# Difference between revisions of "Building stock in Kuopio"

## Question

How to model the building stock of a city?

Calculate building stock into the future
• The dynamics is calculated by adding building floor area at time points greater than construction year, and by subtracting when time point is greater than demolition year. This is done by building category, not individually.
• Full data are stored in the ovariables. Before evaluating extra columns and rows are removed. The first part of the code is about this.

 library(OpasnetUtils) library(ggplot2) objects.latest("Op_en5932", code_name = "initiate") # Building ovariables objects.latest("Op_en6007", code_name = "answer") # findrest #################### Manage the data before calculating # The building stock is measured as m^2 floor area. openv.setN(1) # Set the number of iterations to 1. ####### Remove columns that are not needed. buildings2010@data <- buildings2010@data[ buildings2010@data\$Observation == "AreaBR" , colnames(buildings2010@data) != "Observation" ] construction@data <- construction@data[ construction@data\$Observation == "Area" , colnames(construction@data) != "Observation" ] # The data is not yet specific to construction year, so remove index: heatingTypeFuture@data <- heatingTypeFuture@data[colnames(heatingTypeFuture@data) != "Constructed"] buildings <- buildings2010 * heatingType / 100 futureBuildings <- construction * 10 * energyClassesFuture / 100 # Construction in ten years multiplied by fraction of energy classes year <- Ovariable(data = data.frame( Constructed = factor( c("2000-2010", "2011-2019", "2020-2029", "2030-2039", "2040-2049"), ordered = TRUE ), Year = c(2005, 2015, 2025, 2035, 2045), Result = 1 )) energyClassesFuture <- findrest(energyClassesFuture, cols = "Emission class", total = 100) construction <- construction * year #addc ei mätsää buildingsin kanssa; edellisessä ei ole Heatingia. Samoin energialuokitus puuttuu molemmista #vaikka pitäisi lisätä tiedot futureBuildingsistä. ggplot(construction@output, aes(x = Year, y = Result)) + geom_point(aes(colour = Building)) + theme_grey(base_size = 24)

 What output to show?:Heat Electricity for heating User electricity Total electricity Oil Wood library(OpasnetUtils) library(ggplot2) # A package with fancy graph formats test <- Ovariable( name = 'test', dependencies = data.frame( Name = c('building.stock', 'energy.per.area', 'heating.fraction'), Ident = c('Op_en5932/initiate') ), formula = function(...){ building.stock@output <- building.stock@output[building.stock@output\$Observation == "AreaHR" , ] colnames(building.stock@output)[colnames(building.stock@output) == "Observation"] <- "building.stockObservation" energy.per.area@output <- energy.per.area@output[energy.per.area@output\$Observation == observation , ] colnames(energy.per.area@output)[colnames(energy.per.area@output) == paste("energy.per.area", observation, sep = "")] <- "energy.per.areaResult" heating.fraction@output <- heating.fraction@output[heating.fraction@output\$Observation == 'CorrFraction',c('Type','Heating','heating.fractionResult','heating.fractionSource')] oprint(heating.fraction) out <- building.stock * heating.fraction / 100 * energy.per.area return(out) } ) test <- EvalOutput(test,N=1) oprint(test) oprint(summary(test)) ograph(test, x = "Constructed", fill = "Type") ograph(test, x = "Heating", fill = "Type") ograph(test, x = "Constructed", fill = "Heating", type = geom_bar())

