Weather Years

Certain components of the model rely on a designated “weather year” - a representative historical period selected as a seed to inform projections of future weather conditions and associated outage profiles. This is used to seed our demand, solar/wind load factors, hydro, generation outages, and transmission outages.

For the 2025 Q3 model, the selected weather year is 2023. This year was chosen following a structured evaluation of the last 10 years. We evaluated years based on solar and wind traces, temperature, number of outages, and geographical distribution of outages. 2023 was towards the middle on most metrics, if not slightly below the middle. It was also after the change to 5 minute settlement periods, meaning we don't have to interpolate to get 5 minute solar/wind/demand data.

For full transparency, we also considered and discounted the following options for seeding the above:

• Cycling between 10 weather years: this is a method that AEMO use in some of their modelling, but we discounted it as it then becomes unclear whether a year in the forecast has high revenues due to fundamentals or due to using a weather year with a high number of outages.
• Randomness based on historical distributions: this sounds like a clever way of modelling things, but we believe strongly against adding any deliberate randomness to the model. It only makes the model more of a black box, and it's hard/impossible to capture this randomness perfectly (especially correlation between demand/solar/wind). Also, unless we repeat the randomness every year, this approach still has the downside of making it unclear whether a particular year has high revenues due to fundamentals or due to randomness.