R/heat.R
In emunozh/heat: Performa a heat balance calculation of residential buildings
# Created by Esteban
# Wed Feb 25, 2015
heat <- function(x, ...) UseMethod("heat")
#TODO: Proof source
#' @title heat
#'
#' @description
#' Perform a monthly heat balance and annual energy demand of buildings
#' @source
#' DIN V 18599
#'
#' @param building (optional, default = FALSE) use a user define building
#' data file.
#' @param climate (optional, default = Hamburg) use a specific climate file.
#' @param fp (optional, default = Gas) primary energy factor and CO2-eq
#' emissions factor.
#' @param general_name (optional, default = "No Name") if this value is not
#' specifically define the function will load the values from the building data
#' file.
#' @param general_ploto (optional, default = FALSE)
#' @param general_plotn (optional, default="No Name")
#' @param building_uwb (optional, default = FALSE)
#' @param building_windows (optional, default = FALSE)
#' @param building_uvalw (optional, default = FALSE)
#' @param building_uvalr (optional, default = FALSE)
#' @param building_uvalwindow (optional, default = FALSE)
#' @param building_dim (optional, default = c(FALSE,FALSE))
#' @param building_h (optional, default = FALSE)
#' @param user_aircrate (optional, default = FALSE)
#' @param user_ti (optional, default = FALSE)
#' @param user_qi (optional, default = FALSE)
#' @param building_orientation (optional, default = FALSE)
#' @param building_storagecapacity (optional, default = FALSE)
#' @param building_roofslope (optional, default = FALSE)
#' @param output_type (optional, default = "Year")
#' @examples
#' # Use the default values to compute the heat demand of a building
#' climate_month_names <- c("January","February","March","April",
#' "May","June","July","August",
#' "September","October","November","December")
#'
#' model <- heat(building_orientation = 0,
#' output_type = "Month")
#' Qhm <- model$Qhm
#' barplot(Qhm,
#' names.arg=climate_month_names,
#' main = "Monthly Heat Demand",
#' ylab = "head Demand [kWh]")
#'
#' # This example show the heat demand variation for different
#' # U values combinations
#' Buildings.Number <- 9
#'
#' U.Values <- matrix(c(
#' 1.3, 1.0, 3.0, # 01
#' 1.2, 0.9, 2.7, # 02
#' 1.1, 0.8, 2.7, # 03
#' 1.0, 0.7, 2.7, # 04
#' 0.9, 0.6, 2.4, # 05
#' 0.8, 0.5, 2.1, # 06
#' 0.6, 0.4, 1.9, # 07
#' 0.5, 0.3, 1.6, # 08
#' 0.4, 0.2, 1.6), # 09
#' 3,Buildings.Number)
#'
#' Heat.Demand <- rep(0,Buildings.Number)
#' for (i in 1:Buildings.Number){
#' UvalW <- U.Values[1,i]
#' UvalR <- U.Values[2,i]
#' UvalWindow <- U.Values[3,i]
#' temp.2 <- heat(building_uvalw = UvalW,
#' building_uvalr = UvalR,
#' building_uvalwindow = UvalWindow)
#' Heat.Demand[i] <- temp.2$Qhs
#' }
#'
#' # bar plot
#' barplot(Heat.Demand,
#' names.arg = seq(1910,2010,12),
#' main = "Heat demand for a set of buildings",
#' ylab = "Heat demand [kWh/m²a]",
#' xlab = "Building age")
#'
#' # This example show the heat demand variation for different
#' # user influenced parameters
#' Buildings.Number <- 5
#'
#' Param.Values <- matrix(c(
#' 7, 22, 0.7, # 01
#' 6, 21, 0.6, # 02
#' 5, 20, 0.5, # 03
#' 4, 19, 0.4, # 04
#' 3, 18, 0.3), # 05
#' 3,Buildings.Number)
#'
#' UvalW <- 0.4
#' UvalR <- 0.2
#' UvalWindow <- 1.6
#'
#' Heat.Demand <- rep(0,Buildings.Number)
#' for (i in 1:Buildings.Number){
#' AirCRate <- Param.Values[1,i]
#' Ti <- Param.Values[2,i]
#' qi <- Param.Values[3,i]
#' temp.2 <- heat(user_aircrate = AirCRate,
#' user_ti = Ti,
#' user_qi = qi,
#' building_uvalw = UvalW,
#' building_uvalr = UvalR,
#' building_uvalwindow = UvalWindow)
#' Heat.Demand[i] <- temp.2$Qhs
#' }
#'
#' # bar plot
#' barplot(Heat.Demand,
#' main = "Heat demand for a set of buildings",
#' ylab = "Heat demand [kWh/m²a]",
#' xlab = "Building age")
#'
#' # This example show the heat demand variation for different
#' # Building orientations, it used the ggplot2 library
#' library(ggplot2)
#'
#' iter <- seq(0,360,1)
#' Heat.Demand = rep(0,length(iter))
#' Heat.Gains.Solar = rep(0,length(iter))
#' Irradiation.Sum = rep(0,length(iter))
#'
#' building_dim <- c(12,6)
#' for (i in 1:length(iter)){
#' BO <- iter[i]
#' temp.2 <- heat(building_orientation = BO,
#' building_dim = building_dim)
#' Heat.Demand[i] <- temp.2$Qhs
#' Heat.Gains.Solar[i] <- temp.2$Ss
#' Irradiation.Sum[i] <- temp.2$Ti
#' }
#'
#' # Polar plot + line plot
#' result <- data.frame(heat.demand = Heat.Demand,
#' orientation = iter)
#' doh <- ggplot(result, aes(orientation, heat.demand))
#' # Line plot
#' doh + geom_line(colour = "red", size = 1) +
#' coord_polar(direction = -1, start = -pi/2) +
#' labs(title = "Heat demand for all possible building orientations") +
#' scale_x_continuous(breaks=seq(0, 360, 15))
#'
#' @author M. Esteban Munoz H.