# Rationale

## City-specific data

### Building registry data

Building registry data(#,m2,m3,#)
ObsBuildingConstructedNumberAreaBRVolumeBRAreaHRVolumeHRPopulationDescription
1Detached houses2000-20107563860811633314101479.9324433.4From building registry, except "AreaHR" and "VolumeHR" from heat registry
2Detached houses1990-19992916923824416139061.7124881.1
3Detached houses1980-198913324364661578407178797.9571620.7
4Detached houses1970-197911535339142135897154770.3494803.8
5Detached houses1960-196971423947882591095842.12306409.3
6Detached houses1950-1959769129148400137103224.9330012.2
7Detached houses1940-19494445942019980659599.3190540.2
8Detached houses1930-19392312949211781731007.75991332.4
9Detached houses1920-19291825236220914224430.3478104.3
10Detached houses1910-191983171416328711141.3135619.0
11Detached houses1900-190936485832669748860.69156208.7
12Detached houses1799-189980214169007810738.6134331.57
13Row houses2000-20103548851477614073.8944638.6
14Row houses1990-19991314467315529152676.57167076
15Row houses1980-19892155323816737186453.91274208.7
16Row houses1970-197924811250438869899723.58316296.5
17Row houses1960-19691695935421850067956.79215540.8
18Row houses1950-19591495199218103959914.57190033
19Row houses1940-1949150242667965560316.88191308.4
20Row houses1930-193918210967697238.00222957.0
21Row houses1920-19295066941944720105.5663769.5
22Row houses1910-1919105649310954021.11212753.9
23Row houses1900-1909601617477024126.6776523.4
24Row houses1799-189963075129322412.6677652.3
25Apartment houses2000-20101011949464499167322.2585906.9
26Apartment houses1990-1999260119428487611430730.51508275
27Apartment houses1980-1989393136886479881651065.82279816
28Apartment houses1970-1979149127426429284246841.7864357.7
29Apartment houses1960-1969122114214393011202112707729.1
30Apartment houses1950-195915555041236701256781.7899164
31Apartment houses1940-1949731752152956120935.9423477.3
32Apartment houses1930-193946121224778876206.17266848.7
33Apartment houses1920-19295164681882984489.45295854
34Apartment houses1910-19191753611658028163.1598618
35Apartment houses1900-190977360018943127562.5446681.5
36Apartment houses1799-18991591903724524849.8487015.87
37Leisure houses2000-20101292762210603427622106034
38Leisure houses1990-1999628253150959798253150959798
39Leisure houses1980-1989312106254328436106254328436
40Leisure houses1970-19791517313616701731361670
41Leisure houses1960-1969234180014275641800142756
42Leisure houses1950-195919772724658772724658
43Leisure houses1940-194932658119417658119417
44Leisure houses1930-1939111671402816714028
45Leisure houses1920-1929349616724961672
46Leisure houses1910-19197834530030834530030
47Leisure houses1900-190925817731580817731580
48Leisure houses1799-189911518721517518721517
49Offices2000-201011834251324571.1108975.1
50Offices1990-19993054751651267012.09297204.7
51Offices1980-198915158566116833506.05148602.4
52Offices1970-1979581823514611168.6849534.1
53Offices1960-196914128964226731272.31138695.5
54Offices1950-19591141751489124571.1108975.1
55Offices1940-19492658131900258077.15257577.4
56Offices1930-1939672032563413402.4259441.0
57Offices1920-19296184477348413402.4259441.0
58Offices1910-191937007283936701.20929720.5
59Offices1900-1909111405665824571.1108975.1
60Offices1799-18991088852876622337.3699068.2
61Commercial buildings2000-20103160897506014.09833975.3
62Commercial buildings1990-1999902741281016180422.91019260
63Commercial buildings1980-19895940501135660118277.3668181.6
64Commercial buildings1970-19799179906624018042.29101926
65Commercial buildings1960-1969676132544012028.267950.7
66Commercial buildings1950-1959939921738718042.29101926
67Commercial buildings1940-19494058241690180187.97453004.5
68Commercial buildings1930-19391029621279520046.99113251.1
69Commercial buildings1920-192948512277778018.79745300.5
70Commercial buildings1910-19191704002004.69911325.1
71Commercial buildings1900-1909372511941674173.87419029.2
