Building stock in Kuopio

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Question

How to model the building stock of a city?

Answer

Decisions(-)
Obs	Decision maker	Decision	Option	Variable	Cell	Change	Amount	Description
1	City	RenovationPolicy	Active renovation	renovation		Multiply	1.5
2	City	RenovationPolicy	BAU	renovation		Multiply	1
3	Builders	EfficiencyPolicy	BAU	efficienciesNew	Efficiency:Passive;Constructed:2020-2029	Add	0
4	Builders	EfficiencyPolicy	Active efficiency	efficienciesNew	Efficiency:Passive;Constructed:2020-2029	Add	0.3	Given as fraction because that's how it is calculated in the model
5	Builders	EfficiencyPolicy	Active efficiency	efficienciesNew	Efficiency:Low-energy;Constructed:2020-2029	Add	-0.3	Given as fraction because that's how it is calculated in the model
6	Kuopion Energia	FuelPolicy	BAU	fuelTypes		Add	0
7	Kuopion Energia	FuelPolicy	Buofuel increase	fuelTypes	Burner:Large fluidized bed;Fuel:Wood	Add	0.6
8	Kuopion Energia	FuelPolicy	Buofuel increase	fuelTypes	Burner:Large fluidized bed;Fuel:Peat	Add	-0.6

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.
Also the renovation dynamics is built using event years: at an event, a certain amount of floor area is moved from one energy efficiency category to another.
Full data are stored in the ovariables. Before evaluating, extra columns and rows are removed. The first part of the code is about this.

Example model run

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library(OpasnetUtils)
library(ggplot2)
library(rgdal)
library(maptools)
library(RColorBrewer)
library(classInt)
library(OpasnetUtilsExt)
library(RgoogleMaps)

objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
objects.latest("Op_en6289", code_name = "initiate") # [[Building model]] Generic building model.
objects.latest("Op_en5417", code_name = "initiate") # [[Population of Kuopio]]
objects.latest("Op_en5932", code_name = "initiate") # [[Baseline building stock]] Building ovariables
objects.latest("Op_en5932", code_name = "disperse") # [[Baseline building stock]] Summarised Piltti matrix
# Default run: http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=aO1R3Xdcg2rASbKH
colnames(iF@output)[colnames(iF@output) == "City.area"] <- "Emission.site"
colnames(iF@output)[colnames(iF@output) == "Emissionheight"] <- "Emission.height"

districts <- tidy(opbase.data("Op_en5484.kuopio_city_districts"), widecol = "Location")
districts <- Ovariable("districts", data = data.frame(districts, Result = 1))

decisions <- opbase.data('Op_en5932', subset = 'Decisions')
DecisionTableParser(decisions)

# Remove previous decisions, if any. 
rm(
	"buildings",
	"buildingStock",
    "buildingTypes",
    "construction",
	"efficiencies",
	"efficienciesNew",
	"energyUse",
	"heatingShares",
    "heatingSharesNew",
	"renovation",
	"renovationShares",
	"fuelTypes",
	"year",
	envir = openv
)

fuelTypes <- CheckDecisions(EvalOutput(fuelTypes))

#################### Manage the data before calculating

# The building stock is measured as m^2 floor area.

openv.setN(5) # Set the number of iterations to 1.

obsyear <- (192:205) * 10

####### Remove columns and rows that are not needed from data. These must be done before EvalOutput.

population <- EvalOutput(population)
temp <- oapply(population, cols = "City area", FUN = "sum") # Sum across city areas.
temp <- unkeep(temp * 1, prevresults = TRUE) # Remove populationResult column because it would cause trouble in merge.
population <- population / temp

buildingStock@data <- buildingStock@data[
	buildingStock@data$Observation == "AreaHR" , 
	colnames(buildingStock@data) != "Observation"
]
buildingStock <- buildingStock * population

construction@data <- construction@data[
	construction@data$Observation == "Area" , 
	colnames(construction@data) != "Observation"
]
construction <- construction * constructionAreas / 100 / 3 # Statistics are for three years (2010-2012)

energyUse@data <- energyUse@data[
	energyUse@data[["Energy use"]] == "Heat" , 
	colnames(energyUse@data) != "Energy use"
]

savingPotential@data <- savingPotential@data[
	savingPotential@data$Observation == "Relative" , 
	colnames(savingPotential@data) != "Observation"
]
savingPotential <- 1 - savingPotential / 100 
# Note: If you add decisions to savingPotential, this formula must be updated.

# The data are not yet specific to construction year, so remove index:
heatingSharesNew@data <- heatingSharesNew@data[colnames(heatingSharesNew@data) != "Constructed"]

# Fill in Heating types and convert from % to fraction.
heatingSharesNew <- findrest(heatingSharesNew, cols = "Heating", total = 100)
result(heatingSharesNew) <- result(heatingSharesNew) / 100 

heatingShares <- EvalOutput(heatingShares)
result(heatingShares) <- result(heatingShares) / 100 # From % to fraction

# Fill in the rest of the data (the last emission class was omitted because it is determined by the total).
efficienciesNew <- findrest(efficienciesNew, cols = "Efficiency", total = 100) 
result(efficienciesNew) <- result(efficienciesNew) / 100

# When renovations are done, which type are they?
renovationShares <- findrest(renovationShares, cols = "Renovation", total = 100)
result(renovationShares) <- result(renovationShares) / 100

# What fraction of buildings is renovated per year?
renovation <- EvalOutput(renovation)
result(renovation) <- result(renovation) / 100 # From % to fraction

colnames(iF@output)[colnames(iF@output) == "City.area"] <- "Emission.site"
colnames(iF@output)[colnames(iF@output) == "Emissionheight"] <- "Emission.height"

###################### Actual model

buildings <- EvalOutput(buildings)

