Energy use of buildings

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Moderator:Marjo (see all)

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Question

How to model the use of energy of buildings based on either annual consumption per floor area, or energy efficiency per floor area per indoor-outdoor temperature difference?

Answer

An example code for fetching and using the variables.

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## This code is Op_en5488/ on page [[Energy use of buildings]]

library(OpasnetUtils)

objects.latest("Op_en5488", code_name = "EnergyConsumerDemand") # [[Energy use in buildings]]

cat("These ovariables must be obtained from somewhere to use this method:\n")

oprint(EnergyConsumerDemand@dependencies)

Rationale

Input

**Variables needed to calculate the EnergyConsumerDemand. Note that there are several different methods available, and temperature data is not needed in an annual energy version.**
Dependencies	Measure	Indices	Missing data
buildings (from the model).	Floor area of the building stock to be heated	Typical indices: Building, Heating, Efficiency, Renovation	You can use value 1 to calculate energy need per 1 m² floor area.
temperene (fairly generic data for a given cultural and climatic area, e.g. from Energy use of buildings)	Energy need per floor area and indoor-outdoor temperature difference (W /m² /K)	Required indices: Consumable, Fuel (Commodity). Typical indices: Building, Heating.	if this data is missing, you can only calculate building stock but nothing further.
nontemperene (fairly generic data for a given cultural and climatic area, e.g. from Energy use of buildings)	Energy need for hot water and other non-temperature-dependent activities	Required indices: Consumable, Fuel (Commodity).	Use 0 to calculate energy demand excluding non-heating energy use.
temperatures (location-specific data)	Average outdoor temperatures for particular temperature bins.	Reuired indices: Temperature.	If missing, use the annual energy version.
temperdays (location-specific data)	Number of days per year for particular temperature bins.	Required indices: Temperature.	If missing, use the annual energy version.
efficiencyRatio (fairly generic data for a cultural and climatic area, e.g. from Energy use of buildings)	Relative energy consumption compared with the efficiency group Old.	Required indices: Efficiency. Typical indices: Time, Building.	If no data, use 1 as default.
renovationRatio (fairly generic data for a cultural and climatic area, e.g. from Energy use of buildings)	Relative energy consumption compared with the Renovation location None.	Required indices: Renovation. Typical indices: Building.	If no data, use 1 as default.

Annual calculations

The code below assumes annual energy consumption factors per floor area (kWh /m² /a). No temperature data is needed.

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle Q_{e,r} = \sum_b B_{b,e,r} F_b E_e R_r,}

where

Q = Energy used for heating and cooling (kWh /a)
B = floor area of a building stock indexed by renovation and efficiency (m²)
F = energy consumption factor (kWh / m² /a)
E = relative efficiency of a building stock based on energy class when built (no unit)
R = relative efficiency of a building stock based on energy class after renovated (no unit)
indices used:
- b = building type
- e = efficiency class of building
- r = renovation class of building

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### This code is Op_en5488/energyUseAnnual on page [[Energy use of buildings]].

library(OpasnetUtils)

### energyUse is the energy consumption due to heating of a given building stock.

energyUse <- Ovariable("energyUse", 
	dependencies = data.frame(
		Name = c(
			"energyFactor",
			"efficiencyRatio",
			"renovationRatio",
			"buildings"
		),
		Ident = c(
			"Op_en5488/energyFactor",
			"Op_en5488/efficiencyRatio",
			"Op_en5488/renovationRatio",
			NA
		)
	),
	formula = function(...) {
		
		out <- buildings * energyFactor * efficiencyRatio * renovationRatio
		out <- unkeep(out, prevresults = TRUE, sources = TRUE)
		out <- oapply(out, cols = c("Building"), FUN = sum)
		
		return(out)
	}
)

objects.store(energyUse)
cat("Ovariable energyUse stored.\n")

Temperature-dependent calculations

The code below assumes energy consumption factors relative to floor area (W /m² /K). Local temperature data must be given in either individual or aggregated way. Individual way has temperature data for all timepoints (e.g. days or hours) of the given year, and heatingTime = 1. Aggregated way has a specific Temperature index (e.g. very cold, cold, cool etc) in both ovariables temperature and heatingTime. The ovariable temperature tells what is the actual temperature when it is "very cold", and heatingTime tells how many hours it is "very cold" during the year.