72Commercial buildings1799-1899852212777816037.5990600.9
73Health and social sector buildings2000-2010130534225.816458.0
74Health and social sector buildings1990-1999162652917406767612.8263327.8
75Health and social sector buildings1980-19892193542718288741.7345617.7
76Health and social sector buildings1970-19795976274321129.082289.9
77Health and social sector buildings1960-1969323794312677.449374.0
78Health and social sector buildings1950-19597793267729580.6115205.9
79Health and social sector buildings1940-19495661169521129.082289.9
80Health and social sector buildings1930-19392661478451.632916.0
81Health and social sector buildings1920-192900000
82Health and social sector buildings1910-191900000
83Health and social sector buildings1900-190921494878451.632916.0
84Health and social sector buildings1799-1899230708451.632916.0
85Public buildings2000-201000000
86Public buildings1990-199917100653086322048113251.8
87Public buildings1980-19898948357110375.553295.0
88Public buildings1970-197900000
89Public buildings1960-19699161535808511672.559956.9
90Public buildings1950-1959121716443215563.379942.5
91Public buildings1940-1949111774484114266.473280.6
92Public buildings1930-1939275213282593.913323.8
93Public buildings1920-192965129136397781.739971.2
94Public buildings1910-1919131781296.96661.9
95Public buildings1900-1909537511036484.733309.4
96Public buildings1799-189900000
97Sports buildings2000-20103160148925231.234573.8
98Sports buildings1990-199991330377715693.5103721.2
99Sports buildings1980-19892339931172140105.7265065.4
100Sports buildings1970-19791101029801743.711524.6
101Sports buildings1960-196943269706974.946098.3
102Sports buildings1950-1959556114218718.657622.9
103Sports buildings1940-1949560624678718.657622.9
104Sports buildings1930-19394922146974.946098.3
105Sports buildings1920-192911283681743.711524.6
106Sports buildings1910-191900000
107Sports buildings1900-190900000
108Sports buildings1799-189900000
109Educational buildings2000-201010965329128041.9118506.4
110Educational buildings1990-199935126906409298146.7414772.4
111Educational buildings1980-1989502824283710140209.5592532
112Educational buildings1970-197924244147793967300.6284415.3
113Educational buildings1960-196922406475608.423701.28
114Educational buildings1950-1959439921160211216.847402.6
115Educational buildings1940-194912556202804.211850.6
116Educational buildings1930-19395173051901402159253.2
117Educational buildings1920-192911514002804.211850.6
118Educational buildings1910-191900000
119Educational buildings1900-190911829338430846.1130357
120Educational buildings1799-189942129706311216.847402.6
121Industrial buildings2000-2010121403498312303.767793.0
122Industrial buildings1990-1999735982825925974847.7412407.7
123Industrial buildings1980-198910061742213089102531.1564942
124Industrial buildings1970-1979305353324586030759.3169482.6
125Industrial buildings1960-196925217747859925632.8141235.5
126Industrial buildings1950-1959335451317191633835.3186430.9
127Industrial buildings1940-1949165991184321640590390.7
128Industrial buildings1930-193953774169435126.628247.1
129Industrial buildings1920-19294101224834101.222597.7
130Industrial buildings1910-1919173638001025.35649.4
131Industrial buildings1900-19098782108202.545195.4
132Industrial buildings1799-18995169265305126.628247.1
133Other buildings2000-20101757839469355419385457.8395141.1
134Other buildings1990-1999848368865135308541245.4190711.2
135Other buildings1980-1989867617201241334642169.5194984.3
136Other buildings1970-1979395666755284744119212.288833.7
137Other buildings1960-1969266408885157495912937.859822.2
138Other buildings1950-1959310474438165693415077.969717.6
139Other buildings1940-194924410229835255311867.854874.5
140Other buildings1930-193975507591879193647.916867.2
141Other buildings1920-192993721012789594523.420915.3
142Other buildings1910-191933419161503941605.17421.5
143Other buildings1900-1909116340611206955642.126087.9
144Other buildings1799-189941336801378781994.29220.7