####### Sort factors

buildings@output <- buildings@output[
	buildings@output$EfficiencyPolicy == "BAU" , 
	colnames(buildings@output) != "EfficiencyPolicy"
]

buildings@output$Renovation <- factor(
	buildings@output$Renovation, 
	levels = c("None", "General", "Windows", "Technical systems", "Sheath reform"),
	ordered = TRUE
)

buildings@output$Efficiency <- factor(
	buildings@output$Efficiency,
	levels = c("Old", "New", "Low-energy", "Passive"),
	ordered = TRUE
)

buildings@output$RenovationPolicy <- factor(
	buildings@output$RenovationPolicy,
	levels = c("BAU", "Active renovation"),
	ordered = TRUE
)

heatingEnergy <- EvalOutput(heatingEnergy)
heatingEnergy <- unkeep(heatingEnergy, cols = "Building2", sources = TRUE, prevresults = TRUE)

temp <- buildings * districts
temp@output <- temp@output[temp@output$Year == 2040 , ]
temp <- unkeep(temp, sources = TRUE, prevresults = TRUE)
temp@output <- dropall(temp@output)
temp <- oapply(temp, cols = c("Building", "Heating", "Efficiency", "Renovation"), FUN = "sum", na.rm = TRUE)

MyRmap(
	ova2spat(temp, coord = c("E", "N"), proj4string = "+init=epsg:3067"), # National Land Survey uses EPSG:3067 (ETRS-TM35FIN)
	plotvar = "Result", legend_title = "Floor area", numbins = 8, pch = 19, cex = 2
)

####### Calculate emissions

emis <- heatingEnergy
emis@output <- emis@output[emis@output$Year >= 1980 , ]
emis <- oapply(emis, cols = c("Efficiency", "Renovation", "Building"), FUN = "sum")
emis@output$Year <- as.numeric(levels(emis@output$Year)[emis@output$Year])

emis <- emis * fuelTypes * emissionFactors * 3.6  * 1E-9 # convert from kWh /a to MJ /a and mg to ton

emis <- unkeep(emis, sources = TRUE, prevresults = TRUE)

emis <- emis * emissionLocations
colnames(emis@output)[colnames(emis@output) == "Emission site"] <- "Emission.site"
colnames(emis@output)[colnames(emis@output) == "Emission height"] <- "Emission.height"
emis@output$Emission.site <- ifelse(
	emis@output$Emission.site == "At site of consumption", 
	levels(emis@output$City.area)[emis@output$City.area],
	levels(emis@output$Emission.site)[emis@output$Emission.site]
)

###### Calculate health impacts

bg.mort <- 45182 / 5203826 # same values as used in PILTTI unit: deaths per person-year

erf <- Ovariable("erf", data = data.frame(Pollutant = "PM2.5", Result = 0.0097)) 
# J. T. Tuomisto, A. Wilson, et al. Uncertainty in mortality response to airborne fine particulate matter... 2008
# unit: m^3 /ug

health <- (emis * 1E+6 /365 / 24 / 3600) * iF / (20 / 24 / 3600) * (erf * 1E+6) * bg.mort

# emis is in ton /a
# iF = conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) => conc = iF * emis / pop / BR
# HIA = conc * erf * pop * backgroundrisk = emis (g /s) * iF / BR (m3 /s) * erf (m3 /g) * backgroundrisk

############################# Plot graphs

######## Buildings in Kuopio on map
server <- TRUE

if(server) # The following code only works from Opasnet server.
{
	shp	<- readOGR('PG:host=localhost user=postgres dbname=spatial_db','kuopio_house')
	proj4string(shp) <- ("+init=epsg:3067") # The map projection of NLS Finland.

	epsg4326String <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
	shp2	<- spTransform(shp,epsg4326String) # Convert to longitude-latitude projection.

	MyRmap(shp2, plotvar = "ika", legend_title = "Age of building", numbins = 8, pch = 19, cex = 0.3) # Draw the map.
}

### Impact graphs

BS <- 24

ggplot(buildings@output) + geom_bar() + facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) +
aes(x = Year, weight = buildingsResult/1000000, fill = Renovation) + labs(y = "Floor area (M m2)", title = "Building impacts of renovation policy")

ggplot(heatingEnergy@output) + geom_bar() + facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) + labs(title = "Energy impacts of renovation policy") +
aes(x = Year, weight = heatingEnergyResult/1E+6, fill = Heating) + labs(y = "Heating energy need (GWh /a)")

emis@output <- emis@output[emis@output$Renovation == "BAU" , ]

ggplot(emis@output) + geom_bar() + facet_grid(Pollutant ~ FuelPolicy, scales = "free_y") + theme_gray(base_size = BS) + labs(title = "Emission impacts of biofuel policy") +
aes(x = Year, weight = Result, fill = Fuel) + labs(y = "Emissions to air (ton /a)")

ggplot(health@output) + geom_bar() + facet_grid(FuelPolicy ~ RenovationPolicy) + theme_gray(base_size = BS) + labs(title = "Health impacts of fuel and renovation policy") +
aes(x = Year, weight = Result, fill = Heating) + labs(y = "Premature deaths (# /a)")

Plot results from this page

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library(OpasnetUtils)

library(ggplot2)      # A package with fancy graph formats

test <- Ovariable(
	name = 'test',
	dependencies = data.frame(
		Name = c('building.stock', 'energyUse', '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"


		energyUse@output <- energyUse@output[energyUse@output$Observation == observation , ]
		colnames(energyUse@output)[colnames(energyUse@output) == paste("energyUse", observation, sep = "")] <- "energyUseResult"
		
		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 * energyUse
		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

Dispersion modelling

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library(OpasnetUtils)
library(OpasnetUtilsExt)
library(ggplot2)
library(rgdal)
library(maptools)
library(RColorBrewer)
library(classInt)
library(RgoogleMaps)

objects.latest("Op_en6007", code_name = "answer")

# GIS points for emissions.

districts <- tidy(opbase.data("Op_en5484.kuopio_city_districts"), widecol = "Location")
districts <- Ovariable("districts", data = data.frame(districts, Result = 1))

cat("PM2.5 intake fractions are being calculated for these locations.\n")

oprint(districts)

dis <- ova2spat(EvalOutput(districts), coord = c("E", "N"), proj4string = "+init=epsg:3067")

# Long-distance iF of PM2.5 for exposures beyond 10 km.

objects.latest("Op_en5813", code_name="initiate") # Long-distance iF for PM2.5
iF.PM2.5@data <- iF.PM2.5@data[iF.PM2.5@data$Subcategory == "Large power plants" , ]
iF.PM2.5@data <- iF.PM2.5@data[!colnames(iF.PM2.5@data) %in% c("Obs", "Geographical area", "Year", "PM type", "Source category", "Subcategory")]