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle Q_{e,r,t} = \sum_b (B_{b,e,r} U_b (17 - T_t) E_e R_r + W_b) t_t,}

where

Q = Energy used for heating and cooling (kWh /a)
B = floor area of a building stock indexed by renovation and efficiency (m²)
U = energy consumption factor per floor area for a building type (W /m² /K)
T = temperature outside (assumes that no heating is needed if outside temperature is 17 degrees Celsius)
E = relative efficiency of a building stock based on energy class when built (no unit)
R = relative efficiency of a building stock based on energy class after renovated (no unit)
W = heating need of hot water (W)
t = time spent in a particular outdoor temperature (h /a)
indices used:
- b = building type
- e = efficiency class of building
- r = renovation class of building
- t = ambient temperature class

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### This code is Op_en5488/energyUseTemperature on page [[Energy use of buildings]].

library(OpasnetUtils)

### EnergyUse of a given building stock when U values are available.

energyUse <- Ovariable("energyUse", 
	dependencies = data.frame(
		Name = c(
			"temperene",
			"efficiencyRatio",
			"renovationRatio",
			"nontemperene",
			"buildings",
			"temperatures",
			"temperdays" # fetched here but it is only calculated after energyBalance optimisation
		),
		Ident = c(
			"Op_en5488/temperene",
			"Op_en5488/efficiencyRatio",
			"Op_en5488/renovationRatio",
			"Op_en5488/nontemperene",
			NA,
			NA,
			NA
		)
	),
	formula = function(...) {

		#NOTE! IF OTHER INPUTS ARE INDEXED BY Building, THIS MUST BE CHANGED!
		#NOTE2! buildings must have index Heating.
		buil <- oapply(buildings, cols = "Building", FUN = sum)

		# We assume that energy efficiency of building structures also reduces cooling need.
		tempe <- temperene * efficiencyRatio * renovationRatio

		# We assume that 17 °C is thermoneutral with no heating.
		heat <- buil * (17 - temperatures) * tempe

		# Only positive heating is considered (i.e., cooling is not here)
		result(heat) <- pmax(0, result(heat)) 
		heat@output <- heat@output[heat@output$Consumable == "Heating" , ]

		# We assume that 24 °C is thermoneutral with no cooling.
		cool <- buil * (temperatures - 24) * tempe
		# Only positive cooling is considered (i.e., heating is not here)
		result(cool) <- pmax(0, result(cool)) 
		cool@output <- cool@output[cool@output$Consumable %in% c("Cooling", "District cooling", "Electric cooling") , ]
		cool@output$Heating <- "Not heating"
		
		other <- buil * nontemperene
		other@output$Heating <- "Not heating"
		
		out <- OpasnetUtils::combine(OpasnetUtils::combine(heat, cool), other)
		out <- unkeep(out, prevresults = TRUE, sources = TRUE)
		out@output <- fillna(out@output, marginals = "Temperature")
				
		return(out)
	}
)

objects.store(energyUse)
cat("Ovariable energyUse stored.\n")

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### This code is Op_en5488/EnergyConsumerDemand on page [[Energy use of buildings]].

library(OpasnetUtils)

### EnergyUse of a given building stock when U values are available.

EnergyConsumerDemand <- Ovariable("EnergyConsumerDemand", 
		dependencies = data.frame(
				Name = c(
						"temperene",
						"efficiencyRatio",
						"renovationRatio",
						"nontemperene",
						"buildings",
						"temperatures",
						"temperdays" # fetched here but it is only calculated after energyBalance optimisation
				),
				Ident = c(
						"Op_en5488/temperene",
						"Op_en5488/efficiencyRatio",
						"Op_en5488/renovationRatio",
						"Op_en5488/nontemperene",
						NA,
						NA,
						NA
				)
		),
		formula = function(...) {
			