### Fractions of houses according to heating type

Fractions of houses according heating type(%)
ObsBuildingHeatingFractionOld fractionDescription
1Detached housesDistrict68.3682.5City of Kuopiola
2Detached housesElectricity16.098.93
3Detached housesOil8.504.66
4Detached housesWood5.242.86
5Detached housesGeothermal1.811.04
6Row housesDistrict100100Nearly correct
7Apartment housesDistrict100100Nearly correct
8Leisure housesElectricity100100Assumption
9OfficesDistrict100100Assumption
10Commercial buildingsDistrict100100Assumption
11Health and social sector buildingsDistrict100100Assumption
12Public buildingsDistrict100100Assumption
13Sports buildingsDistrict100100Assumption
14Educational buildingsDistrict100100Assumption
15Industrial buildingsDistrict100100Assumption
16Other buildingsDistrict100100Assumption
Future heating types(%)
ObsBuildingHeatingConstructedFraction
1ResidentialDistrict70-90
2ResidentialGeothermal5-10
3ResidentialElectricity10-15
4Non-residentialDistrict100

### Baseline energy consumption per area unit

----#: . Note that below numbers are very preliminary (esp. electricity)! --Marjo 16:49, 13 March 2013 (EET) (type: truth; paradigms: science: comment)

Baseline energy consumption per area unit(kWh/m2/a)
ObsBuildingHeatingDistrict heatElectricity for heatingUser electricityOilWoodTotal electricityYearDescription
1Detached housesDistrict134.7450184.742010Calculated from energy company´s data; Pöyry
2Detached housesElectricity0130501802010Energiapolar; Pöyry
4Detached housesWood050134.74502010Assumption. Efficiency of good kettles 80%(energiatehokaskoti.fi).
5Detached housesGeothermal090902010Assumption
6Row housesDistrict168.8873.573.52010Calculated from energy company´s data
7Apartment housesDistrict172.3141.741.72010Calculated from energy company´s data
8Commercial buildingsDistrict161.82229.6229.62010Calculated from energy company´s data
9OfficesDistrict161.0793.193.12010Calculated from energy company´s data
10Health and social sector buildingsDistrict214.97122.81122.812010Calculated from energy company´s data
11Public buildingsDistrict165.47110.4110.42010Calculated from energy company´s data
12Sports buildingsDistrict121.3885.985.92010Calculated from energy company´s data
13Educational buildingsDistrict170.00116.4116.42010Calculated from energy company´s data
14Industrial buildingsDistrict168.44212.4212.42010Calculated from energy company´s data
15Leisure housesElectricity02.413.42010Calculated from energy company´s data
16Other buildingsDistrict138.14170.3170.32010Calculated from energy company´s data

Pöyry 2011. [1]

Energiapolar. [2]

### Baseline energy consumption per volume unit

----#: . Note that below numbers are very preliminary (esp. electricity)! --Marjo 16:49, 13 March 2013 (EET) (type: truth; paradigms: science: comment)

Baseline energy consumption per volume unit(kWh/m3/a)
ObsBuildingHeatingHeatElectricity for heatingUser electricityOilWoodYearDescription
1Detached housesDistrict42.1515.672010Calculated from energy company´s data; Energiapolar
2Detached housesElectricity040.6615.672010Energiapolar
3Detached housesOil015.6742.152010Energiapolar
4Detached housesWood015.6742.152010Assumption
5Detached housesGeothermal034.232010Assumption
6Row housesDistrict53.2523.162010From energy company
7Apartment housesDistrict49.2011.902010From energy company
8Commercial buildingsDistrict28.6540.642010From energy company
9OfficesDistrict36.3220.992010From energy company
10Health and social sector buildingsDistrict55.2031.532010From energy company
11Public buildingsDistrict32.2121.492010From energy company
12Sports buildingsDistrict18.3713.002010From energy company; Electricity value comes from city´s renovation data
13Educational buildingsDistrict40.2327.542010From energy company
14Industrial buildingsDistrict30.5738.552010From energy company
15Leisure housesElectricity00.680.292010From energy company
16Other buildingsDistrict29.8836.832010From energy company

### Renovation data

Fraction of houses renovated per year(%)
ObsConstructedResultDescription
12000-20100Assumption
21990-19990Assumption
31980-19892Assumption
41970-19798Assumption based on Pöyry 2011 s.27
51960-19698Assumption based on Pöyry 2011 s.27
61950-19592Assumption
71940-19492Assumption
81930-19392Assumption
91920-19292Assumption
101910-19191Assumption
111900-19091Assumption
121799-18991Assumption

### New buildings per year

Floor area of new houses and additional construction per year(#,m2,m3)
ObsBuildingNumberAreaVolumeYearDescription
1Detached houses244-27135137-40041120728-1411082010-2012From city supervision of buildings
2Row houses26-3913120-1840844141-627212010-2012From city supervision of buildings
3Apartment houses21-3134815-55460128154-2093402010-2012From city supervision of buildings
4Commercial buildings9-149742-8732349576-6512392010-2012From city supervision of buildings
5Offices3-6235-23891993-1065902010-2012From city supervision of buildings
6Industrial buildings14-232948-1163813555-789062010-2012From city supervision of buildings
7Public buildings2-5313-2819905-174702010-2012From city supervision of buildings
8Educational buildings4-6220-147301745-747182010-2012From city supervision of buildings
9Health and social sector buildings2-817-28843280-1750642010-2012From city supervision of buildings
10Sport buildings2010-2012From city supervision of buildings
11Leisure buildings47-692859-36609909 126932010-2012From city supervision of buildings
12Other buildings317-42119849-3619480607-1230132010-2012From city supervision of buildings

