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HomeKey Stats Bristol
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=== Key stats about 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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|}
 
|}
  
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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>
  
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oikadncok
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== Housing stock stats ==
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<r output="display" iframe="width:400px;height:400px">
−
<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")  
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</R><r output="display" iframe="width:400px;height:400px">
+
</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 13:31, 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
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