Weather pattern classification and prediction

How do weather patterns influence wind and solar production, and spot market dynamics?

WEATHER PATTERN CLASSIFICATION

As the penetration of renewable energy increases across the National Electricity Market, meteorological conditions will continue to have a larger impact on electricity market dynamics, such renewable energy supply and residual demand, causing large fluctuations in regional electricity prices.

As the market becomes more sophisticated, proponents must gain a more detailed understanding of the link between meteorological conditions and key climate variables – such as wind speed and solar irradiance – and the implications for regional spot market dynamics, such as demand, renewable energy supply, and wholesale price dynamics.

Our weather pattern classification analysis provides decision makers with a deeper understanding of the the recurrence of dominant weather patterns over spatial domains, and the relationship between weather patterns and specific electricity market outcomes, helping to inform a range of risk and market analysis.

Figure 1: Dominant synoptic patterns in southern Australia

Figure 2: Frequency of weather patterns (1-365 days) vs. Relative wind speed of weather patterns

To learn more, download our recent White Paper on the recurrence of synoptic scale wind regimes across south eastern Australia, or access our latest renewable energy Research Insights.

Applications and benefits

Analysis of the link between individual weather patterns and wholesale market dynamics can support a range of applications, including:

  • Analysis of production characteristics at a specific location under different weather patterns, and alignment with regional spot market outcomes (such as demand, aggregate production and spot price fluctuations), informing the business case for renewable energy or storage project development;
  • Forecast frequency of weather patterns within seasonal and long-term models;
  • Identification of high value or high risk weather patterns, and the frequency of their recurrence;
  • Identification of production shortages or price fluctuations under specific weather patterns and opportunities to manage risk (trading and hedging or energy storage, co-location, etc.);
  • Analysis of system-wide anomalies and extreme events, such as extremes in wind energy production or spot price dynamics linked to specific weather regimes, wind ramps, etc;
  • Alignment of short term weather forecasts with pre-classified circulation patterns based on the automatic classification of future weather days to the closest cluster type;
  • Help to inform risk management strategies and prepare for future events based on the expected site and regional outcomes.

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