### Removed buildings per year

Number of removed buildings has been 15-25 per year during 2009-2012 according dismantling permissions of the city. The actual number may be somewhat larger.

### Energy saving potential of different renovations

Energy saving potential of different renovations(%,kWh/m2/a)
ObsEnergy classBuilding2RenovationRelativeAbsoluteDescription
1OldResidentialNew windows and doors1525Pöyry 2011
2OldResidentialNew windows, sealing of building's sheath, improvement of building's technical systems5075Pöyry 2011
3OldResidentialNew windows, sealing of building's sheath, improvement of building's technical systems, significant reform of building's sheath65100Pöyry 2011
4OldNon-residentialGeneral renovation15-Pöyry 2011
5None00
Building type comparisons(-)
ObsBuildingBuilding2Dummy
1Detached housesResidential
2Row housesResidential
3Apartment housesResidential
4Commercial buildingsNon-residential
5OfficesNon-residential
6Industrial buildingsNon-residential
7Public buildingsNon-residential
8Educational buildingsNon-residential
9Health and social sector buildingsNon-residential
10Sport buildingsNon-residential
11Leisure buildingsNon-residential
12Other buildingsNon-residential

### Energy use by the energy class of a building

Energy use by energy class of building(kWh/m2/a)
ObsEnergy classHeating energyProperty and user electricityService waterDescription
1Old15030Pöyry 2011 s.28
2New705040Pöyry 2011 s.32 (2010 SRMK)
3Low-energy355040Personal communication
4Passive17.5 - 255040Pöyry 2011 s.33; Personal communication

Energy classes of new buildings in the future(%)
ObsEnergy classConstructedFractionDescription
1New2020-202910-20
2Low-energy2020-2029The rest
3Passive2020-202925-35
4New2030-20395-10
5Low-energy2030-203920-50
6Passive2030-2039The rest
7New2040-20490-5
8Low-energy2040-204910-30
9Passive2040-2049The rest
• Old: old buildings to be renovated (or in need of renovation)
• New: normal new buildings (no current need of renovation)
• Low-energy: buildings consuming about half of the energy of a new building
• Passive: buildings consuming a quarter or less of the energy of a new building

### Data regarding indoor environment quality (IEQ) factors

IEQ factors(h-1,%,%,%,-,%,%,%,Bq/m3)
ObsBuildingHeatingVentilation rateDampness%Smoking%Biomass burning%Indoor background emissionsIn noise areas%Too hot in summer%Too cold in winter%RadonDescription
1Detached housesDistrict0.71 (0.3-1.12)5-16.52.35 (1.4-3.4)15100 (95-105)Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
2Detached housesElectricity0.71 (0.3-1.12)5-16.52.35 (1.4-3.4)15100 (95-105)Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
3Detached housesOil0.71 (0.3-1.12)5-16.52.35 (1.4-3.4)15100 (95-105)Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
4Detached housesWood0.71 (0.3-1.12)5-16.52.35 (1.4-3.4)15100 (95-105)Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
5Detached housesGeothermal0.71 (0.3-1.12)5-16.52.35 (1.4-3.4)15100 (95-105)Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
6Row housesDistrict0.71 (0.3-1.12)5-16.52.35 (1.4-3.4)21100 (95-105)Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
7Apartment housesDistrict0.71 (0.3-1.12)5-16.52.35 (1.4-3.4)30100 (95-105)Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
8Leisure housesElectricity
9OfficesDistrict0Assumption
10Commercial buildingsDistrict0Assumption
11Health and social sector buildingsDistrict0Assumption
12Public buildingsDistrict0Assumption
13Sports buildingsDistrict0Assumption
14Educational buildingsDistrict240Haverinen-Shaughnessy et al. 2012; Assumption
15Industrial buildingsDistrict0Assumption
16Other buildingsDistrict