			#NOTE! IF OTHER INPUTS ARE INDEXED BY Building, THIS MUST BE CHANGED!
			#NOTE2! buildings must have index Heating.
			#NOTE3! Heating, cooling and Other should probably be in different ovariables instead of being here:
			# multiple variables necessitates explicit handling in other dependent variables, which is bad.
			buil <- oapply(buildings, cols = "Building", FUN = sum)
			
			# Remove Fuel column as unnecessary and disruptive
			temperene <- temperene[,!colnames(temperene@output) %in% "Fuel"]
			nontemperene <- nontemperene[,!colnames(nontemperene@output) %in% "Fuel"]
			
			# We assume that energy efficiency of building structures also reduces cooling need.
			tempe <- temperene * efficiencyRatio * renovationRatio
			
			# We assume that 17 °C is thermoneutral with no heating.
			heat <- buil * (17 - temperatures) * tempe[tempe$Consumable == "Heating", ]#!colnames(tempe@output) %in% "Fuel"]
			
			# Only positive heating is considered (i.e., cooling is not here)
			result(heat) <- pmax(0, result(heat)) 
			#heat@output <- heat@output[heat@output$Consumable == "Heating" , ]
			
			# We assume that 24 °C is thermoneutral with no cooling.
			cool <- buil * (temperatures - 24) * tempe[tempe$Consumable %in% c("Cooling", "District cooling", "Electric cooling"),]
			# Only positive cooling is considered (i.e., heating is not here)
			result(cool) <- pmax(0, result(cool)) 
			#cool@output <- cool@output[cool@output$Consumable %in% c("Cooling", "District cooling", "Electric cooling") , ]
			#cool@output$Heating <- "Not heating"
			
			other <- buil * nontemperene
			#other@output$Heating <- "Not heating"
			
			out <- OpasnetUtils::combine(heat, cool, other)
			out <- unkeep(out, prevresults = TRUE, sources = TRUE)
			out@output <- fillna(out@output, marginals = "Temperature")
			
			#out[1:10]@output
			#nrow(out@output)
			
			return(out)
		}
)

objects.store(EnergyConsumerDemand)
cat("Ovariable EnergyConsumerDemand stored.\n")

Baseline energy consumption

Heat reflects the energy need for heating in situations where the outdoor temperature is below 17 °C. Cooling reflects the cooling need (measured as thermal energy, not electricity!) in situations where the outdoor temperature is above 24 °C. This is not a U value, because it is about energy use per floor area, not about heat loss through building structures per m². For estimating temperene, we take the total energy consumption in Helsinki and divide that with the total floor area and average temperature difference, see Helsinki energy consumption#U values based on overall data.

Energy use per area and temperature difference(W /K /m2)
Obs	Consumable	Fuel	Energy flow	Description
1	Heating	Heat	1.66	See Helsinki energy consumption: 6921.65/24/365/38990000/(17-4.8)*1E+9
2	District cooling	Cooling	0.3	Guesswork. This uses centralised system.
3	Electric cooling	Electricity	0.3	Guesswork. This uses apartment-specific appliances.

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## This is code Op_en5488/temperene on page [[Energy use of buildings]]
library(OpasnetUtils)

temperene <- Ovariable("temperene", ddata = "Op_en5488", subset = "Energy use per area and temperature difference")

objects.store(temperene)
cat("Ovariable temperene stored.\n")

Temperature-independent energy consumption per floor area.

