Longitudinal data used to forecast the UK’s energy use a decade in advance
A forecasting tool which can predict an individual’s, or an entire city’s energy needs up to ten years in advance has been developed to help planners meet environmental targets.
Using the British Household Panel Survey data, Moulay Larbi Chalal, a PhD student at Nottingham Trent University, analysed data from more than 6,000 households collected over a 17 year period to monitor how people’s requirements for gas and electricity evolve over the course of a lifetime.
Moulay Larbi Chalal said, “The UK household sector consumes more than a quarter of the nation’s gas and electricity and is responsible for around 20 percent of the county’s total carbon dioxide emissions.So developing suitable ways to reduce CO2 emissions is important if we’re to meet strict environmental targets and ensure a more sustainable development of our urban areas.”
The forecasting tool can show that the average single, non-elderly person has almost a 20 percent chance of finding a partner and moving in with that partner within five years. Of those newfound couples, 53 percent will go on to have a child during the same period.
Probable future energy needs can then be calculated based on this data. For instance, around 26 percent of those couples without children will use more than 4,000 Kwhs of electricity per year. While the same amount of energy will be used by as many as 35 percent of couples with children.
Similar calculations can be made for a variety of other demographics, such as single households, lone parents with dependent or non-dependent children, those aged over 65, and more.
Professor Benachir Medjdoub, a professor of digital architectural design at Nottingham Trent University, who supervised the project, said, “This research has introduced a powerful new tool which could lead to the development of innovative energy planning systems and CO2 reduction models for urban areas.
“It shows how we can predict the variation in residential energy usage patterns during different transitions of a person’s life. This could help predict and monitor the levels of energy consumed and the related carbon footprint for whole cities, allowing for a smarter management of the gas and electricity distribution network.”