HomeKey Stats Bristol
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− | == | + | == Demographic stats == |
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{| class='wikitable' | {| class='wikitable' | ||
| align="center" style="background:#f0f0f0;"| | | align="center" style="background:#f0f0f0;"| | ||
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+ | == Energy stats == | ||
Bristol has an annual consumption of 1862 GWh (data from 2013) and a daily peak of around 307 MW during winter months. | Bristol has an annual consumption of 1862 GWh (data from 2013) and a daily peak of around 307 MW during winter months. | ||
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</r> | </r> | ||
− | + | == Housing stock stats == | |
− | + | <r output="display" iframe="width:400px;height:400px"> | |
− | < | + | |
pdf(rpdf, width=5, height=5) | pdf(rpdf, width=5, height=5) | ||
library(ggplot2) | library(ggplot2) | ||
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theme(axis.title.y = element_text(vjust = 1)) + | theme(axis.title.y = element_text(vjust = 1)) + | ||
xlab("Build year") + ylab("Number of homes") | xlab("Build year") + ylab("Number of homes") | ||
− | </ | + | </r><r output="display" iframe="width:400px;height:400px"> |
pdf(rpdf, width=5, height=5) | pdf(rpdf, width=5, height=5) | ||
library(ggplot2) | library(ggplot2) |
Revision as of 12:31, 20 August 2015
Demographic stats
2011 census | Mid 2013 estimated | |
Population | 428,100 | 437,500 |
Households | 182,747 | 186,760 |
Energy stats
Bristol has an annual consumption of 1862 GWh (data from 2013) and a daily peak of around 307 MW during winter months.
Type | Electrical Capacity | Heat Capacity |
---|---|---|
Seabank | 1,145 | - |
Renewable Electricity | 53.9 | - |
Renewable Heat | - | 19.9 |
CHP | 9.2 | 11.9 |
Total | 1,208 | 32 |
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) elec.consumption <- data.frame(Year = c(2009,2010,2011,2012,2013),
DomesticConsumption = c(718754856.9, 718735925.8, 718586755.1, 715382012, 709352993.9), DomesticMPANs = c(189391, 191066, 192449, 193322, 194083), NonDomesticConsumption = c(1176217057, 1202799481, 1185903139, 1135575674, 1152307023), NonDomesticMPANs = c(17500, 17444, 17350, 17552, 17590))elec.consumption$Domestic <- elec.consumption$DomesticConsumption/elec.consumption$DomesticMPANs elec.consumption$NonDomestic <- elec.consumption$NonDomesticConsumption/elec.consumption$NonDomesticMPANs elec.consumption <- elec.consumption[,c(1,6,7)] ggplot(data=elec.consumption, aes(x=Year, y=Domestic)) +
geom_bar(stat="identity", fill="#4D4D4D") + theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + theme(plot.title = element_text(vjust = 2)) + scale_y_continuous(expand = c(0,0)) + xlab("Year") + ylab("Average consumption (kWh)")+ ggtitle("Average domestic electricity consumption\nper customer in Bristol")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) elec.consumption <- data.frame(Year = c(2009,2010,2011,2012,2013),
DomesticConsumption = c(718754856.9, 718735925.8, 718586755.1, 715382012, 709352993.9), DomesticMPANs = c(189391, 191066, 192449, 193322, 194083), NonDomesticConsumption = c(1176217057, 1202799481, 1185903139, 1135575674, 1152307023), NonDomesticMPANs = c(17500, 17444, 17350, 17552, 17590))elec.consumption$Domestic <- elec.consumption$DomesticConsumption/elec.consumption$DomesticMPANs elec.consumption$NonDomestic <- elec.consumption$NonDomesticConsumption/elec.consumption$NonDomesticMPANs elec.consumption <- elec.consumption[,c(1,6,7)] ggplot(data=elec.consumption, aes(x=Year, y=NonDomestic)) +
geom_bar(stat="identity", fill="#4D4D4D") + theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + theme(plot.title = element_text(vjust = 2)) + scale_y_continuous(expand = c(0,0)) + xlab("Year") + ylab("Average consumption (kWh)")+ ggtitle("Average non-domestic electricity consumption\nper customer in Bristol")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) gas.consumption <- data.frame(Year = c(2009,2010,2011,2012,2013),
DomesticConsumption = c(2188753981, 2158524075, 2014008753, 2045449904, 2003430860), DomesticMPANs = c(162126, 163573, 164780, 168961, 167876), NonDomesticConsumption = c(881539141, 832499075, 783226304, 783928929, 735031042), NonDomesticMPANs = c(2013, 1975, 1915, 1974, 1966))gas.consumption$Domestic <- gas.consumption$DomesticConsumption/gas.consumption$DomesticMPANs gas.consumption$NonDomestic <- gas.consumption$NonDomesticConsumption/gas.consumption$NonDomesticMPANs gas.consumption <- gas.consumption[,c(1,6,7)] ggplot(data=gas.consumption, aes(x=Year, y=Domestic)) +