#TODO: change case in examples
#TODO: import examples
#TODO: proof data input
heat.default <- function(
building = FALSE,
climate = "Hamburg",
fp = "Gas",
general_name = "No Name",
general_ploto = FALSE,
general_plotn = "No Name",
building_uwb = FALSE,
building_windows = FALSE,
building_uvalw = FALSE,
building_uvalr = FALSE,
building_uvalwindow = FALSE,
building_dim = c(FALSE,FALSE),
building_h = FALSE,
user_aircrate = FALSE,
user_ti = FALSE,
user_qi = FALSE,
building_orientation = FALSE,
building_storagecapacity = FALSE,
building_roofslope = FALSE,
output_type = "Year")
{
model <- heatest(
building = building,
climate = climate,
fp = fp,
general_name = general_name,
general_ploto = general_ploto,
general_plotn = general_plotn,
building_uwb = building_uwb,
building_windows = building_windows,
building_uvalw = building_uvalw,
building_uvalr = building_uvalr,
building_uvalwindow = building_uvalwindow,
building_dim = building_dim,
building_h = building_h,
user_aircrate = user_aircrate,
user_ti = user_ti,
user_qi = user_qi,
building_orientation = building_orientation,
building_storagecapacity = building_storagecapacity,
building_roofslope = building_roofslope,
output_type = output_type)
class(model) <- "heat"
model}
heatest <- function(
building = FALSE,
climate = FALSE,
fp = FALSE,
general_name = "No Name",
general_ploto = FALSE,
general_plotn = "No Name",
building_uwb = FALSE,
building_windows = FALSE,
building_uvalw = FALSE,
building_uvalr = FALSE,
building_uvalwindow = FALSE,
building_dim = c(FALSE,FALSE),
building_h = FALSE,
user_aircrate = FALSE,
user_ti = FALSE,
user_qi = FALSE,
building_orientation = FALSE,
building_storagecapacity = FALSE,
building_roofslope = FALSE,
output_type = "Year")
{
## input data:
##################
# (1) as function of
##################
# Separate module for building data see "Data/GeometryModule.r"
# (A) General
#------------------------------------------------------------
# Name Char Name of the typ + F
# PlotOption Boolean Plot figure?
# PlotName Name Name to save plot
# (B) Building data:
#------------------------------------------------------------------------------
# Uwb Double Correction value for thermal bridges
# Windows Double Percentage of facade with windows
# UvalW Double U-value for walls
# UvalR Double U-value for Roof
# UvalWindow Double U-value for Window
# Dim 1x2 Double Building dimensions L x B
# h Double Height of Building
# AirCRate Double Air change rate [h-1]
# Ti 1x12 Double Ti(Theta i) internal temperature
# qi Double Internal heat emissions.
# Ob Double Building orientation
# RoofSlope Double Slope of building roof
# StorageCapacity Double Storage capacity of Building
if (building){
load(building)
}else{
#TODO: use internal data
#load("./Data/BuildingData")
data(building)
}
# uncomment this line for de-bug
#load("./Data/BuildingDataDBug")
#building_orientation <- 1
## Check input data
if (general_name == "No Name") {general_name <- T.general_name}
if (general_ploto == FALSE) {general_ploto <- T.general_ploto}
if (general_plotn == "No Name") {general_plotn <- T.general_plotn}
if (building_uwb == FALSE) {building_uwb <- T.building_uwb}
if (building_windows == FALSE) {building_windows <- T.building_windows}
if (building_uvalw == FALSE) {building_uvalw <- T.building_uvalw}
if (building_uvalr == FALSE) {building_uvalr <- T.building_uvalr}
if (building_uvalwindow == FALSE) {building_uvalwindow <- T.building_uvalwindow}
if (building_dim[1] == FALSE) {building_dim <- T.building_dim}
if (building_h == FALSE) {building_h <- T.building_h}
if (user_aircrate == FALSE) {user_aircrate <- T.user_aircrate}
if (user_ti == FALSE) {user_ti <- T.user_ti}
if (user_qi == FALSE) {user_qi <- T.user_qi}
if (building_orientation == FALSE){building_orientation <- T.building_orientation}
if (building_storagecapacity == FALSE){building_storagecapacity <- T.building_storagecapacity}
if (building_roofslope == FALSE){building_roofslope <- T.building_roofslope}
##################
# (2) Imported Data <load>
##################
##Climate:
# Separate module for climate calculation see "Data/ClimateModule.r"
data_list <- try(unlist(data(package="heat")))
data_list <- data_list[data_list != ""]
data_list <- data_list[data_list != "heat"]
data_list <- data_list[data_list != "Data sets"]
data_list <- data_list[data_list != "/usr/lib/R/library"]
if (paste("Climate.I (",climate,")", sep="") %in% data_list){
data(list=climate, package="heat")
} else {
load(climate)
}
## Primary and CO2-eq factors
if ("Fp" %in% data_list){