# Calculate exposure concentration * population for a unit emission and all emission points.

out <- Ovariable()
for(i in 1:length(dis$City.area)) 
{
	print(paste(i, "\n"))
	temp <- GIS.Exposure(GIS.Concentration.matrix(
		1, 
		LA = coordinates(dis)[i, 2], 
		LO = coordinates(dis)[i, 1],
		N = 1
	))
	out@output <- rbind(out@output, data.frame(City.area = dis$City.area[i], temp@output))
}

out@output <- out@output[out@output$HAVAINTO == "VAESTO" , ]
out@marginal <- !grepl("Result$", colnames(out@output))
out <- unkeep(out, cols = c("KUNTA", "ID_NRO", "XKOORD", "YKOORD", "HAVAINTO", "dx", "dy"), sources = TRUE)

# Large matrix with detailed exposures in grids.
PILTTI.matrix <- out

# This produces an intake fraction if you give PM2.5 emissions as ton /a. GIS.Concentration.matrix takes ton /a and gives ug /m3.
# iF = intake (g /s) per emission (g /s) = concentration (ug /m3) * population (#) * breathing rate (m3 /s) / emission (g /s).

iF <- oapply(out, cols = c("LAbin", "LObin"), FUN = "sum", na.rm = TRUE)
iF <- iF * 20 / (24 * 3600) * 1E-6 # Divide by breathing rate 20 m3 /d and scale from ug to g to get intake fraction.
iF@output <- data.frame(Emissionheight = "Low", iF@output)
iF@output <- orbind(iF, data.frame(Emissionheight = "High", Result = 0))
iF@marginal <- c(TRUE, iF@marginal)
iF@output <- fillna(iF@output, marginals = colnames(iF@output)[iF@marginal])

iF <- iF + iF.PM2.5 * 1E-6 # Scale iF.PM2.5 from ppm to fractions.

objects.store(PILTTI.matrix, iF)
cat("Objects PILTTI.matrix and iF saved.\n")

Where and how do the emissions of heating take place?

Emission locations(-)
Obs	Heating	Emission site	Emission height
1	District	Haapaniemi	High
2	Electricity	Haapaniemi	High
3	Geothermal	Haapaniemi	High
4	Oil	At site of consumption	Low
5	Wood	At site of consumption	Low
6	Gas	At site of consumption	Low

