Per-asset AI weather models for energy

Name: Per-asset AI weather models for energy: one site-calibrated model per generation asset, trained on that site's terrain, elevation, land cover, local microclimate and its own SCADA observations, delivering day-ahead and intra-day asset-level forecasts by API. Latent byproduct assets include the paired site-weather-to-SCADA-generation training corpora and the realized forecast-vs-actual error records per asset.
Creator: hylosense

HYLOSENSEManage supplier listing

Per-asset AI weather models for energy: one site-calibrated model per generation asset, trained on that site's terrain, elevation, land cover, local microclimate and its own SCADA observations, delivering day-ahead and intra-day asset-level forecasts by API. Latent byproduct assets include the paired site-weather-to-SCADA-generation training corpora and the realized forecast-vs-actual error records per asset.

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Coverage

Universe, instruments and categories.

Industry: Application SoftwareElectric Utilities
Instruments: commoditiesderivativesequities

Dataset card

Type: not stated
Format: API time-series feed (day-ahead and intra-day horizons) integrated into trading and SCADA systems; underlying assets are model weights plus gridded/site-level meteorological and climate-projection inputs (GHG scenarios, terrain elevation, land cover)
Volume: est. per-asset panels in the millions of rows: each asset yields hourly day-ahead plus intra-day forecast vectors across many meteorological variables (say 8,760 day-ahead cycles x 10-30 variables + finer intra-day refresh), so 20-100 assets in pilots implies 5-50M forecast rows to date; the training corpora are larger still since they include multi-year on-site observation and SCADA series per site.
Users: est. 3-10 contracted counterparties (pilots and deployments named: Fortum wind parks in the Nordics, gas plants across UK and Germany) — a 7-person Seed-stage company at ~€470K raised, so client count is in single digits to low tens, not a platform user base
History: 3 years
Update frequency: not stated
Growth: Active — an ongoing named pilot (Fortum, grown out of Fortum's Spark Innovation Challenge where hylosense won best startup), deployments described as live and calibrated within 30 days, and an active briefing funnel; Seed-funded and 7 staff means growth is pilot-led rather than scaled
Launched: not stated
Delivery: not stated
Entity mapping: not stated
Sample: not stated
Point-in-time: not stated
Licence: not stated

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