data(list="Fp", package="heat")
} else {
load(fp)
}
factor.fp <- Fp[[fp]][1]
factor.CO2 <- Fp[[fp]][2]
# I 4x12 Double Isolation level orientation x months
# Month 1x12 Double Month number
# Te 1x12 Double outside temperature
# t 1x12 Double days of the month
## output data:
# Qhs --> Specific heat demand
# Hts --> Specific transmission losses
# Get the radiation levels for this building
Climate.I <- getSolarRadiation(building_orientation)
##################
## Heat gains Qg
##################
##internal gains Si
# heated area An
Building.An <- building_dim[1]*building_dim[2]*building_h/3
Heat.gains.Si <- user_qi * Building.An
##monthly solar heat flows Ss
# Ss(M) average monthly solar heat flow [W]
# I(M,j) average solar intensity of radiation [W/m²]
# A(s,j) actual collector surface [m²]
# J orientation (direction and down-grade to vertical)
Heat.gains.Ss <-
Climate.I[,"north"]*(building_dim[1]*building_h*building_windows) +
Climate.I[,"west"] *(building_dim[2]*building_h*building_windows) +
Climate.I[,"south"]*(building_dim[1]*building_h*building_windows) +
Climate.I[,"east"] *(building_dim[2]*building_h*building_windows)
##Heat gains Qg
#0,024 kWh = 1 Wd
#Ss(M) average monthly solar heat flow [W]
#Si(M) heat flow by internal heat sources [W]
#t(M) number of the days in a particular month [d/M]
Heat.gains.Qg <- 0.024 * (Heat.gains.Ss + Heat.gains.Si) * Climate.t
##################
## Heat losses Ql
##################
##ventilation loss Hv !! Only for natural ventilation
# AirCRate = air change rate [h-1]
# V volume of air in heated building (according to EnEV it obtains V = 0.8* Ve)
# pL* CPL heat storage capacity of air = 0.34 [Wh/(m²K)]
Building.Ve <- building_dim[1]*building_dim[2]*building_h
Building.V <- 0.8 * Building.Ve
Constant.plCpl = 0.34
Heat.loss.Hv <- user_aircrate * Building.V * Constant.plCpl;
##thermal bridge addition
# Uwb correction value for thermal bridges [W/m²K]
# A total heat transmitting building envelope [m²]
Building.A.Roof <- 2*(building_dim[2]*((building_dim[1]/2)/cos(building_roofslope*pi/180)))
Building.A.Wall.1 <- building_dim[1]*building_h
Building.A.Wall.2 <- building_dim[2]*building_h
Building.A.Window <- Building.A.Wall.1 * building_windows * 2 + Building.A.Wall.2 * building_windows * 2
# Building.A.Slab = building_dim[1] * building_dim[2]
Building.A <- 2*Building.A.Wall.1 + 2*Building.A.Wall.2 + Building.A.Roof
Heat.loss.Hwb <- building_uwb * Building.A
##transmission losses Ht
# Temperature correction factor Fx
# Ti(Theta i) internal temperature [K]
# Tu(M) Temperature in unheated space [K]
# Te(M) Ambient (=external) temperature [K]
# Fx(M) = (Ti-Tu(M))/(Ti-Te(M)) integrated in Area Calculator module
# Heat losses referring to heat loss surface Hu
# U(i) Heat transfer coefficient [W/(m²K)]
# A(i) Analogical to building part surface [m²]
# Fx(i) Temperature correction factor
# Hu(i) = Fx * U * (integrated in Htu)
Heat.loss.Hu <- 0
# Htfh Not consider in these calculation.
Heat.loss.Htfh <- 0
# Ls Not consider in these calculation. Integrated in Htu
Heat.loss.Ls <- 0
##transmission losses Ht
# U(i) Heat transfer coefficient incountercurrent with ambient air[W/(sqm K)]
# A(i) Analogical to building part surface [sqm]
# Hu Heat losses referring to heat loss surface [W/K]
# Ls thermal guide value for ground bordering surfaces [W/K]
# Hwb thermal bridges addition [W/K]
# Htfh Specific transmission heat loss by building parts with integrated panel heating [W/K]
# Ht = sum (U) * sum (A) + Hu + Ls + Hwb + Htfh
Heat.loss.Htu <- (
(2*Building.A.Wall.1*(1-building_windows) +
2*Building.A.Wall.2*(1-building_windows)
) * building_uvalw) +
(Building.A.Roof * building_uvalr) +
(Building.A.Window * building_uvalwindow)
Heat.loss.Ht <- Heat.loss.Htu + Heat.loss.Hu + Heat.loss.Ls + Heat.loss.Hwb + Heat.loss.Htfh
#specific total heat loss (transmission and ventilation heat losses;HT+HV) [W/K] H
Heat.loss.H <- Heat.loss.Ht + Heat.loss.Hv
##Heat losses Ql
# 0.024 kWh = 1 Wd
# H(M) specific total heat loss (transmission and ventilation heat losses;HT+HV) [W/K]
# Ti-Te(M) difference between internal and ambient temperature [K]
# t(M) number of the days in a particular month [d/M]
Heat.loss.Ql <- 0.024 * Heat.loss.H * (user_ti - Climate.Te) * Climate.t
##################
## Monthly heat demand Qh
##################
# Ql(M) sum of monthly heat loss due to transmission and ventilation [kWh/M]
# Qg(M) sum of monthly heat gains [kWh/M]
# n(M) monthly utilization factor for the gains [-]
Heat.demand.Thermal = building_storagecapacity * Building.Ve / Heat.loss.H
