Poor weather forecasts are quietly draining Australia’s solar profits — and an $80bn market is lining up to fix it

Forecast misses are costing real money and changing the way solar assets are valued

Solstice AI’s recent analysis finds that weather forecast errors are inflicting a meaningful drag on Australian solar earnings. The report concludes that inaccuracies in short- and medium-term forecasts are shaving more than $100 million a year from generator revenues across the country. Globally, Solstice AI projects roughly an $80 billion addressable market by 2030 for better forecasting and related services that shave forecast error, reduce imbalance costs and help owners run assets more profitably.

This isn’t an academic problem. For owners of large solar farms, traders and funds that buy power under merchant exposure, forecast errors change cash flow, increase uncertainty and — over time — show up in asset valuations and share prices. The gap between expected and actual generation forces a string of real costs: penalties in real-time markets, lost merchant sales, and extra curtailment. For investors focused on yield and stability, those are not trivial line items.

How inaccurate weather forecasts translate into cash losses

At the simplest level, solar operators forecast how much power their plants will produce. Markets and contracts use those numbers to schedule supply and match demand. When actual output differs from forecasted output, the owner must either buy or sell in the spot market to close the gap. That activity triggers imbalance charges and can force sales at unfavourable prices.

Forecast misses also affect contract performance. Fixed-price power purchase agreements (PPAs) and hedges are priced against forecasted production. If a plant underperforms against its forecast, the operator may face liquidated damages, lower delivered volume to counterparties, or fail to hit minimum delivery thresholds that trigger contract penalties.

Operationally, bad forecasts can increase curtailment and suboptimal dispatch decisions. Grid operators rely on forecasts to manage network constraints. If a forecast overstates generation during a constraint, an operator may be forced to curtail output later, or to buy replacement energy at peak prices. Finally, poorer forecasts raise the cost of capital: lenders and investors assign higher risk premiums to assets with unpredictable cash flows.

Who feels the pain — and how it changes valuations and volatility

Not all players are hit the same. Merchant assets and developer portfolios that retain market exposure bear the largest immediate cost. For yield-oriented vehicles — such as renewables-focused REITs, yieldcos and infrastructure funds — the problem is simpler: less predictable distributions and higher downside in low-generation stretches.

Trading desks at utilities and independent power traders also take a hit. Forecast error widens P&L volatility for intraday and balancing trades. That increases margin calls and hedging costs, and can force traders to hold larger buffers or buy expensive insurance-style hedges.

On the public markets, the effect is clearer: stocks of companies with material merchant exposure or exposed balance sheets see more earnings volatility and sometimes wider share-price swings. Australian utilities with large renewable portfolios — including integrated players such as AGL (AGL) and Origin Energy (ORG) — have a stronger incentive to reduce imbalance risk. For investors, the key consequence is valuation compression for assets where forecasting risk is high and margins depend on tight dispatch timing.

An $80bn opportunity by 2030 — where forecasting firms and AI fit in

Solstice AI’s market figure reflects the combined value of reduced imbalance costs, higher realised output, fewer penalties and the new services that will be sold into the energy stack: forecasting subscriptions, trading signalling, asset-level optimisation and integration with energy management systems. The drivers are simple — faster renewable build, increasing merchant exposure as more PPAs move to indexed or merchant-linked pricing, and the growing value of short-term precision for storage and hybrid assets.

Competition in this space is broad. Traditional meteorological services and utility forecasting teams are being challenged by nimble AI-focused startups and specialist energy-forecast firms. The winners will be those who can show consistent error reduction at the plant level, demonstrate savings in real-world P&L terms, and integrate forecasts into trading and dispatch workflows so the theoretical accuracy turns into cash.

Signals investors should watch next: KPIs, partnerships and catalysts

If you invest in renewables or utilities, watch for three kinds of signals. First, asset-level KPIs: reductions in mean absolute error, fewer imbalance events, declines in curtailment and higher realised yield versus P50/P90 baselines. Second, corporate disclosures: vendor partnerships, procurement of new forecasting services, and line items in trading P&L that show lower balancing costs.

Third, regulatory and contract shifts. Changes to how imbalance prices are settled or to PPA indexation can shift how much forecasting accuracy is worth. On the public markets, keep an eye on companies that announce model pilots or rollouts — integrated utilities such as AGL (AGL) or Origin Energy (ORG) can meaningfully change their merchant exposure. Sector ETFs that track clean energy and solar themes — for example ICLN and TAN — may also react as the market prices the benefits of improved forecasting.

Report caveats and next steps for verification

Solstice AI’s figures are a strong signal, but they are not the final word. Any headline number depends on underlying assumptions: which assets were modelled, how imbalance prices were estimated, and whether the report uses measured data or simulated scenarios. There is also a commercial incentive for a forecasting vendor to highlight the size of the problem.

Investors should look for independent audits, plant-level before-and-after studies from early customers, and corroborating data from market operators on imbalance volumes and costs. In practice, the prize for reducing error is real. The question for investors is which firms can turn better models into measurable, repeatable cashflow improvements — and which customers will actually pay for that value.

Photo: Kindel Media / Pexels

Sources

• prnewswire.com