UTC) (type: truth; paradigms: science: comment)

Technical notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries PPT.

Table 4. Shares of different energy sinks by building type.

Share of energy demand by use type(kWh /m2 /a)

Obs	Energy type	Use	Residential	Other	Public	Industry	Description	Auxiliaries
1	Cooling	Infiltration	0.0	0.0	0.0	0.0		PPT
2	Cooling	Ventilation	0.0	0.0	0.0	9.0		PPT
3	Cooling	Losses through walls through transmission	0.0	0.0	0.0	0.0		PPT
4	Cooling	Heat input by solar radiation through windows	0.0	0.0	0.0	0.0		PPT
5	Cooling	Losses through windows through transmission	0.0	0.0	0.0	0.0		PPT
6	Cooling	Other effects (e.g. people, electrical Appliances)	0.0	0.0	0.0	0.0		PPT
7	Heating	Infiltration	0.0	0.0	0.0	0.0		PPT
8	Heating	Ventilation	29.19	27.56	27.22	27.22		PPT
9	Heating	Walls	34.75	32.81	32.40	32.40		PPT
10	Heating	Windows	15.49	14.63	14.44	14.44		PPT
11	Heating	Floors	8.74	8.25	8.15	8.15		PPT
12	Heating	Roofs	9.33	8.81	8.70	8.70		PPT
13	Heating	Other	0.0	0.0	0.0	0.0		PPT
14	Heating	Warm water	1.89	0.0	1.76	1.76		Copenhagen Excel
15	Electricity	Lighting	2.82	20.05	49.68	8.67		Copenhagen Excel
16	Electricity	Appliances	24.84	18.74	14.19	13.00		Copenhagen Excel
17	Electricity	Ventilation	0	12.03	21.29	18.24		Copenhagen Excel
18	Electricity	Other	0.56	23.69	56.77	51.76		Copenhagen Excel

Notes

• Data from Climate policies Helsinki additional data
• We went through the data mentioned above and also the files given in Climate policies Helsinki data but could not find the data for cooling, heating:infiltration, heating:other, heating:warm water:other and electricity:ventilation:residential.
• Total estimated amount of energy needed for cooling 23504480 kWh/a (whole Helsinki). Based on [2] 14332 GWh/a for housing (total energy demand minus traffic 18 % [3] and [4] 2 % of total energy demand of housing used for cooling. Total floor area of Helsinki needed (table 1).

Technical notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries: see table.

Table 5. Changes in energy efficiency of different energy sinks.

Efficiency increase decreasing energy demand(% /a)

Obs	Energy type	Use	Residential	Public	Industrial	Other	Description
1	Electricity	Lighting /Lamp stock	0.6	0.6	0.6	0.6
2	Electricity	Appliances	0.6	0.6	0.6	0.6
3	Electricity	Ventilation	0.6	0.6	0.6	0.6
4	Electricity	Other	0.6	0.6	0.6	0.6
5	Cooling	Infiltration	2	0.6	0.6	0.6
6	Cooling	Ventilation	2	0.6	0.6	0.6
7	Cooling	Other (e.g. electric appliances, people)	2	0.6	0.6	0.6
8	Heating	Infiltration	0.6	0.6	0.6	0.6
9	Heating	Ventilation	0.6	0.6	0.6	0.6
10	Heating	Floors	0.6	0.6	0.6	0.6
11	Heating	Roofs	0.6	0.6	0.6	0.6
12	Heating	Other reasons	0.6	0.6	0.6	0.6
13	Heating	Warm water	1.1	0.6	0.6	0.6

Notes

• Estimated 3 % of houses renovated/year and 20 % increase in energy efficiency when renovated. [5] Data apart from 0.6% values from Climate policies Helsinki additional data
• The presentation about tables 4 and 5 PresentationHW9

Technical notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries Excel.

Table 6. Important energy parameters.

Energy parameters(-)

Obs	Parameter	Unit	Residential	Public	Industrial	Other	Description	Auxiliary
1	Ratio of wall area / effective area	-	0.00	0.00	0.00	0.00		PPT
2	Ratio of window/effective area	-	0.00	0.00	0.00	0.00		PPT
3	U-value of windows	W /m2 /K	1.0	1.4	1.4	1.4		PPT
4	Solar heat gain coefficient "G-Value" of windows	%	0.40	0.0	0.0	0.0		PPT
5	Efficiency increase of G-value of windows	% /a	0.0	0.0	0.0	0.0		Excel
6	Efficiency increase of U-value of windows	% /a	0.0	0.0	0.0	0.0		Excel
7	U-value of building walls	W /m2 /K	0.16	0.25	0.25	0.28		PPT

Notes

• Data used: Unit heat consumption of buildings in Finland.
• Data Climate policies Helsinki additional data only contained 0.00 for all the parameters.
• EU legislation value for U-value of windows in the document below was 1.0, so we used it for building buildings and the past years' values for the non-residential buildings. We assumed this will be the new residential buildings to be within the legislative value.
• HW9 table 6 presentation: Helsinki Building Stock

Technical notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries: see table.

Values derived from Unit heat consumption of buildings in Finland (these could be used to update Table 6):

Important energy parameters

Parameter	Value	Table	Description
Efficiency increase of U values walls 1/a	0.019642857	u.factor	(0.28-0.17)/0.28/(2010-1990)
Efficiency increase of U values windows 1/a	0.014285714	u.factor	(1.4-1)/1.4/(2010-1990)
U value wall W/m2/K	0.17-0.28	u.factor	values for buildings built 1990-2010
U value window W/m2/K	1.0-1.4	u.factor	values for buildings built 1990-2010
G value %	70	sun.heat.absorption.parameters
Ratio of wall/effective area	0.647727273	surface.area	year 2010: 114/176
Ratio of window/effective area	0.1	surface.area	year 2010: 17.6/176

Lighting

Tables 7-8. Run times and shares of lamps by lamp type and building type.

Average run time of lamps per year(h /a)

Obs	Lamp	Residential	Public	Industrial	Other	Description
1	Incandescent lamps
2	Compact fluorescent lamps
3	Fluorescent lamps
4	Low voltage halogen lamps
5	Medium voltage halogen lamps

Shares of lamp types(%)

Obs	Lamp	Residential	Public	Industrial	Other	Description
1	Incandescent lamps
2	Compact fluorescent lamps
3	Fluorescent lamps
4	Low voltage halogen lamps
5	Medium voltage halogen lamps

Notes

Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries Excel.

Other

Sheet 4_Input Buildings (Area Demand). Priority 1. Auxiliaries PPT. Nice to have specific factor otherwise we adapt standard data or other studies.

Possibly important information

Data	Result
Number of households
Number of persons per household

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)

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 [6]