Using data analytics for business decisions in the UK energy sector: a case study integrating gas demand with weather data

Abstract

Data analytics has been highlighted in many sectors as providing a pivotal role in business operations and strategy. The energy and utility sectors in the UK are becoming increasingly aware of the value of their operational data and investigation into how statistical analysis can improve operational efficiency and performance, leading to savings and reduced consumer bills. Vast resources of open data are accessible and analysing integrated datasets enhances the opportunities for insight. This paper demonstrates data analytics via a case study. Gas demand is highly volatile with many influencing factors. Regional weather measurements used to predict demand are inadequate and unrepresentative of local areas; daily discrepancies of over £140,000 were identified between forecasts of gas demand. Establishing demand profiles for smaller areas enables better predictions and hence reduced purchasing and storage costs. Such enhanced knowledge of infrastructure capacity and utilisation is timely ahead of increased emphasis on sustainability. [Received: December 2, 2016; Accepted: July 19, 2017]