Energy forecasting

Energy supply companies are becoming aware of the need to be customer-focused to build better relations, loyalty and increase sales. Distribution network operators are also realising the need to become more customer-centric in order to be pre-emptive in network design rather than reactive. Understanding household level demand and how it is changing (especially with increased low carbon tech uptake) opens the channels for improved customer service.

Potential smart storage solutions (batteries) which offer a promising way to mitigate peak demand, producing a more efficient grid, ideally require forecasts down at the household level. However, individual household energy usage is highly erratic with sharp peaks occurring over short timescales and at different times of day on different days of the week. Traditional forecasting methods, typically applied at high voltage over thousands or millions of households and commercial premises where the profiles are much smoother, provide a very smooth forecast. However, this is not sufficient or realistic for an individual household and is of limited use to energy storage solutions.

A simple averaging method will mollify peaks that occur at slightly differing times, resulting in a poor forecast. But using just a previous week (LW) will result in a highly uncertain prediction.

We have developed our own forecasting methodology (AA) that allows us to average over several days still maintain peaks, even if they occur at different times of day. Thus providing a realistic forecast while also producing a more accurate forecast with less uncertainty.

The chart below shows an actual electricity usage over the course of a day (Actual), also plotted is a flat forecast (which achieves a higher accuracy under standard measures than an average forecast), a forecast that is just to take last week's actual usage (LW), and finally the forecast based on our adjusted averaging method (AA) that is able to mimic the peaks but still maintain accuracy.