UK Power Distribution
Case study
UK Power Distribution
#Weather #Power
Weather disrupts power supplies – knowing where and when this will happen allows you to plan.
🡒 The power distribution network in the UK is a regulated monopoly – good performance isn't ensured by competition, but by regulatory oversight. In short, the power distributor will be fined for poor performance, which includes power interruptions to its clients. Given much of the high-voltage infrastructure is above ground, it is prone to damage from weather. Predicting where/when faults will occur based on forecast weather data allows planning to minimise interruptions and associated regulator penalties.
What we’ve done
We have built predictive models that predict the numbers and locations of faults on the UK's SSE distribution network. Weather forecasts for up to 5 days may be used, with predicted regional fault numbers returned along with measures of uncertainty.
Technical aspects
Unlike many predictive modelling problems, the uncertainty around the predictions is of particular interest here – planning is made against best-/worst-case scenarios. The predictive modelling methods currently gathered under the machine-learning banner, tend not to provide inference. We've evaluated a large number of model classes, favouring those more statistical in nature so we can give probabilistic predictions.
The currently favoured models are variants of Generalised Additive Models, combined with boosting, allowing for flexible modelling of the error distributions. Separate models are fitted for each 24-hour weather forecast period i.e. one day into the future, two days into the future, etc. Models have been fitted and validated against a decade of fault and forecast data, and are naturally validated every day through their industrial use.
#Weather #Power
Weather disrupts power supplies – knowing where and when this will happen allows
you to plan.
🡒 The power distribution network in the UK is a regulated monopoly – good performance isn't ensured by competition, but by regulatory oversight. In short, the power distributor will be fined for poor performance, which includes power interruptions to its clients. Given much of the high-voltage infrastructure is above ground, it is prone to damage from weather. Predicting where/when faults will occur based on forecast weather data allows planning to minimise interruptions and associated regulator penalties.
What we’ve done
We have built predictive models that predict the numbers and locations of faults on the UK's SSE distribution network. Weather forecasts for up to 5 days may be used, with predicted regional fault numbers returned along with measures of uncertainty.
Technical aspects
Unlike many predictive modelling problems, the uncertainty around the predictions is of particular interest here – planning is made against best-/worst-case scenarios. The predictive modelling methods currently gathered under the machine-learning banner, tend not to provide inference. We've evaluated a large number of model classes, favouring those more statistical in nature so we can give probabilistic predictions.
The currently favoured models are variants of Generalised Additive Models, combined with boosting, allowing for flexible modelling of the error distributions. Separate models are fitted for each 24-hour weather forecast period i.e. one day into the future, two days into the future, etc. Models have been fitted and validated against a decade of fault and forecast data, and are naturally validated every day through their industrial use.