geom_bar(stat="identity", fill="#4D4D4D") + theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + theme(plot.title = element_text(vjust = 2)) + scale_y_continuous(expand = c(0,0)) + xlab("Year") + ylab("Average consumption (kWh)")+ ggtitle("Average domestic gas consumption\nper customer in Bristol")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) gas.consumption <- data.frame(Year = c(2009,2010,2011,2012,2013),
DomesticConsumption = c(2188753981, 2158524075, 2014008753, 2045449904, 2003430860), DomesticMPANs = c(162126, 163573, 164780, 168961, 167876), NonDomesticConsumption = c(881539141, 832499075, 783226304, 783928929, 735031042), NonDomesticMPANs = c(2013, 1975, 1915, 1974, 1966))gas.consumption$Domestic <- gas.consumption$DomesticConsumption/gas.consumption$DomesticMPANs gas.consumption$NonDomestic <- gas.consumption$NonDomesticConsumption/gas.consumption$NonDomesticMPANs gas.consumption <- gas.consumption[,c(1,6,7)] ggplot(data=gas.consumption, aes(x=Year, y=NonDomestic)) +
geom_bar(stat="identity", fill="#4D4D4D") + theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + theme(plot.title = element_text(vjust = 2)) + scale_y_continuous(expand = c(0,0)) + xlab("Year") + ylab("Average consumption (kWh)")+ ggtitle("Average non-domestic gas consumption\nper customer in Bristol")
Housing stock stats
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) library(scales) library(grid) build_year <- data.frame (
build.year= c("pre-1870", "1871-1919", "1920-1945", "1946-1954", "1955-1979", "post-1980") , number=c(9847,37379,60307,25823,29126,18297))order <- c("pre-1870", "1871-1919", "1920-1945", "1946-1954", "1955-1979", "post-1980") ggplot(data=build_year, aes(x=build.year, y=number)) + geom_bar(stat="identity", fill="#4D4D4D", colour="black") +
theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + scale_y_continuous(expand = c(0,0), labels=comma) + scale_x_discrete(limits = order) + ggtitle('Build year of Bristol homes') + theme(plot.title = element_text(vjust=2)) + theme(axis.title.y = element_text(vjust = 1)) + xlab("Build year") + ylab("Number of homes")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) library(scales) library(grid) built_form <- data.frame (
built.form= c("Detached", "Semi-detached", "Bungalow", "Terraced", "Flat") , number=c(8387,48887,2567,75569,45369))ggplot(data=built_form, aes(x=built.form, y=number)) + geom_bar(stat="identity", fill="#4D4D4D", colour="black") +
theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + scale_y_continuous(expand = c(0,0), labels=comma) + ggtitle('Built form of Bristol homes') + theme(plot.title = element_text(vjust=2)) + theme(axis.title.y = element_text(vjust = 1)) + xlab("Built form") + ylab("Number of homes")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) library(scales) library(grid) bedrooms <- data.frame (
bedrooms= c("1 bedroom", "2 bedroom", "3 bedroom", "4 bedroom", "5 or more") , number=c(23952,40834,92492,11340,12161))ggplot(data=bedrooms, aes(x=bedrooms, y=number)) + geom_bar(stat="identity", fill="#4D4D4D", colour="black") +
theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + scale_y_continuous(expand = c(0,0), labels=comma) + ggtitle('Number of bedrooms in Bristol homes') + theme(plot.title = element_text(vjust=2)) + theme(axis.title.y = element_text(vjust = 1)) + xlab("Number of bedrooms") + ylab("Number of homes")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) library(scales) library(grid) tenure <- data.frame (
tenure= c("Owner occupied", "Privately rented", "Council/housing association") , number=c(97622,29360,53797))ggplot(data=tenure, aes(x=tenure, y=number)) + geom_bar(stat="identity", fill="#4D4D4D", colour="black") +
theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + scale_y_continuous(expand = c(0,0), labels=comma) + ggtitle('Tenure of Bristol homes') + theme(plot.title = element_text(vjust=2)) + theme(axis.title.y = element_text(vjust = 1)) + xlab("Tenure") + ylab("Number of homes")
The total energy bills for Bristol broken down by sector are:
Estimated bill, £ millions (excl. VAT) | |
Domestic electricity | 110.78 |
Domestic gas | 98.18 |
Non-domestic electricity | 118.45 |
Non-domestic gas | 22.23 |