Gens 2012 [4]

Haverinen-Shaughnessy 2010 [5]

Haverinen-Shaughnessy et al. 2012 [6]

Turunen et al. 2010 [7]

## Other potentially useful data

### Regulations regarding energy consumption of buildings

Maximum allowed energy consumption per unit (= E-value)(kWh/m2/a)
ObsBuildingYearE-valueDescription
1Detached houses2012 forward204Heated net area <120 m2; Finland´s Environmental Administration
4Shops and other commercial buildings2012 forward240Finland´s Environmental Administration
6Health and social sector buildings: Hospitals2012 forward450Finland´s Environmental Administration
7Health and social sector buildings: Health care centers etc.2012 forward170Finland´s Environmental Administration
9Sports buildings2012 forward170Does not apply to swimming- and ice halls; Finland´s Environmental Administration
11Industrial buildings2012 forward-E-value must be calculated but there´s no limit for it; Finland´s Environmental Administration
12Leisure buildings2012 forward-E-value must be calculated but there´s no limit for it; Finland´s Environmental Administration
13Other buildings2012 forward-E-value must be calculated but there´s no limit for it; Finland´s Environmental Administration

### Emission factors for wood heating

Emission factors for wood heating(PJ/a; mg/MJ)
ObsTypeObservationResultDescription
1Residential buildings34.2 (30.8-37.6)Karvosenoja et al. 2008
2Primary wood-heated residential buildings20.2 (16.6-23.9)Karvosenoja et al. 2008
3Manual feed boilers with accumulator tank5.42 (3.89-7.22)80.0 (37.6-150)Karvosenoja et al. 2008
4Manual feed boilers without accumulator tank2.67 (1.67-3.87)700 (329-1310)Karvosenoja et al. 2008
5Automatic feed wood chip boilers1.46 (1.01-2)50.0 (23.5-93.9)Karvosenoja et al. 2008
6Automatic feed pellet boilers0.102 (0.0693-0.142)30.0 (14.1-56.3)Karvosenoja et al. 2008
7Iron stoves0.142 (0.0976-0.196)700 (329-1310)Karvosenoja et al. 2008
8Other stoves and ovens10.2 (7.86-12.8)140 (65.8-263)Karvosenoja et al. 2008
9Low-emission stoves080 (37.6-150)Karvosenoja et al. 2008
10Open fireplaces0.163 (0.111-0.224)800 (376-1500)Karvosenoja et al. 2008
11Supplementary wood-heated residential buildings14.0 (10.7-17.4)Karvosenoja et al. 2008
12Iron stoves0.212 (0.135-0.316)700 (329-1310)Karvosenoja et al. 2008
13Other stoves and ovens13.6 (10.4-16.9)140 (65.8-263)Karvosenoja et al. 2008
14Low-emission stoves080 (37.6-150)Karvosenoja et al. 2008
15Open fireplaces0.222 (0.14-0.332)800 (376-1500)Karvosenoja et al. 2008
16Recreational buildings5.00 (4.50-5.50)Karvosenoja et al. 2008
17Iron stoves0.782 (0.372-1.37)700 (329-1310)Karvosenoja et al. 2008
18Other stoves and ovens3.96 (3.19-4.59)140 (65.8-263)Karvosenoja et al. 2008
19Open fireplaces0.262 (0.118-0.477)800 (376-1500)Karvosenoja et al. 2008

Karvosenoja et al. 2008 [8]