Temperature-independent energy use per area(W /m2)
Obs	Consumable	Fuel	Energy intensity	Description
1	Consumer electricity	Electricity	5	Assumes 50 kWh /m2 /a (see below)
2	Hot water	Heat	4	Assumes that hot water is ca. 20 % of energy need of heating: 6921.65/24/365/389900001E+90.2

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## This is code Op_en5488/nontemperene on page [[Energy use of buildings]]
library(OpasnetUtils)

nontemperene <- Ovariable("nontemperene", ddata = "Op_en5488", subset = "Temperature-independent energy use per area")

objects.store(nontemperene)
cat("Ovariable nontemperene stored.\n")

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		168.88	73.5	73.5	2010	Calculated from energy company´s data. Based on district
7	Apartment houses		172.31	41.7	41.7	2010	Calculated from energy company´s data. Based on district
8	Commercial		161.82	229.6	229.6	2010	Calculated from energy company´s data. Based on district
9	Offices		161.07	93.1	93.1	2010	Calculated from energy company´s data. Based on district
10	Health and social sector		214.97	122.81	122.81	2010	Calculated from energy company´s data. Based on district
11	Public		165.47	110.4	110.4	2010	Calculated from energy company´s data. Based on district
12	Sports		121.38	85.9	85.9	2010	Calculated from energy company´s data. Based on district
13	Educational		170	116.4	116.4	2010	Calculated from energy company´s data. Based on district
14	Industrial		168.44	212.4	212.4	2010	Calculated from energy company´s data. Based on district
15	Leisure houses		2.4	1	3.4	2010	Calculated from energy company´s data. Based on electricity
16	Other		138.14	170.3	170.3	2010	Calculated from energy company´s data
17	Non-residential buildings	No energy source	113				Urgenche data for Basel
18	Non-residential buildings	Heating oil	113				Urgenche data for Basel
19	Non-residential buildings	Coal	113				Urgenche data for Basel
20	Non-residential buildings	Gas	113				Urgenche data for Basel
21	Non-residential buildings	Electricity	113				Urgenche data for Basel
22	Non-residential buildings	Wood	113				Urgenche data for Basel
23	Non-residential buildings	Centrifuge, hydro-extractor	113				Urgenche data for Basel
24	Non-residential buildings	Solar heater/ collector	113				Urgenche data for Basel
25	Non-residential buildings	Long-distance heating	113				Urgenche data for Basel
26	Non-residential buildings	Other sources	113				Urgenche data for Basel
27	Special constructions		86-106				Urgenche data for Basel (Mixed use)
28	Single-family houses		69				Urgenche data for Basel
29	Multiple-family houses		54-106				Urgenche data for Basel
30	Residential buildings with subsidiary use		86-106				Urgenche data for Basel
31	Buildings with partial residential use		86-106				Urgenche data for Basel

Pöyry 2011. ^[1]
Energiapolar. ^[2]
Energiatehokaskoti.fi/Öljylämmitys ^[3]

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# This code is Op_en5488/energyFactor on page [[Energy use of buildings]]
library(OpasnetUtils)

energyFactor <- Ovariable("energyFactor",
	dependencies = data.frame(
		Name = c("dummy")
	),
	formula = function (...) {
	out <- Ovariable('energyFactor',
		ddata = 'Op_en5488',
		subset = 'Baseline energy consumption per area unit'
	)
	out@data <- out@data[
		out@data[["Energy use"]] == "Heat" , 
		colnames(out@data) != "Energy use"
	]
	levels(out@data$Heating)[levels(out@data$Heating) == ""] <- NA
	out@data <- fillna(out@data, "Heating")
	return(EvalOutput(out))
	}
)

objects.store(energyFactor)
cat("Object energyFactor initiated!\n")

Energy efficiency in heating

What is the relative energy consumption of different efficiency classes compared with Old? This table tells that with some background information about heat (in kWh/m2/a), electricity, and water consumption.

