✅ Finalized
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Bristol is the largest city in south-west England, with about 470,000 residents, and was named European Green Capital in 2015. In April 2015, Bristol City Council and the University of Bristol launched Bristol is Open as a joint venture and city-scale testbed. After three years, the council became the sole owner, and the company is now dormant.
Bristol joined BigClouT as a new pilot city, alongside Grenoble and Tsukuba. The city focused on a problem at the crossroads of climate policy and household hardship: energy that people pay for but never use. Appliances left on standby, heating running in empty homes and energy use during expensive peak hours all waste money that many Bristol households cannot afford to lose. The pilot tested whether predicting what a home will use before it happens could help residents act to reduce this waste.
Bristol City Council and Bristol is Open, together with Lancaster University in the BigClouT consortium, created a tool to predict household energy use. Instead of only showing what a home has already used, the tool forecasts future consumption and points out where energy is wasted, such as standby or phantom load from unused devices and demand during peak periods, when electricity is most expensive and the grid is under stress. The forecasting used the BigClouT data lake, the project’s shared city data platform. The goal was not only to cut carbon emissions: Bristol focused on energy poverty, reasoning that households struggling to pay their bills benefit more from removing hidden waste than from being told simply to use less.
Key goal or objective
To reduce household energy use in Bristol and help households avoid energy poverty by predicting domestic consumption and revealing waste that residents cannot see, especially standby load and demand during expensive peak periods.
Household energy waste is hard to address because it is not visible. Meter readings come after the fact and only show total use. They tell residents whether they used more than expected, but not which devices, times or habits caused the difference. Standby load makes things worse by drawing power all the time from unused appliances. The amount is too small to notice day to day, but it adds up over a year.
Energy poverty makes the issue more urgent. Households already struggling to pay their bills have little capacity to absorb waste and cannot easily cut back further. For them, finding spending that brings no benefit is more valuable than general advice to use less. Bristol took this seriously: the city’s smart city programme targeted fuel poverty directly, and many council neighbourhoods are among the most deprived in England.
Prediction changes what can be offered. A forecast lets a household see its energy use before it happens, turning a bill from a report into a chance to make decisions. Residents can shift usage out of peak times, cut standby draw or plan for expected costs. It also helps the city model overall demand, which matters for grid planning as electrification grows.
BigClouT provided the platform. The project combined big data, cloud computing and IoT to give citizens more influence in smart cities. The European Commission funded the project under Horizon 2020, and Japan’s NICT supported the Japanese partners. The project ran from 1 July 2016 to 30 June 2019 in Grenoble, Bristol, Fujisawa and Tsukuba, all using a shared data lake and analytics system.
The pilot tackles four connected problems.
First, households waste energy they cannot see. Standby and phantom loads run continuously and invisibly, at a scale that standard meter readings do not reveal.
Second, retrospective billing arrives too late to change behaviour. A bill describes a month that has already happened as a single aggregate figure, with no link back to the decisions that produced it.
Third, peak demand increases household costs and strains the grid. Shifting consumption requires knowing in advance when the expensive periods will occur.
Fourth, energy poverty makes generic energy-saving advice unhelpful. Households already cutting back have little further to give. They need to identify spending that delivers no benefit, rather than being encouraged to reduce essential consumption.
Completed
The pilot created a predictive household energy tool that runs on the BigClouT platform. It showed that forecasting can replace retrospective reporting, turning energy feedback from a look at the past into a way to make decisions about the near future.
The project made energy poverty a main goal of a smart city technology pilot, instead of treating it as a side benefit.
The tool ran on the shared BigClouT architecture alongside the Grenoble, Fujisawa and Tsukuba pilots, showing the same platform serving a European household energy use case and a Japanese infrastructure-monitoring one.
