Training exposure: Difference between revisions

Revision as of 14:11, 12 May 2012

This page is a knowledge crystal of subtype variable. The page identifier is Op_en5674
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Question

What is the exposure in the Training assessment?

Answer

Training exposure: Difference between revisions(-)
Obs	Year	Unit	Result	Description
1	2012	ug/m3	10
2	2020	ug/m3	20

+ Show code - Hide code


out <- formula.Op_en5674(dependencies.Op_en5674)
cat("Computed training exposures.\n")
print(out)

Rationale

Formula

+ Show code - Hide code


formula.Op_en5674 <- function(x) {
	data <- tidy(op_baseGetData("opasnet_base", x$curves[1]), direction = "wide")
	curves <- strsplit(as.character(data$Parameters), split = ",")
	coefficients <- data.frame(array(NA, dim = c(length(curves), 10)))

	for(j in 1:length(curves)) {
		coefficients[j, 1:length(curves[[j]])] <- curves[[j]]
	}
	coefficients[nrow(coefficients) + 1, 1] <- x$deviation
	for(k in 1:ncol(coefficients)) {
		coefficients[, k] <- ifelse(is.na(coefficients[, k]), 0, as.numeric(coefficients[, k]))
	}
	x.new <- polyroot(as.vector(colSums(coefficients)))
	x.new <- ifelse(abs(Im(x.new)) < 1e-6, Re(x.new), 1e+30) # Take only the real roots.
	x.new <- x.new[abs(x.new) == min(abs(x.new))] # Pick the root that is closest to zero (=BAU).
	y.new <- 0
	for(k in 1:ncol(coefficients)) {
		y.new <- y.new + coefficients[, k] * x.new^(k-1)
	}
	temp <- data[1, ]
	temp[1, ] <- "Deviation"
	data <- rbind(data, temp)
	data <- cbind(data, data.frame(Result = y.new))
	return(data)
}

dependencies.Op_en5674 <- list(
	curves = c("Op_en5478", "Cost curves for energy"),
	bau = c("Op_en5469", "Energy balance in Kuopio"),
	deviation = -5
)

**Urgenche research project 2011 - 2014: city-level climate change mitigation**
Urgenche pages Urgenche main page · Category:Urgenche · Urgenche project page (password-protected) Relevant data Building stock data in Urgenche‎ · Building regulations in Finland · Concentration-response to PM2.5 · Emission factors for burning processes · ERF of indoor dampness on respiratory health effects · ERF of several environmental pollutions · General criteria for land use · Indoor environment quality (IEQ) factors · Intake fractions of PM · Land use in Urgenche · Land use and boundary in Urgenche · Energy use of buildings Relevant methods Building model · Energy balance · Health impact assessment · Opasnet map · Help:Drawing graphs · OpasnetUtils‎ · Recommended R functions‎ · Using summary tables‎ 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) Energy balances Energy balance in Basel · Energy balance in Kuopio · Energy balance in Stuttgart · Energy balance in Suzhou

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@@ Line 4: / Line 4: @@
 == Question ==
-What costs curves should be used for different energy products?
+What is the exposure in the [[Training assessment]]?
 == Answer ==
-<t2b index="Row,Actor,Fuel,Use,Reference,Unit,Observation" locations="Result,Description" unit="-">
+<t2b index="Year,Observation" locations="Unit,Result,Description" unit="-">
-|Kuopion energia|Coal and peat|Demand|Current|€,ton|0, -0.06|
+|ug/m3|10|
-|Kuopio market|Coal and peat|Supply|Current|€,ton|0, 0.5|
+|ug/m3|20|
 </t2b>
-Parameter c<sub>i</sub> are estimated from data: it is the current supply or demand given the current price (used as the point where p = 0).
 <rcode
@@ Line 25: / Line 24: @@
 >
-out <- formula.Op_en5478(dependencies.Op_en5478)
+out <- formula.Op_en5674(dependencies.Op_en5674)
-cat("Computed changes in energy production and consumption in a new situation.\n")
+cat("Computed training exposures.\n")
 print(out)
@@ Line 32: / Line 31: @@
 == Rationale ==
-For detailed rationale, see [[Energy balance]].
-<math>\Sigma f_i(p) = \Sigma (a_i p^2 + b_i p + c_i) = 0</math>
-<math>p = \frac{-\Sigma b_i \pm \sqrt{(\Sigma b_i)^2 - 4 \Sigma a_i \Sigma c_i}} {2 \Sigma a_i}</math>
-Cost curves are parameterised in the way that the current price p = 0 and therefore the current supply or demand is c<sub>i</sub>.
-When a demand or supply changes, a new price p must be calculated based on f<sub>i</sub>, and then the supplies and demands of other actors can be calculated, resulting in a new balance.
-{{comment|# |There is an alternative way to describe the functions:
-* Zero price is considered as 0 €/product unit. The true market price must be known. This function's parameters are more difficult to estimate.|--[[User:Jouni|Jouni]] 06:16, 28 January 2012 (EET)}}
-{{comment|# |There is also alternative possibilities for functional forms (p is the price of the product):
-* Exponential: P0 + P1 * exp(P2*p - P3)
-|--[[User:Jouni|Jouni]] 17:04, 28 January 2012 (EET)}}
-=== Dependencies ===
 === Formula ===
@@ Line 62: / Line 43: @@
 >
-formula.Op_en5478 <- function(x) {
+formula.Op_en5674 <- function(x) {
 	data <- tidy(op_baseGetData("opasnet_base", x$curves[1]), direction = "wide")
 	curves <- strsplit(as.character(data$Parameters), split = ",")
@@ Line 88: / Line 69: @@
 }
-dependencies.Op_en5478 <- list(
+dependencies.Op_en5674 <- list(
 	curves = c("Op_en5478", "Cost curves for energy"),
 	bau = c("Op_en5469", "Energy balance in Kuopio"),

Training exposure: Difference between revisions

Revision as of 14:11, 12 May 2012

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Question

Answer

Rationale

Formula

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