Heat.demand.a = 1 + Heat.demand.Thermal/16
Heat.demand.y <- Heat.gains.Qg/Heat.loss.Ql
Heat.demand.Qhm <- rep(0,12)
for (m in 1:12) {
if (Heat.demand.y[m] == 1 ){
Heat.demand.n <- (Heat.demand.a / (Heat.demand.a +1))
} else {
Heat.demand.n <- (1-Heat.demand.y[m]^Heat.demand.a)/(1-Heat.demand.y[m]^(Heat.demand.a+1))
}
Heat.demand.Qhm[m] <- Heat.loss.Ql[m] - Heat.demand.n * Heat.gains.Qg[m]
}
# subtraction of negative heat demand
Heat.demand.Qhm[Heat.demand.Qhm < 0] <- 0
Heat.demand.Qh <- sum(Heat.demand.Qhm)
## Specific heat demand [kWh/sqm a] Qhs
# and specific transmission losses Hts [W/sqm K]
Heat.demand.Qhs = Heat.demand.Qh / Building.An
Heat.demand.Hts = Heat.loss.Ht / Building.An
## Sum of solar heat gains
Heat.gains.Ss.sum <- sum(Heat.gains.Ss)
## Total Irradiation
Total.Irradiation <- sum(Climate.I)
## Primary energy demand
Heat.demand.Qpm <- Heat.demand.Qhm * factor.fp #month
Heat.demand.Qp <- Heat.demand.Qh * factor.fp #year
## CO2 emissions
CO2m <- Heat.demand.Qpm * factor.CO2 * 3.6e-9 #month
CO2 <- Heat.demand.Qp * factor.CO2 * 3.6e-9 #year
## Result
#TODO: format output as list
if (output_type == "Year"){
result = data.frame(
Ti = Total.Irradiation,
Ss = Heat.gains.Ss.sum,
Qhs = Heat.demand.Qhs,
Hts = Heat.demand.Hts,
Qp = Heat.demand.Qp,
CO2 = CO2
)
} else if (output_type == "Month"){
result = data.frame(
Qhm = Heat.demand.Qhm,
Qpm = Heat.demand.Qpm,
CO2 = CO2m
)
}
return(result)
}
#' @title getSolarRadiation
#'
#' @description
#' Computes the solar radiation of the building based on its orientation
#'
#' @param building_orientation orientation of the building in degrees
#' @return Climate.I radiation levels
#' @author M. Estebna Munoz H.
#TODO: make example
getSolarRadiation <- function(building_orientation){
## Solar radiation matrix
# it is easier to change the radiation values that to rotate the building :)
if (
building_orientation == 0 ||
building_orientation == 180 ||
building_orientation == 360
){
new.Orientation <- c("north", "west", "south","east")
temp.rectangular <- TRUE
} else if (
building_orientation == 90 ||
building_orientation == 270
){
new.Orientation <- c("west", "south","east","north")
temp.rectangular <- TRUE
} else if (
building_orientation > 0 &&
building_orientation < 90
){
temp.b <- (building_orientation-0)/90
new.Orientation <- data.frame(A=c("north","west","south","east"),
B=c("west","south","east","north" ))
temp.rectangular <- FALSE
} else if (
building_orientation > 90 &&
building_orientation < 180
){
temp.b <- (building_orientation-90)/90
new.Orientation <- data.frame(A=c("west","south","east","north"),
B=c("south","east","north","west"))
temp.rectangular <- FALSE
} else if (
building_orientation > 180 &&
building_orientation < 270
){
temp.b <- (building_orientation-180)/90
new.Orientation <- data.frame(A=c("south","east","north","west"),
B=c("east","north","west","south"))
temp.rectangular <- FALSE
} else if (
building_orientation > 270 &&
building_orientation < 360
){
temp.b <- (building_orientation-270)/90
new.Orientation <- data.frame(A=c("east","north","west","south"),
B=c("north","west","south","east"))
temp.rectangular <- FALSE
}
# (1)N
# ^ 7
# | /
# | /
# 90° /
# | /
# | /
# | /
# | /
# | / <
# (II) ( x1---|-----x ) (I)
# > | | /4
# | | / |
# | | / |
# | | / |
# | |/ |
# (1)W <--180°------|----+-----|--------------0°---> (1)E
# | | |
# 2 | |
# (III) x----|----3x (IV)
# |
# | 1) building_orientation = 0 || 180 || 360
# | N-W-S-E
# | 2) building_orientation = 90 || 270
# | W-S-E-N
# | 3) building_orientation = 1 - 89 (I )
# 270° NW-SW-SE-NE
# | 4) building_orientation = 91 - 179 (II )
# v SW-SE-NE-NW
# (1)S 5) building_orientation = 181 - 269 (III)
# SE-NE-NW-SW
# 6) building_orientation = 271 - 359 (IV )
# NE-NW-SW-SE
if (temp.rectangular == TRUE){
colnames(Climate.I) <- new.Orientation
} else {
temp.val <- matrix(rep(0,48),nrow=12,ncol=4)
for (i in 1:4) {
temp.Pos <- grep(paste("^",new.Orientation$A[i],"$",sep=""), colnames(Climate.I))
temp.A <- Climate.I[temp.Pos]
temp.Pos <- grep(paste("^",new.Orientation$B[i],"$",sep=""), colnames(Climate.I))
temp.B <- Climate.I[temp.Pos]
temp.val[,i] = unlist(temp.A*(1-temp.b) + temp.B*temp.b)
}
Climate.I <- as.data.frame(temp.val)
colnames(Climate.I) <- c("north", "west", "south","east")
}
return(Climate.I)
}
#TODO: make print function
#TODO: make summary function
#TODO: make plot function
emunozh/heat documentation built on May 16, 2019, 5:11 a.m.
