Generalised Additive Model for Gas Boiler Breakdown Demand Prediction


At British Gas, we operate a service and repair business with more than 6,000 qualified engineers ready to serve customers who are urgently in need across the country. Predicting demand accurately ahead of time allows us to optimally schedule workforce. Additional workforce can be scheduled in case demand is forecasted to increase substantially. We have developed a prototype demand forecasting procedure which uses a mixture of machine learning techniques. The key component uses Generalised Additive Model (GAM) to estimate the number of incoming work requests. It takes into account the non-linear effects of multiple predictor variables. The models were trained at patches level in order to capture local behaviour. Planning operators can then use the model output to fine-tune workforce assignment at the local level to meet changing demand.

eRum 2018

Slides can be downloaded here.