HomeKey Stats Bristol
Contents
Demographic stats
| 2011 census | Mid 2013 estimated | |
| Population | 428,100 | 437,500 |
| Households | 182,747 | 186,760 |
Fuel poverty
For the Low Income High Costs definition of fuel poverty the latest figures (2013) estimate that there are 25,379 households in Bristol in fuel poverty. This is equivalent to 13.2% of households which is in the top 10% in England. For the old 10% definition the most recent data available is from 2012 and estimates that 28,661 households are in fuel poverty, this corresponds to 15.2%.
Energy stats
Bristol has an annual electricity consumption of 1862 GWh (data from 2013) and a daily peak power demand of around 307 MW during winter months. The daily peak demand is calculated by applying the peak to annual consumption ratio of the UK to Bristol.
Bristol's annual gas consumption is 2738 GWh (data from 2013). There is insufficient data available to calculate the peak gas consumption. Because of the inherent storage capacity in the gas network there are fewer issues surrounding the peak.
Local Generation
| Type | Electrical Capacity (MWe) | Heat Capacity (MWt) |
|---|---|---|
| Renewable Electricity | 75.6 | - |
| Renewable Heat | - | 3.6 |
| CHP | 3.4 | 4.9 |
| Total | 79.0 | 8.5 |
Consumption by sector
The total energy bills for Bristol broken down by sector are:
| Annual Consumption (GWh) | Meters | Average Annual Consumption (kWh) | Average Unit Price (£) | Average Annual Bill (£) | Total Spend (£000,000's) | ||
|---|---|---|---|---|---|---|---|
| Domestic | Standard Electricity | 593 | 171,945 | 3,450 | 0.15 | 518 | 89 |
| Economy 7 Electricity | 116 | 22,138 | 5,246 | 0.17 | 910 | 20 | |
| Gas | 2,003 | 167,876 | 11,934 | 0.05 | 586 | 98 | |
| Non-Domestic | Electricity | 1,152 | 17,590 | 65,509 | 0.10 | 6,616 | 116 |
| Gas | 735 | 1,966 | 373,871 | 0.03 | 10,917 | 21 |
| Annual Consumption (GWh) | Meters | Average Annual Consumption (kWh) | Average Unit Price (£) | Average Annual Bill (£) | Total Spend (£000,000's) | ||
|---|---|---|---|---|---|---|---|
| Domestic | Standard Electricity | 938 | 263,219 | ||||
| Economy 7 Electricity | |||||||
| Gas | 2,751 | 229,997 | |||||
| Non-Domestic | Electricity | ||||||
| Gas | 1,000 | 2,470 |
Recent trends
The plots below show the change in electricity and gas consumption for domestic and no-domestic sectors over the last five years. In general there has been a slow and steady decline in consumption, with the exception of non-domestic electricity which has fluctuated but remained roughly constant.
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")
Customer types
| Profile class | Description | Count |
|---|---|---|
| 1 | Domestic Unrestricted Customer | 170,308 |
| 2 | Domestic Economy 7 Customers | 23,144 |
| 3 | Non-Domestic Unrestricted Customers | 14,367 |
| 4 | Non-Domestic Economy 7 Customers | 2,750 |
| 5 | Non-Domestic Maximum Demand (MD) Customers with a Peak Load Factor (LF) of less than 20% | 367 |
| 6 | Non-Domestic Maximum Demand Customers with a Peak Load Factor between 20% and 30% | 630 |
| 7 | Non-Domestic Maximum Demand Customers with a Peak Load Factor between 30% and 40% | 354 |
| 8 | Non-Domestic Maximum Demand Customers with a Peak Load Factor over 40% | 457 |
| 0 | Non-Domestic Half Hourly Metered | 11,126 |
Local electricity infrastructure
There are two Grid Supply Points (GSPs) that directly supply Bristol. These are located at Seabank and Iron Acton and shown on the map below along with the outline of the Bristol urban area.
The table below gives the number of substations at different voltage levels in both the Bristol City limits and the Bristol Urban Area.
| Bristol City | Bristol Urban Area | |
|---|---|---|
| 132kV substations | 5 | 7 |
| 33kV Substations | 24 | 35 |
| 11kV Substations | 1,858 | 2,616 |
| Meters | 211,673 |
Local gas infrastructure
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")