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# Created by Esteban
# Wed Feb 25, 2015
heat <- function(x, ...) UseMethod("heat")
#TODO: Proof source
#' @title heat
#'
#' @description
#' Perform a monthly heat balance and annual energy demand of buildings
#' @source
#' DIN V 18599
#'
#' @param building (optional, default = FALSE) use a user define building
#' data file.
#' @param climate (optional, default = Hamburg) use a specific climate file.
#' @param fp (optional, default = Gas) primary energy factor and CO2-eq
#' emissions factor.
#' @param general_name (optional, default = "No Name") if this value is not
#' specifically define the function will load the values from the building data
#' file.
#' @param general_ploto (optional, default = FALSE)
#' @param general_plotn (optional, default="No Name")
#' @param building_uwb (optional, default = FALSE)
#' @param building_windows (optional, default = FALSE)
#' @param building_uvalw (optional, default = FALSE)
#' @param building_uvalr (optional, default = FALSE)
#' @param building_uvalwindow (optional, default = FALSE)
#' @param building_dim (optional, default = c(FALSE,FALSE))
#' @param building_h (optional, default = FALSE)
#' @param user_aircrate (optional, default = FALSE)
#' @param user_ti (optional, default = FALSE)
#' @param user_qi (optional, default = FALSE)
#' @param building_orientation (optional, default = FALSE)
#' @param building_storagecapacity (optional, default = FALSE)
#' @param building_roofslope (optional, default = FALSE)
#' @param output_type (optional, default = "Year")
#' @examples
#' # Use the default values to compute the heat demand of a building
#' climate_month_names <- c("January","February","March","April",
#' "May","June","July","August",
#' "September","October","November","December")
#'
#' model <- heat(building_orientation = 0,
#' output_type = "Month")
#' Qhm <- model$Qhm
#' barplot(Qhm,
#' names.arg=climate_month_names,
#' main = "Monthly Heat Demand",
#' ylab = "head Demand [kWh]")
#'
#' # This example show the heat demand variation for different
#' # U values combinations
#' Buildings.Number <- 9
#'
#' U.Values <- matrix(c(
#' 1.3, 1.0, 3.0, # 01
#' 1.2, 0.9, 2.7, # 02
#' 1.1, 0.8, 2.7, # 03
#' 1.0, 0.7, 2.7, # 04
#' 0.9, 0.6, 2.4, # 05
#' 0.8, 0.5, 2.1, # 06
#' 0.6, 0.4, 1.9, # 07
#' 0.5, 0.3, 1.6, # 08
#' 0.4, 0.2, 1.6), # 09
#' 3,Buildings.Number)
#'
#' Heat.Demand <- rep(0,Buildings.Number)
#' for (i in 1:Buildings.Number){
#' UvalW <- U.Values[1,i]
#' UvalR <- U.Values[2,i]
#' UvalWindow <- U.Values[3,i]
#' temp.2 <- heat(building_uvalw = UvalW,
#' building_uvalr = UvalR,
#' building_uvalwindow = UvalWindow)
#' Heat.Demand[i] <- temp.2$Qhs
#' }
#'
#' # bar plot
#' barplot(Heat.Demand,
#' names.arg = seq(1910,2010,12),
#' main = "Heat demand for a set of buildings",
#' ylab = "Heat demand [kWh/m²a]",
#' xlab = "Building age")
#'
#' # This example show the heat demand variation for different
#' # user influenced parameters
#' Buildings.Number <- 5
#'
#' Param.Values <- matrix(c(
#' 7, 22, 0.7, # 01
#' 6, 21, 0.6, # 02
#' 5, 20, 0.5, # 03
#' 4, 19, 0.4, # 04
#' 3, 18, 0.3), # 05
#' 3,Buildings.Number)
#'
#' UvalW <- 0.4
#' UvalR <- 0.2
#' UvalWindow <- 1.6
#'
#' Heat.Demand <- rep(0,Buildings.Number)
#' for (i in 1:Buildings.Number){
#' AirCRate <- Param.Values[1,i]
#' Ti <- Param.Values[2,i]
#' qi <- Param.Values[3,i]
#' temp.2 <- heat(user_aircrate = AirCRate,
#' user_ti = Ti,
#' user_qi = qi,
#' building_uvalw = UvalW,
#' building_uvalr = UvalR,
#' building_uvalwindow = UvalWindow)
#' Heat.Demand[i] <- temp.2$Qhs
#' }
#'
#' # bar plot
#' barplot(Heat.Demand,
#' main = "Heat demand for a set of buildings",
#' ylab = "Heat demand [kWh/m²a]",
#' xlab = "Building age")
#'
#' # This example show the heat demand variation for different
#' # Building orientations, it used the ggplot2 library
#' library(ggplot2)
#'
#' iter <- seq(0,360,1)
#' Heat.Demand = rep(0,length(iter))
#' Heat.Gains.Solar = rep(0,length(iter))
#' Irradiation.Sum = rep(0,length(iter))
#'
#' building_dim <- c(12,6)
#' for (i in 1:length(iter)){
#' BO <- iter[i]
#' temp.2 <- heat(building_orientation = BO,
#' building_dim = building_dim)
#' Heat.Demand[i] <- temp.2$Qhs
#' Heat.Gains.Solar[i] <- temp.2$Ss
#' Irradiation.Sum[i] <- temp.2$Ti
#' }
#'
#' # Polar plot + line plot
#' result <- data.frame(heat.demand = Heat.Demand,
#' orientation = iter)
#' doh <- ggplot(result, aes(orientation, heat.demand))
#' # Line plot
#' doh + geom_line(colour = "red", size = 1) +
#' coord_polar(direction = -1, start = -pi/2) +
#' labs(title = "Heat demand for all possible building orientations") +
#' scale_x_continuous(breaks=seq(0, 360, 15))
#'
#' @author M. Esteban Munoz H.