Building stock

Building registry data(#,m2,m3,#)
Obs	Building	Constructed	Number	AreaBR	VolumeBR	AreaHR	VolumeHR	Description
1	Detached houses	2000-2010	756	386081	1633314	101479.9	324433.4	From building registry, except AreaHR and VolumeHR from heat registry
2	Detached houses	1990-1999	291	69238	244161	39061.7	124881.1
3	Detached houses	1980-1989	1332	436466	1578407	178797.9	571620.7
4	Detached houses	1970-1979	1153	533914	2135897	154770.3	494803.8
5	Detached houses	1960-1969	714	239478	825910	95842.12	306409.3
6	Detached houses	1950-1959	769	129148	400137	103224.9	330012.2
7	Detached houses	1940-1949	444	59420	199806	59599.3	190540.2
8	Detached houses	1930-1939	231	29492	117817	31007.75	991332.4
9	Detached houses	1920-1929	182	52362	209142	24430.34	78104.3
10	Detached houses	1910-1919	83	17141	63287	11141.31	35619.0
11	Detached houses	1900-1909	364	8583	26697	48860.69	156208.7
12	Detached houses	1799-1899	80	21416	90078	10738.61	34331.57
13	Row houses	2000-2010	35	4885	14776	14073.89	44638.6
14	Row houses	1990-1999	131	44673	155291	52676.57	167076
15	Row houses	1980-1989	215	53238	167371	86453.91	274208.7
16	Row houses	1970-1979	248	112504	388698	99723.58	316296.5
17	Row houses	1960-1969	169	59354	218500	67956.79	215540.8
18	Row houses	1950-1959	149	51992	181039	59914.57	190033
19	Row houses	1940-1949	150	24266	79655	60316.88	191308.4
20	Row houses	1930-1939	18	2109	6769	7238.002	22957.0
21	Row houses	1920-1929	50	6694	19447	20105.56	63769.5
22	Row houses	1910-1919	10	5649	31095	4021.112	12753.9
23	Row houses	1900-1909	60	1617	4770	24126.67	76523.4
24	Row houses	1799-1899	6	3075	12932	2412.667	7652.3
25	Apartment houses	2000-2010	101	19494	64499	167322.2	585906.9
26	Apartment houses	1990-1999	260	119428	487611	430730.5	1508275
27	Apartment houses	1980-1989	393	136886	479881	651065.8	2279816
28	Apartment houses	1970-1979	149	127426	429284	246841.7	864357.7
29	Apartment houses	1960-1969	122	114214	393011	202112	707729.1
30	Apartment houses	1950-1959	155	55041	236701	256781.7	899164
31	Apartment houses	1940-1949	73	17521	52956	120935.9	423477.3
32	Apartment houses	1930-1939	46	12122	47788	76206.17	266848.7
33	Apartment houses	1920-1929	51	6468	18829	84489.45	295854
34	Apartment houses	1910-1919	17	5361	16580	28163.15	98618
35	Apartment houses	1900-1909	77	3600	18943	127562.5	446681.5
36	Apartment houses	1799-1899	15	9190	37245	24849.84	87015.87
37	Leisure houses	2000-2010	129	27622	106034	27622	106034
38	Leisure houses	1990-1999	628	253150	959798	253150	959798
39	Leisure houses	1980-1989	312	106254	328436	106254	328436
40	Leisure houses	1970-1979	15	17313	61670	17313	61670
41	Leisure houses	1960-1969	23	41800	142756	41800	142756
42	Leisure houses	1950-1959	19	7727	24658	7727	24658
43	Leisure houses	1940-1949	32	6581	19417	6581	19417
44	Leisure houses	1930-1939	11	1671	4028	1671	4028
45	Leisure houses	1920-1929	3	496	1672	496	1672
46	Leisure houses	1910-1919	7	8345	30030	8345	30030
47	Leisure houses	1900-1909	25	8177	31580	8177	31580
48	Leisure houses	1799-1899	11	5187	21517	5187	21517
49	Offices	2000-2010	11	834	2513	24571.1	108975.1
50	Offices	1990-1999	30	5475	16512	67012.09	297204.7
51	Offices	1980-1989	15	15856	61168	33506.05	148602.4
52	Offices	1970-1979	5	8182	35146	11168.68	49534.1
53	Offices	1960-1969	14	12896	42267	31272.31	138695.5
54	Offices	1950-1959	11	4175	14891	24571.1	108975.1
55	Offices	1940-1949	26	5813	19002	58077.15	257577.4
56	Offices	1930-1939	6	7203	25634	13402.42	59441.0
57	Offices	1920-1929	6	18447	73484	13402.42	59441.0
58	Offices	1910-1919	3	7007	28393	6701.209	29720.5
59	Offices	1900-1909	11	1405	6658	24571.1	108975.1
60	Offices	1799-1899	10	8885	28766	22337.36	99068.2
61	Commercial	2000-2010	3	1608	9750	6014.098	33975.3
62	Commercial	1990-1999	90	27412	81016	180422.9	1019260
63	Commercial	1980-1989	59	40501	135660	118277.3	668181.6
64	Commercial	1970-1979	9	17990	66240	18042.29	101926
65	Commercial	1960-1969	6	7613	25440	12028.2	67950.7
66	Commercial	1950-1959	9	3992	17387	18042.29	101926
67	Commercial	1940-1949	40	5824	16901	80187.97	453004.5
68	Commercial	1930-1939	10	2962	12795	20046.99	113251.1
69	Commercial	1920-1929	4	8512	27777	8018.797	45300.5
70	Commercial	1910-1919	1	70	400	2004.699	11325.1
71	Commercial	1900-1909	37	2511	9416	74173.87	419029.2
72	Commercial	1799-1899	8	5221	27778	16037.59	90600.9
73	Health and social sector	2000-2010	1	30	53	4225.8	16458.0
74	Health and social sector	1990-1999	16	26529	174067	67612.8	263327.8
75	Health and social sector	1980-1989	21	9354	27182	88741.7	345617.7
76	Health and social sector	1970-1979	5	976	2743	21129.0	82289.9
77	Health and social sector	1960-1969	3	237	943	12677.4	49374.0
78	Health and social sector	1950-1959	7	793	2677	29580.6	115205.9
79	Health and social sector	1940-1949	5	661	1695	21129.0	82289.9
80	Health and social sector	1930-1939	2	66	147	8451.6	32916.0
81	Health and social sector	1920-1929	0	0	0	0	0
82	Health and social sector	1910-1919	0	0	0	0	0
83	Health and social sector	1900-1909	2	149	487	8451.6	32916.0
84	Health and social sector	1799-1899	2	30	70	8451.6	32916.0
85	Public	2000-2010	0	0	0	0	0
86	Public	1990-1999	17	10065	30863	22048	113251.8
87	Public	1980-1989	8	948	3571	10375.5	53295.0
88	Public	1970-1979	0	0	0	0	0
89	Public	1960-1969	9	16153	58085	11672.5	59956.9
90	Public	1950-1959	12	1716	4432	15563.3	79942.5
91	Public	1940-1949	11	1774	4841	14266.4	73280.6
92	Public	1930-1939	2	752	1328	2593.9	13323.8
93	Public	1920-1929	6	5129	13639	7781.7	39971.2
94	Public	1910-1919	1	31	78	1296.9	6661.9
95	Public	1900-1909	5	375	1103	6484.7	33309.4
96	Public	1799-1899	0	0	0	0	0
97	Sports	2000-2010	3	1601	4892	5231.2	34573.8
98	Sports	1990-1999	9	1330	3777	15693.5	103721.2
99	Sports	1980-1989	23	3993	11721	40105.7	265065.4
100	Sports	1970-1979	1	1010	2980	1743.7	11524.6
101	Sports	1960-1969	4	326	970	6974.9	46098.3
102	Sports	1950-1959	5	561	1421	8718.6	57622.9
103	Sports	1940-1949	5	606	2467	8718.6	57622.9
104	Sports	1930-1939	4	92	214	6974.9	46098.3
105	Sports	1920-1929	1	128	368	1743.7	11524.6
106	Sports	1910-1919	0	0	0	0	0
107	Sports	1900-1909	0	0	0	0	0
108	Sports	1799-1899	0	0	0	0	0
109	Educational	2000-2010	10	965	3291	28041.9	118506.4
110	Educational	1990-1999	35	12690	64092	98146.7	414772.4
111	Educational	1980-1989	50	28242	83710	140209.5	592532
112	Educational	1970-1979	24	24414	77939	67300.6	284415.3
113	Educational	1960-1969	2	240	647	5608.4	23701.28
114	Educational	1950-1959	4	3992	11602	11216.8	47402.6
115	Educational	1940-1949	1	255	620	2804.2	11850.6
116	Educational	1930-1939	5	1730	5190	14021	59253.2
117	Educational	1920-1929	1	151	400	2804.2	11850.6
118	Educational	1910-1919	0	0	0	0	0
119	Educational	1900-1909	11	829	3384	30846.1	130357
120	Educational	1799-1899	4	2129	7063	11216.8	47402.6
121	Industrial	2000-2010	12	1403	4983	12303.7	67793.0
122	Industrial	1990-1999	73	59828	259259	74847.7	412407.7
123	Industrial	1980-1989	100	61742	213089	102531.1	564942
124	Industrial	1970-1979	30	53533	245860	30759.3	169482.6
125	Industrial	1960-1969	25	21774	78599	25632.8	141235.5
126	Industrial	1950-1959	33	54513	171916	33835.3	186430.9
127	Industrial	1940-1949	16	5991	18432	16405	90390.7
128	Industrial	1930-1939	5	3774	16943	5126.6	28247.1
129	Industrial	1920-1929	4	1012	2483	4101.2	22597.7
130	Industrial	1910-1919	1	736	3800	1025.3	5649.4
131	Industrial	1900-1909	8	78	210	8202.5	45195.4
132	Industrial	1799-1899	5	1692	6530	5126.6	28247.1
133	Other	2000-2010	1757	839469	3554193	85457.8	395141.1
134	Other	1990-1999	848	368865	1353085	41245.4	190711.2
135	Other	1980-1989	867	617201	2413346	42169.5	194984.3
136	Other	1970-1979	395	666755	2847441	19212.2	88833.7
137	Other	1960-1969	266	408885	1574959	12937.8	59822.2
138	Other	1950-1959	310	474438	1656934	15077.9	69717.6
139	Other	1940-1949	244	102298	352553	11867.8	54874.5
140	Other	1930-1939	75	50759	187919	3647.9	16867.2
141	Other	1920-1929	93	72101	278959	4523.4	20915.3
142	Other	1910-1919	33	41916	150394	1605.1	7421.5
143	Other	1900-1909	116	34061	120695	5642.1	26087.9
144	Other	1799-1899	41	33680	137878	1994.2	9220.7

