Skip to main content

Envisioning is an emerging technology research institute and advisory.

LinkedInInstagramGitHub

2011 — 2026

research
  • Reports
  • Newsletter
  • Methodology
  • Origins
  • Vocab
services
  • Research Sessions
  • Signals Workspace
  • Bespoke Projects
  • Use Cases
  • Signal Scanfree
  • Readinessfree
impact
  • ANBIMAFuture of Brazilian Capital Markets
  • IEEECharting the Energy Transition
  • Horizon 2045Future of Human and Planetary Security
  • WKOTechnology Scanning for Austria
audiences
  • Innovation
  • Strategy
  • Consultants
  • Foresight
  • Associations
  • Governments
resources
  • Pricing
  • Partners
  • How We Work
  • Data Visualization
  • Multi-Model Method
  • FAQ
  • Security & Privacy
about
  • Manifesto
  • Community
  • Events
  • Support
  • Contact
  • Login
ResearchServicesPricingPartnersAbout
ResearchServicesPricingPartnersAbout
  1. Home
  2. Research
  3. Atmos
  4. Renewable Energy Forecasting Engines

Renewable Energy Forecasting Engines

Machine learning models that predict solar and wind power output for grid planning
Back to AtmosView interactive version

Solar and wind forecasting engines fuse satellite imagery, numerical weather prediction ensembles, sky cameras, lidar, and turbine SCADA data to deliver minute-to-day-ahead power estimates. Machine-learning models correct biases, capture ramp events, and quantify uncertainty, while edge devices at solar plants analyze cloud motion in real time to update dispatch schedules every few seconds. APIs stream probabilistic forecasts to traders, grid operators, and storage optimizers so they can plan bidding strategies and battery setpoints.

Utilities use the forecasts to minimize imbalance penalties, optimize reserve procurement, and coordinate maintenance windows with expected lulls. Independent power producers feed the data into automated trading systems, while corporate offtakers rely on it to time flexible loads or hedging instruments. Some services extend to rooftop fleets, aggregating behind-the-meter PV output for distribution grid planning.

TRL 7 solutions are widely deployed, but accuracy hinges on data access and integration into market systems. As 5G, IoT, and open weather data expand, forecasting engines will become more granular, supporting distribution-level control and carbon-aware operations for data centers and EV fleets.

TRL
7/9Operational
Impact
4/5
Investment
3/5
Category
software

Related Organizations

Solargis

Slovakia · Company

95%

Specializes in solar resource assessment and PV energy forecasting using satellite data and algorithms.

Developer
Vaisala logo
Vaisala

Finland · Company

95%

A global leader in weather, environmental, and industrial measurements.

Developer
Enercast

Germany · Company

90%

Provides AI-based power forecasting for wind and solar assets to enable precise energy trading.

Developer
Meteomatics logo
Meteomatics

Switzerland · Company

90%

Delivers high-resolution weather data via API and uses 'Meteodrones' to gather lower-atmosphere data for better forecasts.

Developer
Open Climate Fix

United Kingdom · Nonprofit

90%

Nonprofit research lab focused on using open source machine learning to reduce emissions, specifically in grid forecasting.

Developer
Vortex FDC

Spain · Company

90%

Provides wind resource assessment and forecasting using the WRF (Weather Research and Forecasting) model.

Developer
Whiffle

Netherlands · Startup

90%

Uses Large Eddy Simulation (LES) on GPUs to provide ultra-high-resolution local weather forecasts for wind farms.

Developer
DNV logo
DNV

Norway · Company

85%

Provides extensive solar and wind forecasting services (Forecaster) for grid operators and asset owners.

Developer
Spire Global logo
Spire Global

United States · Company

85%

Uses a constellation of nanosatellites to collect radio occultation data, fed into ML models for forecasting.

Developer
Tomorrow.io logo
Tomorrow.io

United States · Startup

85%

Operates proprietary radar satellites and uses generative AI ('Gale') for weather intelligence.

Developer
UL Solutions logo
UL Solutions

United States · Company

85%

Offers the AWS Truepower suite, a leading platform for renewable energy project design and operational forecasting.

Developer

Supporting Evidence

Evidence data is not available for this technology yet.

Connections

software
software
Grid Load Balancing AI

Machine learning systems that forecast and optimize power dispatch across renewable-heavy grids

TRL
7/9
Impact
5/5
Investment
4/5
software
software
Environmental Risk Modeling

Satellite and AI-driven forecasts for wildfires, floods, and climate-driven supply chain disruptions

TRL
7/9
Impact
4/5
Investment
4/5
software
software
Autonomous Grid Orchestration

Real-time AI control systems that balance renewable energy, storage, and demand across power grids

TRL
5/9
Impact
5/5
Investment
5/5
software
software
Climate-Aligned Financial Risk Engines

Quantify climate hazards and carbon policy impacts on portfolios, loans, and asset valuations

TRL
5/9
Impact
5/5
Investment
4/5
software
software
Multi-Scale Climate Simulation Engines

AI-enhanced climate models simulating weather and climate from global to city scale

TRL
6/9
Impact
5/5
Investment
4/5
software
software
Climate Model Emulators and Surrogates

Machine learning models that replicate climate simulations in seconds instead of days

TRL
4/9
Impact
4/5
Investment
3/5

Book a research session

Bring this signal into a focused decision sprint with analyst-led framing and synthesis.
Research Sessions