#TODO: change case in examples
#TODO: import examples
#TODO: proof data input
heat.default <- function(
building = FALSE,
climate = "Hamburg",
fp = "Gas",
general_name = "No Name",
general_ploto = FALSE,
general_plotn = "No Name",
building_uwb = FALSE,
building_windows = FALSE,
building_uvalw = FALSE,
building_uvalr = FALSE,
building_uvalwindow = FALSE,
building_dim = c(FALSE,FALSE),
building_h = FALSE,
user_aircrate = FALSE,
user_ti = FALSE,
user_qi = FALSE,
building_orientation = FALSE,
building_storagecapacity = FALSE,
building_roofslope = FALSE,
output_type = "Year")
{
model <- heatest(
building = building,
climate = climate,
fp = fp,
general_name = general_name,
general_ploto = general_ploto,
general_plotn = general_plotn,
building_uwb = building_uwb,
building_windows = building_windows,
building_uvalw = building_uvalw,
building_uvalr = building_uvalr,
building_uvalwindow = building_uvalwindow,
building_dim = building_dim,
building_h = building_h,
user_aircrate = user_aircrate,
user_ti = user_ti,
user_qi = user_qi,
building_orientation = building_orientation,
building_storagecapacity = building_storagecapacity,
building_roofslope = building_roofslope,
output_type = output_type)
class(model) <- "heat"
model}
heatest <- function(
building = FALSE,
climate = FALSE,
fp = FALSE,
general_name = "No Name",
general_ploto = FALSE,
general_plotn = "No Name",
building_uwb = FALSE,
building_windows = FALSE,
building_uvalw = FALSE,
building_uvalr = FALSE,
building_uvalwindow = FALSE,
building_dim = c(FALSE,FALSE),
building_h = FALSE,
user_aircrate = FALSE,
user_ti = FALSE,
user_qi = FALSE,
building_orientation = FALSE,
building_storagecapacity = FALSE,
building_roofslope = FALSE,
output_type = "Year")
{
## input data:
##################
# (1) as function of
##################
# Separate module for building data see "Data/GeometryModule.r"
# (A) General
#------------------------------------------------------------
# Name Char Name of the typ + F
# PlotOption Boolean Plot figure?
# PlotName Name Name to save plot
# (B) Building data:
#------------------------------------------------------------------------------
# Uwb Double Correction value for thermal bridges
# Windows Double Percentage of facade with windows
# UvalW Double U-value for walls
# UvalR Double U-value for Roof
# UvalWindow Double U-value for Window
# Dim 1x2 Double Building dimensions L x B
# h Double Height of Building
# AirCRate Double Air change rate [h-1]
# Ti 1x12 Double Ti(Theta i) internal temperature
# qi Double Internal heat emissions.
# Ob Double Building orientation
# RoofSlope Double Slope of building roof
# StorageCapacity Double Storage capacity of Building
if (building){
load(building)
}else{
#TODO: use internal data
#load("./Data/BuildingData")
data(building)
}
# uncomment this line for de-bug
#load("./Data/BuildingDataDBug")
#building_orientation <- 1
## Check input data
if (general_name == "No Name") {general_name <- T.general_name}
if (general_ploto == FALSE) {general_ploto <- T.general_ploto}
if (general_plotn == "No Name") {general_plotn <- T.general_plotn}
if (building_uwb == FALSE) {building_uwb <- T.building_uwb}
if (building_windows == FALSE) {building_windows <- T.building_windows}
if (building_uvalw == FALSE) {building_uvalw <- T.building_uvalw}
if (building_uvalr == FALSE) {building_uvalr <- T.building_uvalr}
if (building_uvalwindow == FALSE) {building_uvalwindow <- T.building_uvalwindow}
if (building_dim[1] == FALSE) {building_dim <- T.building_dim}
if (building_h == FALSE) {building_h <- T.building_h}
if (user_aircrate == FALSE) {user_aircrate <- T.user_aircrate}
if (user_ti == FALSE) {user_ti <- T.user_ti}
if (user_qi == FALSE) {user_qi <- T.user_qi}
if (building_orientation == FALSE){building_orientation <- T.building_orientation}
if (building_storagecapacity == FALSE){building_storagecapacity <- T.building_storagecapacity}
if (building_roofslope == FALSE){building_roofslope <- T.building_roofslope}
##################
# (2) Imported Data <load>
##################
##Climate:
# Separate module for climate calculation see "Data/ClimateModule.r"
data_list <- try(unlist(data(package="heat")))
data_list <- data_list[data_list != ""]
data_list <- data_list[data_list != "heat"]
data_list <- data_list[data_list != "Data sets"]
data_list <- data_list[data_list != "/usr/lib/R/library"]
if (paste("Climate.I (",climate,")", sep="") %in% data_list){
data(list=climate, package="heat")
} else {
load(climate)
}
## Primary and CO2-eq factors
if ("Fp" %in% data_list){