New buildings per year

Floor area of new houses and additional construction per year(#,m2,m3)
Obs	Building	Number	Area	Volume	Year	Description
1	Detached houses	244-271	35137-40041	120728-141108	2010-2012	From city supervision of buildings
2	Row houses	26-39	13120-18408	44141-62721	2010-2012	From city supervision of buildings
3	Apartment houses	21-31	34815-55460	128154-209340	2010-2012	From city supervision of buildings
4	Commercial	9-14	9742-87323	49576-651239	2010-2012	From city supervision of buildings
5	Offices	3-6	235-23891	993-106590	2010-2012	From city supervision of buildings
6	Industrial	14-23	2948-11638	13555-78906	2010-2012	From city supervision of buildings
7	Public	2-5	313-2819	905-17470	2010-2012	From city supervision of buildings
8	Educational	4-6	220-14730	1745-74718	2010-2012	From city supervision of buildings
9	Health and social sector	2-8	17-28843	280-175064	2010-2012	From city supervision of buildings
10	Sports		0		2010-2012	From city supervision of buildings? Empty row
11	Leisure houses	47-69	2859-3660	9909 12693	2010-2012	From city supervision of buildings
12	Other	317-421	19849-36194	80607-123013	2010-2012	From city supervision of buildings

Construction areas

Where are new buildings built in Kuopio in the future? The numbers are fractions (in %) of total floor area built in Kuopio.

Construction areas(%)
Obs	City area	Fraction of floor area	Description
1	City center	5	All numbers are guesstimates and not based on data.
2	Petonen	10
3	Puijonlaakso	2
4	Saaristokaupunki	17
5	Neulamäki	1
6	Itkonniemi-Männistö-Linnanpelto	1
7	Haapaniemi	1
8	Särkiniemi-Särkilahti	2
9	Niirala	1
10	Saarijärvi	1
11	Jynkkä	2
12	Julkula	2
13	Inkilänmäki-Peipposenrinne	1
14	Päiväranta	1
15	Länsi-Puijo	1
16	Kelloniemi	1
17	Levänen	2
18	Kettulanlahti	1
19	Rahusenkangas-Kuivinniemi	1
20	Pitkälahti	47

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.

Heating types of buildings

Fractions of houses according heating type(%)
Obs	Building	Heating	Fraction	Old fraction	Description
1	Detached houses	District	68.36	82.5	City of Kuopio
2	Detached houses	Electricity	16.09	8.93
3	Detached houses	Oil	8.50	4.66
4	Detached houses	Wood	5.24	2.86
5	Detached houses	Geothermal	1.81	1.04
6	Row houses	District	83.1	100	See table below
7	Row houses	Electricity	16.9	100	Subtraction
8	Apartment houses	District	91.3	100	See table below
9	Apartment houses	Oil	8.7	100	Subtraction
10	Leisure houses	Electricity	100	100	Assumption
11	Offices	District	89.3	100	See table below
12	Offices	Oil	10.7	100	Subtraction
13	Commercial	District	100	100	Assumption
14	Health and social sector	District	100	100	Assumption
15	Public	District	100	100	Assumption
16	Sports	District	100	100	Assumption
17	Educational	District	85	100	See table below
18	Educational	Oil	15	100	Subtraction
19	Industrial	District	57	100	See table below
20	Industrial	Oil	43	100	Subtraction
21	Other	District	100	100	Assumption

Explanations:

"Old fraction" is based on data according to which the number of detached houses residing in the central city area but NOT district-heated is 700 (Excel-file from city of Kuopio). However, more accurate number is assumed to be around 3000 (email from Arja A. 8.5.2013), which is reflected in the current "Fraction" column.

Proportion of buildings in district heat in main urban area (Pitkälahti-Sorsalo) area of Kuopio. Data from N:\YMAL\Projects\Urgenche\WP10 Kuopio\Scenarios_data_ver6.xlsx

District heat fraction(#,m2)
Obs	Building	Contracts	Fraction of contracts	Floor area	Fraction of floor area
1	Apartment houses	958	0.646	2417061	0.913
2	Detached houses	3241	0.52	587191	0.476
3	Row houses	393	0.313	499020	0.831
4	Industry and warehouses	129	0.286	354666	0.57
5	Office buildings	90	0.625	330593	0.893
6	Schools and research buildings	61	0.592	412216	0.85
7	Public services	167	0	545507	0
8	Other buildings	30	0	123491	0
9	Total	5069	0.454	5269745	0.706
10	Total residential	4592	0.512	3503272	0.782

Future heating types(%)
Obs	Building2	Heating	Fraction	Description
1	Residential	District		Rest of heating
2	Residential	Geothermal	5-10
3	Residential	Electricity	10-15
4	Non-residential	District	100

Energy efficiency in heating

Energy use by energy class of building(kWh/m2/a)
Obs	Efficiency	Heat	User electricity	Water	Description
1	Old	150		30	Pöyry 2011 s.28
2	New	70	50	40	Pöyry 2011 s.32 (2010 SRMK)
3	Low-energy	35	50	40	Personal communication
4	Passive	17.5 - 25	50	40	Pöyry 2011 s.33; Personal communication

Energy efficiency of new buildings in the future(%)
Obs	Efficiency	Constructed	Fraction	Description
1	New	2020-2029	10-20
2	Low-energy	2020-2029		The rest of energy class
3	Passive	2020-2029	25-35
4	New	2030-2039	5-10
5	Low-energy	2030-2039	20-50
6	Passive	2030-2039		The rest of energy class
7	New	2040-2049	0-5
8	Low-energy	2040-2049	10-30
9	Passive	2040-2049		The rest of energy class

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
Chinese green building system: [9] [10]

Baseline energy consumption

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

⇤# : There might be an error in the table: there is only district for non-residential buildings. Therefore they cancel out in the model. CHECK THIS BEFORE PUBLISHING RESULTS! --Jouni (talk) 22:47, 17 February 2014 (EET)

