For many energy suppliers, the day begins with numbers: sales, revenue, forecasts, variances. The data is available, the reports have been drawn up – and yet meetings often start with the same question: what do these figures actually tell us?

The issue is rarely data avilability. It lies in identifying relevant anomalies quickly, interpreting them correctly and drawing conclusions from them.

While forecast discrepancies are part of everyday life, their causes are not

Discrepancies between forecasts and actual results are common in the energy sector. Markets change, conditions fluctuate, and assumptions do not necessarily hold true. Problems arise however, when it is unclear where these discrepancies stem from and how significant they actually are.

If you look only at aggregated values, you will see that there are discrepancies – but you won’t understand why. The underlying causes remain hidden because individual market locations, time periods or segments are masked by the total value.

Structure rather than individual assessment

This is precisely where a structured analytical approach is needed.
A standardised forecast review ensures that forecast deviations are not only identified but also systematically assessed:

• deviations are clearly displayed
• the quality of the forecast can be assessed statistically
• causes can be analysed in detail – down to individual market locations

This allows for a consistant, transparent and replicable picture to emerge from a multitude of individual figures.

Clarity enables action 

Identifying forecast discrepancies at an early stage saves time and money, as there is no need to search for the causes after the fact. When discrepancies are analysed systematically and assessed using a shared database, the focus automatically shifts: away from the question of whether the figures are correct – and towards the question of which measures make sense.

This provides energy suppliers in particular with the confidence to act in their day-to-day operations: the quality of forecasts can be deliberately improved, coordination times are reduced, and decisions are based on more reliable information

If you’d like to explore in more depth how forecast variances can be systematically analysed and evaluated, you’ll find further practical insights by speaking to our colleagues.

You’re also welcome to take a look at the live demo of the Forecast Check.