Saving utilities millions in imbalance costs
via dedicated AI weather models built for each generation asset
Trusted by
via dedicated AI weather models built for each generation asset
Trusted by
We don't sell you another forecast — we train site-specific AI weather models tuned to each asset's microclimate.
We ingest each asset's terrain, elevation, land cover, and local microclimate — the detail generic forecasts average away. Captures what ECMWF and DWD miss, fine-tuned asset by asset.
One model per asset, calibrated to your site's own SCADA observations — not a regional average shared across providers. Up to 60% better day-ahead generation accuracy.
Day ahead and intra-day, site-specific forecasts stream into your existing trading and SCADA systems. From start to saved millions in 30 days, no hardware, no workflow change.
Gas, wind, solar, and hydro all rely on weather to forecast output, schedule operations, and avoid costly mismatches between expected and actual generation.
Electricity prices, intraday positions, and imbalance exposure are driven by short-term weather variability, making forecast uncertainty a direct financial risk for trading desks.
Grid capacity, congestion, losses, and operational safety depend on ambient weather conditions, especially as networks operate closer to their physical and regulatory limits.
Charging and dispatch strategies for storage assets depend on weather-driven supply and demand dynamics, where timing errors directly impact revenue and system stability.
How energy companies across the world are using hylosense to turn forecast accuracy into measurable savings.
Wind Power
hylosense's ongoing pilot with Fortum involves forecasting wind at parks in the Nordics — and is already achieving 20% to 40% improvement in day-ahead wind forecast accuracy. The project grew out of Fortum's Spark Innovation Challenge, where hylosense was awarded best startup.
Gas Power
Across gas power plant locations in the UK and Germany, hylosense delivered up to 64% improvement in temperature forecast accuracy — directly reducing imbalance exposure and improving trading positions. Models were live and calibrated within 30 days of deployment.
Site Risk Assessment
Evaluating the right locations to build new solar power plants is a task with long-term consequences. By employing the power of climate projection data under different GHG emission scenarios, terrain elevation as well as land cover data, we're able to assess future threats to multiple locations with high accuracy.
Estimate the imbalance cost a site-tuned hylosense model could remove. Start from a real example asset, then adjust the numbers to match your own.
Illustrative estimate only, based on user-supplied and market-average assumptions — not a quote, forecast of results, or commitment. Actual savings vary by asset, market, and conditions.
We work with generation, trading, and grid operations teams at major utilities worldwide. Book a session to see how hylosense integrates with your existing systems and workflows.
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