Baseline energy consumption per area unit(kWh/m2/a)
Obs	Building	Heating	Heat	User electricity	Total electricity	Year	Description
1	Detached houses	District	134.74	50	184.74	2010	Calculated from energy company´s data; Pöyry
2	Detached houses	Electricity	130	50	180	2010	Energiapolar; Pöyry
3	Detached houses	Oil	134.74	50	50	2010	Pöyry. Efficiency 90-95% (energiatehokaskoti.fi).
4	Detached houses	Wood	134.74	50	50	2010	Assumption. Efficiency of good kettles 80%(energiatehokaskoti.fi).
5	Detached houses	Geothermal	40	50	90	2010	Assumption
6	Row houses	District	168.88	73.5	73.5	2010	Calculated from energy company´s data
7	Apartment houses	District	172.31	41.7	41.7	2010	Calculated from energy company´s data
8	Commercial	District	161.82	229.6	229.6	2010	Calculated from energy company´s data
9	Offices	District	161.07	93.1	93.1	2010	Calculated from energy company´s data
10	Health and social sector	District	214.97	122.81	122.81	2010	Calculated from energy company´s data
11	Public	District	165.47	110.4	110.4	2010	Calculated from energy company´s data
12	Sports	District	121.38	85.9	85.9	2010	Calculated from energy company´s data
13	Educational	District	170	116.4	116.4	2010	Calculated from energy company´s data
14	Industrial	District	168.44	212.4	212.4	2010	Calculated from energy company´s data
15	Leisure houses	Electricity	2.4	1	3.4	2010	Calculated from energy company´s data
16	Other	District	138.14	170.3	170.3	2010	Calculated from energy company´s data
17	Row houses	Electricity	150	50			Guesstimate
18	Apartment houses	Oil	150	50			Guesstimate
19	Offices	Oil	150	50			Guesstimate
20	Educational	Oil	150	50			Guesstimate
21	Industrial	Oil	150	50			Guesstimate
22	Non-residential buildings		150	50			Guesstimate for Basel
23	Special constructions		150	50			Guesstimate for Basel
24	Single-family houses		150	50			Guesstimate for Basel
25	Multiple-family houses		150	50			Guesstimate for Basel
26	Residential buildings with subsidiary use		150	50			Guesstimate for Basel
27	Buildings with partial residential use		150	50			Guesstimate for Basel

Pöyry 2011. ^[1]

Energiapolar. ^[2]

Energiatehokaskoti.fi/Öljylämmitys ^[3]

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

Baseline energy consumption per volume unit(kWh/m3/a)
Obs	Building	Heating	Heat	User electricity	Year	Description
1	Detached houses	District	42.15	15.67	2010	Calculated from energy company´s data; Energiapolar
2	Detached houses	Electricity	40.66	15.67	2010	Energiapolar
3	Detached houses	Oil	42.15	15.67	2010	Energiapolar
4	Detached houses	Wood	42.15	15.67	2010	Assumption
5	Detached houses	Geothermal	18.56	15.67	2010	Assumption
6	Row houses	District	53.25	23.16	2010	From energy company
7	Apartment houses	District	49.2	11.9	2010	From energy company
8	Commercial	District	28.65	40.64	2010	From energy company
9	Offices	District	36.32	20.99	2010	From energy company
10	Health and social sector	District	55.2	31.53	2010	From energy company
11	Public	District	32.21	21.49	2010	From energy company
12	Sports	District	18.37	13	2010	From energy company; Electricity value comes from city´s renovation data
13	Educational	District	40.23	27.54	2010	From energy company
14	Industrial	District	30.57	38.55	2010	From energy company
15	Leisure houses	Electricity	0.68	0.29	2010	From energy company
16	Other	District	29.88	36.83	2010	From energy company

Renovations and their impact

Fraction of houses renovated per year(%)
Obs	Age	Result	Description
1	0	0	Assumption Pöyry 2011 s.27 says that on average, 3 % of buildings are renovated.
2	20	1.5	Assumption Result applies to buildings older than the value in the Age column.
3	30	4	Assumption
4	50	1.5	Assumption
5	100	1	Assumption
6	1000	1	Assumption

Energy saving potential of different renovations(%,kWh/m2/a)
Obs	Efficiency	Building2	Renovation	Relative	Absolute	Renovation details	Description
1	Old	Residential	Windows	15	25	New windows and doors	Pöyry 2011
2	Old	Residential	Technical systems	50	75	New windows, sealing of building's sheath, improvement of building's technical systems	Pöyry 2011
3	Old	Residential	Sheath reform	65	100	New windows, sealing of building's sheath, improvement of building's technical systems, significant reform of building's sheath	Pöyry 2011
4	Old	Non-residential	General	15	-	General renovation	Pöyry 2011
5	Old		None	0	0	Renovation not done
6	New		None	0	0	Renovation not done
7	Low-energy		None	0	0	Renovation not done
8	Passive		None	0	0	Renovation not done

Popularity of renovation types(%)
Obs	Renovation	Fraction	Description
1	Windows	10-20
2	Technical systems	20-25
3	Sheath reform	15-20
4	General		The rest of renovations

Building type comparisons(-)
Obs	Building	Building2
1	Detached houses	Residential
2	Row houses	Residential
3	Apartment houses	Residential
4	Commercial	Non-residential
5	Offices	Non-residential
6	Industrial	Non-residential
7	Public	Non-residential
8	Educational	Non-residential
9	Health and social sector	Non-residential
10	Sports	Non-residential
11	Leisure houses	Non-residential
12	Other	Non-residential

Indoor environment quality (IEQ) factors

IEQ factors(h-1,%,%,%,-,%,%,%,Bq/m3)
Obs	Building	Heating	Ventilation rate	Dampness%	Smoking%	In noise areas%	Radon	Description
1	Detached houses	District	0.71 (0.3-1.12)	5-16.5	2.35 (1.4-3.4)	15	100 (95-105)	Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
2	Detached houses	Electricity	0.71 (0.3-1.12)	5-16.5	2.35 (1.4-3.4)	15	100 (95-105)	Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
3	Detached houses	Oil	0.71 (0.3-1.12)	5-16.5	2.35 (1.4-3.4)	15	100 (95-105)	Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
4	Detached houses	Wood	0.71 (0.3-1.12)	5-16.5	2.35 (1.4-3.4)	15	100 (95-105)	Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
5	Detached houses	Geothermal	0.71 (0.3-1.12)	5-16.5	2.35 (1.4-3.4)	15	100 (95-105)	Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
6	Row houses	District	0.71 (0.3-1.12)	5-16.5	2.35 (1.4-3.4)	21	100 (95-105)	Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
7	Apartment houses	District	0.71 (0.3-1.12)	5-16.5	2.35 (1.4-3.4)	30	100 (95-105)	Gens, 2012; Turunen et al. 2010; Haverinen-Shaughnessy, 2012; Assumption based on city´s data; Kurttio 2006
8	Leisure houses	Electricity
9	Offices	District			0			Assumption
10	Commercial	District			0			Assumption
11	Health and social sector	District			0			Assumption
12	Public	District			0			Assumption
13	Sports	District			0			Assumption
14	Educational	District		24	0			Haverinen-Shaughnessy et al. 2012; Assumption
15	Industrial	District			0			Assumption
16	Other	District