data(list="Fp", package="heat")
} else {
load(fp)
}
factor.fp <- Fp[[fp]][1]
factor.CO2 <- Fp[[fp]][2]
# I 4x12 Double Isolation level orientation x months
# Month 1x12 Double Month number
# Te 1x12 Double outside temperature
# t 1x12 Double days of the month
## output data:
# Qhs --> Specific heat demand
# Hts --> Specific transmission losses
# Get the radiation levels for this building
Climate.I <- getSolarRadiation(building_orientation)
##################
## Heat gains Qg
##################
##internal gains Si
# heated area An
Building.An <- building_dim[1]*building_dim[2]*building_h/3
Heat.gains.Si <- user_qi * Building.An
##monthly solar heat flows Ss
# Ss(M) average monthly solar heat flow [W]
# I(M,j) average solar intensity of radiation [W/m²]
# A(s,j) actual collector surface [m²]
# J orientation (direction and down-grade to vertical)
Heat.gains.Ss <-
Climate.I[,"north"]*(building_dim[1]*building_h*building_windows) +
Climate.I[,"west"] *(building_dim[2]*building_h*building_windows) +
Climate.I[,"south"]*(building_dim[1]*building_h*building_windows) +
Climate.I[,"east"] *(building_dim[2]*building_h*building_windows)
##Heat gains Qg
#0,024 kWh = 1 Wd
#Ss(M) average monthly solar heat flow [W]
#Si(M) heat flow by internal heat sources [W]
#t(M) number of the days in a particular month [d/M]
Heat.gains.Qg <- 0.024 * (Heat.gains.Ss + Heat.gains.Si) * Climate.t
##################
## Heat losses Ql
##################
##ventilation loss Hv !! Only for natural ventilation
# AirCRate = air change rate [h-1]
# V volume of air in heated building (according to EnEV it obtains V = 0.8* Ve)
# pL* CPL heat storage capacity of air = 0.34 [Wh/(m²K)]
Building.Ve <- building_dim[1]*building_dim[2]*building_h
Building.V <- 0.8 * Building.Ve
Constant.plCpl = 0.34
Heat.loss.Hv <- user_aircrate * Building.V * Constant.plCpl;
##thermal bridge addition
# Uwb correction value for thermal bridges [W/m²K]
# A total heat transmitting building envelope [m²]
Building.A.Roof <- 2*(building_dim[2]*((building_dim[1]/2)/cos(building_roofslope*pi/180)))
Building.A.Wall.1 <- building_dim[1]*building_h
Building.A.Wall.2 <- building_dim[2]*building_h
Building.A.Window <- Building.A.Wall.1 * building_windows * 2 + Building.A.Wall.2 * building_windows * 2
# Building.A.Slab = building_dim[1] * building_dim[2]
Building.A <- 2*Building.A.Wall.1 + 2*Building.A.Wall.2 + Building.A.Roof
Heat.loss.Hwb <- building_uwb * Building.A
##transmission losses Ht
# Temperature correction factor Fx
# Ti(Theta i) internal temperature [K]
# Tu(M) Temperature in unheated space [K]
# Te(M) Ambient (=external) temperature [K]
# Fx(M) = (Ti-Tu(M))/(Ti-Te(M)) integrated in Area Calculator module
# Heat losses referring to heat loss surface Hu
# U(i) Heat transfer coefficient [W/(m²K)]
# A(i) Analogical to building part surface [m²]
# Fx(i) Temperature correction factor
# Hu(i) = Fx * U * (integrated in Htu)
Heat.loss.Hu <- 0
# Htfh Not consider in these calculation.
Heat.loss.Htfh <- 0
# Ls Not consider in these calculation. Integrated in Htu
Heat.loss.Ls <- 0
##transmission losses Ht
# U(i) Heat transfer coefficient incountercurrent with ambient air[W/(sqm K)]
# A(i) Analogical to building part surface [sqm]
# Hu Heat losses referring to heat loss surface [W/K]
# Ls thermal guide value for ground bordering surfaces [W/K]
# Hwb thermal bridges addition [W/K]
# Htfh Specific transmission heat loss by building parts with integrated panel heating [W/K]
# Ht = sum (U) * sum (A) + Hu + Ls + Hwb + Htfh
Heat.loss.Htu <- (
(2*Building.A.Wall.1*(1-building_windows) +
2*Building.A.Wall.2*(1-building_windows)
) * building_uvalw) +
(Building.A.Roof * building_uvalr) +
(Building.A.Window * building_uvalwindow)
Heat.loss.Ht <- Heat.loss.Htu + Heat.loss.Hu + Heat.loss.Ls + Heat.loss.Hwb + Heat.loss.Htfh
#specific total heat loss (transmission and ventilation heat losses;HT+HV) [W/K] H
Heat.loss.H <- Heat.loss.Ht + Heat.loss.Hv
##Heat losses Ql
# 0.024 kWh = 1 Wd
# H(M) specific total heat loss (transmission and ventilation heat losses;HT+HV) [W/K]
# Ti-Te(M) difference between internal and ambient temperature [K]
# t(M) number of the days in a particular month [d/M]
Heat.loss.Ql <- 0.024 * Heat.loss.H * (user_ti - Climate.Te) * Climate.t
##################
## Monthly heat demand Qh
##################
# Ql(M) sum of monthly heat loss due to transmission and ventilation [kWh/M]
# Qg(M) sum of monthly heat gains [kWh/M]
# n(M) monthly utilization factor for the gains [-]
Heat.demand.Thermal = building_storagecapacity * Building.Ve / Heat.loss.H