Energy use by energy class of building(ratio)
Obs	Efficiency	Ratio	Heat	User electricity	Water	Description
1	Traditional	1.2-1.4	200			Guesstimate
2	Old	1	150		30	Pöyry 2011 s.28
3	New	0.4-0.5	70	50	40	Pöyry 2011 s.32 (2010 SRMK)
4	Low-energy	0.2-0.25	35	50	40	Personal communication
5	Passive	0.1-0.16	17.5 - 25	50	40	Pöyry 2011 s.33; Personal communication

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# This code is Op_en5488/efficiencyRatio on page [[Energy use of buildings]]
library(OpasnetUtils)

efficiencyRatio <- Ovariable(
	name = 'efficiencyRatio',
	ddata = 'Op_en5488',
	subset = 'Energy use by energy class of building'
)

objects.store(efficiencyRatio)
cat("Object efficiencyRatio initiated!\n")

Energy efficiency of buildings when they are built(%)
Obs	Efficiency	Constructed	Fraction	Description
1	Traditional	1800-1944	100
2	Old	1945-1994	100
3	New	1995-2019	100
4	New	2020-2029	10-20
5	Low-energy	2020-2029		The rest of energy class
6	Passive	2020-2029	25-35
7	New	2030-2039	5-10
8	Low-energy	2030-2039	20-50
9	Passive	2030-2039		The rest of energy class
10	New	2040-2070	0-5
11	Low-energy	2040-2070	10-30
12	Passive	2040-2070		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: [3] [4]

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#This code is Op_en5488/efficiencyShares from page [[Energy use of buildings]].
library(OpasnetUtils)

efficiencyShares <- Ovariable("efficiencyShares", 
	dependencies = data.frame(
		Name = c("dummy")
	),
	formula = function(...) {
		es <- Ovariable(
			name = 'es',
			ddata = 'Op_en5488',
			subset = 'Energy efficiency of buildings when they are built'
		)
		es <- findrest(es, cols = "Efficiency", total = 100) / 100

		constructed <- unique(es@data$Constructed)
		temp <- ((strsplit(as.character(constructed), split = "-")))

		y <- data.frame()
		for(i in 1:length(constructed)) {
			y <- rbind(
				y,
					data.frame(
					Constructed = constructed[i],
					Time = seq(from = as.numeric(temp[[i]][1]), to = as.numeric(temp[[i]][2]), by = 5),
					Result = 1
				)
			)
		}
		y <- Ovariable(output = y, marginal = c(TRUE, TRUE, FALSE))

		out <- unkeep(es * y, cols = "Constructed", prevresults = TRUE, sources = TRUE)

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

dummy <- 1

objects.store(efficiencyShares, dummy)
cat("Objects efficiencyShares, dummy initiated!\n")

Impact of renovations

Energy saving potential of different renovations(ratio,kWh/m2/a)
Obs	Renovation	Relative	Absolute	Renovation details	Description
1	Windows	0.85	25	New windows and doors	Pöyry 2011
2	Technical systems	0.50	75	New windows, sealing of building's sheath, improvement of building's technical systems	Pöyry 2011
3	Sheath reform	0.35	100	New windows, sealing of building's sheath, improvement of building's technical systems, significant reform of building's sheath	Pöyry 2011
4	General	0.85	-	General renovation	Pöyry 2011
5	None	1	0	Renovation not done

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# This code is Op_en5488/renovationRatio on page [[Energy use of buildings]]
library(OpasnetUtils)

renovationRatio <- Ovariable(
	name = 'renovationRatio',
	ddata = 'Op_en5488',
	subset = 'Energy saving potential of different renovations'
)
renovationRatio@data$Renovation <- factor(
	renovationRatio@data$Renovation, 
	levels = c("None", "General", "Windows", "Technical systems", "Sheath reform"),
	ordered = TRUE
)

objects.store(renovationRatio)
cat("Object renovationRatio initiated!\n")

Data not used

This section contains data that are relevant for the topic but are not used in the actual method calculations.

----#: . 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)
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

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

Keywords

References

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

Related files

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

[2] Energiapolar/Arvioi sähkönkulutus[1]

[3] Energiatehokaskoti.fi/Öljylämmitys[2]

[1]

[2]

[3]

Energy use of buildings

Contents

Question

Answer

Rationale

Input

Annual calculations

Temperature-dependent calculations

Baseline energy consumption

Energy efficiency in heating

Impact of renovations

Data not used

Regulations regarding energy consumption of buildings

See also

Keywords

References

Related files

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