Gens 2012 ^[4]

Haverinen-Shaughnessy 2010 ^[5]

Haverinen-Shaughnessy et al. 2012 ^[6]

Turunen et al. 2010 ^[7]

Regulations regarding energy consumption of buildings

Maximum allowed energy consumption per unit (= E-value)(kWh/m2/a)
Obs	Building	Year	E-value	Description
1	Detached houses	2012 forward	204	Heated net area <120 m2; Finland´s Environmental Administration
2	Row houses	2012 forward	150	Finland´s Environmental Administration
3	Apartment houses	2012 forward	130	Finland´s Environmental Administration
4	Shops and other commercial buildings	2012 forward	240	Finland´s Environmental Administration
5	Offices	2012 forward	170	Finland´s Environmental Administration
6	Health and social sector: Hospitals	2012 forward	450	Finland´s Environmental Administration
7	Health and social sector: Health care centers etc.	2012 forward	170	Finland´s Environmental Administration
8	Public	2012 forward	240	Finland´s Environmental Administration
9	Sports	2012 forward	170	Does not apply to swimming- and ice halls; Finland´s Environmental Administration
10	Educational	2012 forward	170	Finland´s Environmental Administration
11	Industrial	2012 forward	-	E-value must be calculated but there´s no limit for it; Finland´s Environmental Administration
12	Leisure buildings	2012 forward	-	E-value must be calculated but there´s no limit for it; Finland´s Environmental Administration
13	Other	2012 forward	-	E-value must be calculated but there´s no limit for it; Finland´s Environmental Administration

Emission factors for heating

Emission factors for wood heating(PJ /a; mg /MJ)
Obs	Type	Activity in Finland	PM2.5 emission factor	Description
1	Residential buildings	34.2 (30.8-37.6)		Karvosenoja et al. 2008
2	Primary wood-heated residential buildings	20.2 (16.6-23.9)		Karvosenoja et al. 2008
3	Manual feed boilers with accumulator tank	5.42 (3.89-7.22)	80.0 (37.6-150)	Karvosenoja et al. 2008
4	Manual feed boilers without accumulator tank	2.67 (1.67-3.87)	700 (329-1310)	Karvosenoja et al. 2008
5	Automatic feed wood chip boilers	1.46 (1.01-2)	50.0 (23.5-93.9)	Karvosenoja et al. 2008
6	Automatic feed pellet boilers	0.102 (0.0693-0.142)	30.0 (14.1-56.3)	Karvosenoja et al. 2008
7	Iron stoves	0.142 (0.0976-0.196)	700 (329-1310)	Karvosenoja et al. 2008
8	Other stoves and ovens	10.2 (7.86-12.8)	140 (65.8-263)	Karvosenoja et al. 2008
9	Low-emission stoves	0	80 (37.6-150)	Karvosenoja et al. 2008
10	Open fireplaces	0.163 (0.111-0.224)	800 (376-1500)	Karvosenoja et al. 2008
11	Supplementary wood-heated residential buildings	14.0 (10.7-17.4)		Karvosenoja et al. 2008
12	Iron stoves	0.212 (0.135-0.316)	700 (329-1310)	Karvosenoja et al. 2008
13	Other stoves and ovens	13.6 (10.4-16.9)	140 (65.8-263)	Karvosenoja et al. 2008
14	Low-emission stoves	0	80 (37.6-150)	Karvosenoja et al. 2008
15	Open fireplaces	0.222 (0.14-0.332)	800 (376-1500)	Karvosenoja et al. 2008
16	Recreational buildings	5.00 (4.50-5.50)		Karvosenoja et al. 2008
17	Iron stoves	0.782 (0.372-1.37)	700 (329-1310)	Karvosenoja et al. 2008
18	Other stoves and ovens	3.96 (3.19-4.59)	140 (65.8-263)	Karvosenoja et al. 2008
19	Open fireplaces	0.262 (0.118-0.477)	800 (376-1500)	Karvosenoja et al. 2008

Karvosenoja et al. 2008 ^[8]

The table below contains the current situation for Kuopio and Basel. Kuopio uses 'District', and Basel uses 'Long-distance heating'.

Fuel use in different heating types(-)
Obs	Heating	Burner	Fuel	Fraction	Description
1	Wood	Domestic	Wood	1
2	Oil	Domestic	Light oil	1
3	Gas	Domestic	Gas	1
4	Heating oil	Domestic	Light oil	1
5	Other sources	Domestic	Other sources	1
6	No energy source	Domestic	Other sources	1
7	Geothermal	Grid	Electricity	0.3
8	Centrifuge, hydro-extractor	Grid	Electricity	0.3	Not quite clear what this is but presumably a heat pump.
9	Solar heater/ collector	Grid	Electricity	0.1	Use only; life-cycle impacts omitted.
10	Electricity	Grid	Electricity	1
11	Long-distance heating	Large fluidized bed	Gas	1
12	Coal	Large fluidized bed	Coal	1
13	District	Large fluidized bed	Wood	0.1
14	District	Large fluidized bed	Peat	0.85
15	District	Large fluidized bed	Heavy oil	0.05

Emission factors of energy production(mg /MJ)
Obs	Burner	Fuel	PM2.5	CO2	CO2official	Description
1	Domestic	Wood	80.0 (37.6-150)	74200	0	Manual feed boilers with accumulator tank (Karvosenoja et al. 2008)
2	Domestic	Light oil	0-10	74200	74200	Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
3	Domestic	Other sources	0-10	74200	74200	Same as oil.
4	Domestic	Gas	0-3	55650	55650	For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
5	Large fuildized bed	Gas	0-3	55650	55650	For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
6	Large fluidized bed	Coal	2-20	106000	106000	Same as peat.
7	Large fluidized bed	Wood	2-20	74200	0	Leijupoltto 100-300 MW Emission factors for burning processes. Karvosenoja et al., 2008
8	Large fluidized bed	Peat	2-20	106000	106000	Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
9	Large fluidized bed	Heavy oil	8-22	106000	106000	Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
10	Grid	Electricity	1-10	53000	212000	50 % of large-scale burning (because of nuclear and hydro). Heavy oil 279 kg /MWh. Officially, electricity is not CHP but requires a double amount of coal to produce it.