## Calculations for ovariables

 library(OpasnetUtils) buildings2010 <- Ovariable( name = 'buildings2010', ddata = 'Op_en5932', subset = 'Building registry data' ) energy.per.area <- Ovariable( name = 'energy.per.area', ddata = 'Op_en5932', subset = 'Baseline energy consumption per area unit' ) heatingType <- Ovariable( name = 'heatingType', ddata = 'Op_en5932', subset = 'Fractions of houses according heating type' ) heatingTypeFuture <- Ovariable( name = 'heatingTypeFuture', ddata = 'Op_en5932', subset = 'Future heating types' ) energy.per.volume <- Ovariable( name = 'energy.per.volume', ddata = 'Op_en5932', subset = 'Baseline energy consumption per volume unit' ) renovation <- Ovariable( name = 'renovation', ddata = 'Op_en5932', subset = 'Fraction of houses renovated per year' ) construction <- Ovariable( name = 'construction', ddata = 'Op_en5932', subset = 'Floor area of new houses and additional construction per year' ) energyClassesFuture <- Ovariable( name = 'energyClassesFuture', ddata = 'Op_en5932', subset = 'Energy classes of new buildings in the future' ) Maximum.allowed.energy.consumption.per.unit <- Ovariable( name = 'Maximum.allowed.energy.consumption.per.unit', ddata = 'Op_en5932', subset = 'Maximum allowed energy consumption per unit (= E-value)' ) energy.saving.potential.of.different.renovations <- Ovariable( name = 'energy.saving.potential.of.different.renovations', ddata = 'Op_en5932', subset = 'Energy saving potential of different renovations' ) energy.saving.potential.of.different.renovations <- Ovariable( name = 'energy.saving.potential.of.different.renovations', ddata = 'Op_en5932', subset = 'Energy saving potential of different renovations' ) energy.classes.of.buildings <- Ovariable( name = 'energy.classes.of.buildings', ddata = 'Op_en5932', subset = 'Energy use by energy class of building' ) buildingTypes <- Ovariable( name = 'buildingTypes', ddata = 'Op_en5932', subset = 'Building type comparisons' ) objects.store( buildings2010, energy.per.area, energy.per.volume, heatingType, heatingTypeFuture, renovation, construction, energyClassesFuture, energy.saving.potential.of.different.renovations, energy.classes.of.buildings, buildingTypes ) cat("Objects buildings2010, energy.per.area, energy.per.volume, heatingType, heatingTypeFuture, renovation, construction, energyClassesFuture, energy.saving.potential.of.different.renovations, energy.classes.of.buildings, buildingTypes initiated!\n")

## Other preliminary calculations

 Which data do you want to use?:Public summary data Secret detailed data Password (only needed with secret data):library(OpasnetUtils) library(ggplot2) if(secret) { objects.get("ozJoC2EaIChFI0uC") building.stock <- objects.decode(etable, key) } else { building.stock <- Ovariable("building.stock", ddata = "Op_en5932", getddata = FALSE)@output } colnames(building.stock) <- as.vector(t(building.stock[1, ])) building.stock <- building.stock[2:nrow(building.stock) , ] for(i in c(1,3,4,5,7,8)) { building.stock[[i]] <- as.numeric(gsub(",", "", building.stock[[i]])) } building.stock <- dropall(building.stock) cat("Mean floor area of buildings in Kuopio by heating type.\n") oprint(as.data.frame(as.table(tapply( building.stock\$kokonaisala, building.stock[c("paaasiallinen_lammitystapa")], mean, na.rm = TRUE )))) ggplot(building.stock, aes(x = paaasiallinen_lammitystapa, weight = kokonaisala, fill = kayttotarkoitus)) + geom_bar() + theme_grey(base_size = 24) + theme(axis.text.x = element_text(angle = 90, hjust = 1))

 Urgenche pages Urgenche main page · Category:Urgenche · Urgenche project page (password-protected) City Kuopio Climate change policies and health in Kuopio (assessment) · Climate change policies in Kuopio (plausible city-level climate policies) · Health impacts of energy consumption in Kuopio · Building stock in Kuopio · Cost curves for energy (prioritization of options) · Energy balance in Kuopio (energy data) · Energy consumption and GHG emissions in Kuopio by sector · Energy consumption classes (categorisation) · Energy consumption of heating of buildings in Kuopio · Energy transformations (energy production and use processes) · Fuels used by Haapaniemi energy plant · Greenhouse gas emissions in Kuopio · Haapaniemi energy plant in Kuopio · Land use in Kuopio · Building data availability in Kuopio · Password-protected pages: File:Heat use in Kuopio.csv · Kuopio housing City Basel Buildings in Basel (password-protected)

## References

1. Pöyry 2011: Kuopion kasvihuonekaasupäästöjen vähentämismahdollisuudet v 2020 mennessä. [1]
2. Energiapolar/Arvioi sähkönkulutus[2]