Heat.demand.a = 1 + Heat.demand.Thermal/16
Heat.demand.y <- Heat.gains.Qg/Heat.loss.Ql
Heat.demand.Qhm <- rep(0,12)
for (m in 1:12) {
if (Heat.demand.y[m] == 1 ){
Heat.demand.n <- (Heat.demand.a / (Heat.demand.a +1))
} else {
Heat.demand.n <- (1-Heat.demand.y[m]^Heat.demand.a)/(1-Heat.demand.y[m]^(Heat.demand.a+1))
}
Heat.demand.Qhm[m] <- Heat.loss.Ql[m] - Heat.demand.n * Heat.gains.Qg[m]
}
# subtraction of negative heat demand
Heat.demand.Qhm[Heat.demand.Qhm < 0] <- 0
Heat.demand.Qh <- sum(Heat.demand.Qhm)
## Specific heat demand [kWh/sqm a] Qhs
# and specific transmission losses Hts [W/sqm K]
Heat.demand.Qhs = Heat.demand.Qh / Building.An
Heat.demand.Hts = Heat.loss.Ht / Building.An
## Sum of solar heat gains
Heat.gains.Ss.sum <- sum(Heat.gains.Ss)
## Total Irradiation
Total.Irradiation <- sum(Climate.I)
## Primary energy demand
Heat.demand.Qpm <- Heat.demand.Qhm * factor.fp #month
Heat.demand.Qp <- Heat.demand.Qh * factor.fp #year
## CO2 emissions
CO2m <- Heat.demand.Qpm * factor.CO2 * 3.6e-9 #month
CO2 <- Heat.demand.Qp * factor.CO2 * 3.6e-9 #year
## Result
#TODO: format output as list
if (output_type == "Year"){
result = data.frame(
Ti = Total.Irradiation,
Ss = Heat.gains.Ss.sum,
Qhs = Heat.demand.Qhs,
Hts = Heat.demand.Hts,
Qp = Heat.demand.Qp,
CO2 = CO2
)
} else if (output_type == "Month"){
result = data.frame(
Qhm = Heat.demand.Qhm,
Qpm = Heat.demand.Qpm,
CO2 = CO2m
)
}
return(result)
}
#' @title getSolarRadiation
#'
#' @description
#' Computes the solar radiation of the building based on its orientation
#'
#' @param building_orientation orientation of the building in degrees
#' @return Climate.I radiation levels
#' @author M. Estebna Munoz H.
#TODO: make example
getSolarRadiation <- function(building_orientation){
## Solar radiation matrix
# it is easier to change the radiation values that to rotate the building :)
if (
building_orientation == 0 ||
building_orientation == 180 ||
building_orientation == 360
){
new.Orientation <- c("north", "west", "south","east")
temp.rectangular <- TRUE
} else if (
building_orientation == 90 ||
building_orientation == 270
){
new.Orientation <- c("west", "south","east","north")
temp.rectangular <- TRUE
} else if (
building_orientation > 0 &&
building_orientation < 90
){
temp.b <- (building_orientation-0)/90
new.Orientation <- data.frame(A=c("north","west","south","east"),
B=c("west","south","east","north" ))
temp.rectangular <- FALSE
} else if (
building_orientation > 90 &&
building_orientation < 180
){
temp.b <- (building_orientation-90)/90
new.Orientation <- data.frame(A=c("west","south","east","north"),
B=c("south","east","north","west"))
temp.rectangular <- FALSE
} else if (
building_orientation > 180 &&
building_orientation < 270
){
temp.b <- (building_orientation-180)/90
new.Orientation <- data.frame(A=c("south","east","north","west"),
B=c("east","north","west","south"))
temp.rectangular <- FALSE
} else if (
building_orientation > 270 &&
building_orientation < 360
){
temp.b <- (building_orientation-270)/90
new.Orientation <- data.frame(A=c("east","north","west","south"),
B=c("north","west","south","east"))
temp.rectangular <- FALSE
}
# (1)N
# ^ 7
# | /
# | /
# 90° /
# | /
# | /
# | /
# | /
# | / <
# (II) ( x1---|-----x ) (I)
# > | | /4
# | | / |
# | | / |
# | | / |
# | |/ |
# (1)W <--180°------|----+-----|--------------0°---> (1)E
# | | |
# 2 | |
# (III) x----|----3x (IV)
# |
# | 1) building_orientation = 0 || 180 || 360
# | N-W-S-E
# | 2) building_orientation = 90 || 270
# | W-S-E-N
# | 3) building_orientation = 1 - 89 (I )
# 270° NW-SW-SE-NE
# | 4) building_orientation = 91 - 179 (II )
# v SW-SE-NE-NW
# (1)S 5) building_orientation = 181 - 269 (III)
# SE-NE-NW-SW
# 6) building_orientation = 271 - 359 (IV )
# NE-NW-SW-SE
if (temp.rectangular == TRUE){
colnames(Climate.I) <- new.Orientation
} else {
temp.val <- matrix(rep(0,48),nrow=12,ncol=4)
for (i in 1:4) {
temp.Pos <- grep(paste("^",new.Orientation$A[i],"$",sep=""), colnames(Climate.I))
temp.A <- Climate.I[temp.Pos]
temp.Pos <- grep(paste("^",new.Orientation$B[i],"$",sep=""), colnames(Climate.I))
temp.B <- Climate.I[temp.Pos]
temp.val[,i] = unlist(temp.A*(1-temp.b) + temp.B*temp.b)
}
Climate.I <- as.data.frame(temp.val)
colnames(Climate.I) <- c("north", "west", "south","east")
}
return(Climate.I)
}
#TODO: make print function
#TODO: make summary function
#TODO: make plot function
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