See Emission factors for burning processes

Calculations for ovariables

+ Show code - Hide code


library(OpasnetUtils)

buildingStock <- Ovariable(
	name = 'buildingStock',
	ddata = 'Op_en5932',
	subset = 'Building registry data'
)

energyUse <- Ovariable(
	name = 'energyUse',
	ddata = 'Op_en5932',
	subset = 'Baseline energy consumption per area unit'
)

heatingShares <- Ovariable(
	name = 'heatingShares',
	ddata = 'Op_en5932',
	subset = 'Fractions of houses according heating type'
)

heatingSharesNew <- Ovariable(
	name = 'heatingSharesNew',
	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'
)
renovation@data$Age <- as.numeric(levels(renovation@data$Age)[renovation@data$Age])

construction <- Ovariable(
	name = 'construction',
	ddata = 'Op_en5932',
	subset = 'Floor area of new houses and additional construction per year'
)

constructionAreas <- Ovariable(
	name = 'constructionAreas',
	ddata = 'Op_en5932',
	subset = 'Construction areas'
)

efficienciesNew <- Ovariable(
	name = 'efficienciesNew',
	ddata = 'Op_en5932',
	subset = 'Energy efficiency 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)'
)

savingPotential <- Ovariable(
	name = 'savingPotential',
	ddata = 'Op_en5932',
	subset = 'Energy saving potential of different renovations'
)
levels(savingPotential@data$Building2)[levels(savingPotential@data$Building2) == ""] <- NA

renovationShares <- Ovariable(
	name = 'renovationShares',
	ddata = 'Op_en5932',
	subset = 'Popularity of renovation types'
)

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'
)
buildingTypes@data$buildingTypesResult <- 1

emissionFactors <- Ovariable(
	name = 'emissionFactors',
	ddata = 'Op_en5932',
	subset = 'Emission factors of energy production'
)

fuelTypes <- opbase.data("Op_en5932.fuel_use_in_different_heating_types")
fuelTypes <- fuelTypes[colnames(fuelTypes) != "Obs"]
fuelTypes <- Ovariable("fuelTypes", data = fuelTypes)


emissionLocations <- Ovariable("emissionLocations",
	ddata = 'Op_en5932',
	subset = 'Emission locations'
)
emissionLocations@data$emissionLocationsResult <- 1

## Additional index needed in followup: Year, Efficiency

eventyear <- Ovariable("eventyear", data = data.frame(
	Constructed = factor(
		c("1799-1899", "1900-1909", "1910-1919", "1920-1929", "1930-1939", "1940-1949",
			"1950-1959", "1960-1969", "1970-1979", "1980-1989", "1990-1999",
			"2000-2010", "2011-2019", "2020-2029", "2030-2039", "2040-2049"
		), 
		ordered = TRUE
		), 
	Eventyear = c(1880, 1905 + 0:14 * 10),
	Result = 1
))

efficiencies <- Ovariable("efficiencies", data = data.frame(
	Constructed = factor(
		c("1799-1899", "1900-1909", "1910-1919", "1920-1929", "1930-1939", "1940-1949",
			"1950-1959", "1960-1969", "1970-1979", "1980-1989", "1990-1999",
			"2000-2010", "2011-2019"
		), 
		ordered = TRUE
	), 
	Efficiency = factor(c(rep("Old", 11), rep("New", 2))),
	Result = 1
))

objects.store(
	buildingStock, 
	energyUse,
	energy.per.volume,
	heatingShares,
	heatingSharesNew,
	renovation,
	renovationShares,
	construction,
	constructionAreas,
	efficienciesNew,
	savingPotential,
	energy.classes.of.buildings,
	buildingTypes,
	emissionFactors,
	fuelTypes,
	emissionLocations,
	eventyear,
	efficiencies
)

cat("Objects buildingStock, 
	energyUse,
	energy.per.volume,
	heatingShares,
	heatingSharesNew,
	renovation,
	renovationShares,
	construction,
	constructionAreas,
	efficienciesNew,
	savingPotential,
	energy.classes.of.buildings,
	buildingTypes,
	emissionFactors,
	fuelTypes,
	emissionLocations,
	eventyear,
	efficiencies
initiated!\n")

Other preliminary calculations

+ Show code - Hide code

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))

Keywords

References

↑ Pöyry 2011: Kuopion kasvihuonekaasupäästöjen vähentämismahdollisuudet v 2020 mennessä. [1]
↑ Energiapolar/Arvioi sähkönkulutus[2]
↑ Energiatehokaskoti.fi/Öljylämmitys[3]
↑ Gens 2012 [4]
↑ Haverinen-Shaughnessy 2010 [5]
↑ Haverinen-Shaughnessy et al. 2012 [6]
↑ Turunen et al. 2010 [7]
↑ Karvosenoja et al. 2008 [8]

Related files

[1] Pöyry 2011: Kuopion kasvihuonekaasupäästöjen vähentämismahdollisuudet v 2020 mennessä. [1]

[2] Energiapolar/Arvioi sähkönkulutus[2]

[3] Energiatehokaskoti.fi/Öljylämmitys[3]

[4] Gens 2012 [4]

[5] Haverinen-Shaughnessy 2010 [5]

[6] Haverinen-Shaughnessy et al. 2012 [6]

[7] Turunen et al. 2010 [7]

[8] Karvosenoja et al. 2008 [8]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

Building stock in Kuopio

Contents

Question

Answer

Rationale

Dispersion modelling

Building stock

Heating types of buildings

Energy efficiency in heating

Baseline energy consumption

Renovations and their impact

Indoor environment quality (IEQ) factors

Regulations regarding energy consumption of buildings

Emission factors for heating

Calculations for ovariables

Other preliminary calculations

See also

Keywords

References

Related files

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