Windscape.ai

Affordable

Actionable

Wind

Alerts

10x Breakthrough
technology innovation

The vast majority of wind turbines today aren't able to adjust to wind changes until after winds arrive. That reduces energy production and exposes valuable equipment to damaging gusts. Doppler LIDAR applied to providing wind-event alerts ("preview" or "feed forward" data) is very expensive to deploy and maintain, and is therefore not widely deployed. Windscape.ai is a 10x cost breakthrough for preview data to enable wind farms across the industry to optimize operations.

 

Windscape.ai's 10- to 60-second alerts of actual wind changes go directly to the site's control system, enabling it to optimize the turbines' pitch, yaw and other settings for maximum energy capture and lower impact on gearboxes and components. The result will be increased wind farm energy production and extended turbine life.

Windscape.ai uses an ultra-low-cost, wifi mesh network of sensors across and surrounding the wind farm that feed into AI data correlation. Advances in edge AI and sensor-on-a-chip technologies have now made the approach both possible and practical. This patented "hardware enabled AI" technique is ten times cheaper than any alternative. Our experienced team is currently completing a field pilot to prove effectiveness.

 
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Wind industry today

  • More wind power was built in the US than natural gas last year.

  • 65,000 wind turbines installed in the US

 

  • 400,000 wind turbines are installed worldwide

 

  • $100B annual US and $600B world annual wind industry with 20% annual growth

 

  • Accelerating national attention to green energy.

 

  • Meanwhile 2018 US average wind PPAs were below 2¢/kWh - squeezing margins

 

  • Small production increases have huge impacts on margins

 

  • Turbine size and cost keeps growing - going to 6 MW onshore

 

  • It costs hundreds of thousands of dollars to replace a gearbox

Windscape:ai value proposition

Example assumptions
  •     Wind farm size: 50 MW

  •     Annual output*: 153,300 MWh/yr

    • (Based on 35% CF - average for US fleet)

  •     Annual revenue*: $6,132,000

    • (Based on $40 PPA - low range)

  •     Output improvement with windscape.ai: 1% 

    •  (Anticipate 2%-3%. Using lowest estimate here.)

  •     Annual energy production (AEP) only.

    • Turbine life improvements not included.

Result: Increased wind farm annual revenue: $61,320
 

Technology concept

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Wind = pressure

Wind changes aloft are caused by and produce pressure changes due to conservation of energy.
 

Pressure changes near ground and both upstream and downstream from wind farms are related to wind changes at the wind farm.

Sensor arrays

Dozens of low-cost pressure sensors on short posts will be scattered around wind farms within an area up to 400m.

 

The data will be collected on a wireless mesh network.

AI-generated alerts

Windscape.ai will build a predictive AI model of the data from the sensor array and tower-top anemometers. 

 

The edge AI model will compute real time wind change alerts to the owner’s SCADA. 

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wind industry landscape

  • 65,000 wind turbines installed in the US will generate 400 GWh in 2021

 

  • 400,000 wind turbines are installed worldwide

 

  • $100B annual US and $600B world annual wind industry with 20% annual growth

 

  • Accelerating national attention to green energy.

 

  • Meanwhile 2018 US average wind PPAs were below 2¢/kWh - squeezing margins

 

  • Small production increases have huge impacts on margins

 

  • Turbine size and cost keeps growing - going to 6 MW onshore

 

  • It costs hundreds of thousands of dollars to replace a gearbox

Development progress

Known factors
  • Pressure changes (Δp) are the drivers of air movement (wind).

  • The Bernoulli Principle drives boundary layer Δp from moving air.

  • Local and regional weather, thermals, etc. also drive wind and Δp.

  • Turbulence will also show up in high frequency Δp.

  • Δp can be measured very accurately and rapidly.

 

Expected but still to be demonstrated:
  • Area wide measurements of Δp near ground level correlates to wind vectors at the wind farm.

  • Correlated patterns repeat and therefore allow AI prediction.

  • Atmospheric events 60m or more above ground quickly produce Δp at the sensor array.

  • These effects are significant enough to measure:

             - Pressure drops downwind creating high p to low p movement.

             - Intersection of two moving air masses.

             - Partial heating from partial cloud cover.

             - Weather system movement (e.g. thunderstorm).

 

Our field tests are starting to answer these questions.
 

Roadmap

Pilot 1

Began in June 2021 with the helpful assistance of the UC Davis Atmospheric Science group at their test site.

  • 20 node array with pressure sensors in 300m radius area

  • Wifi base station and communications gateway

  • Tower anemometer data feed and AI analysis of data

  • Demonstrate Δp at the sensor array.

  • Write up results for initial fundraising

Phase 2 testing
  • Gen 2 sensor nodes with solar power

  • Edge AI capability

  • Multiple test sites

  • Live data analysis

 

Commercial Site Pilot
  • Sites in progress with well respected owners. 

News

        Windscape.ai is upping our game on updates and notifications. We invite you to follow the new Windscape.ai LinkedIn page or check back for updates.

  • Linkedin

Recent accomplishments:

  • Accepted to The Batchery Incubator - Q421

  • Patent issued - years ago

  • Sweat-equity team assembled - this year

  • Demo site and data proof-of-concept completed - recently

  • Pilot sites identified - for later this year

 

Team

 
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Richard D. Ely, PhD

Inventor, Advisor

Davis Wind, Founder

- Davis Hydro, Founder

 

- Massachusetts Institute of Technology

  • BS Geophysics

  • Graduate Research OR and EE

- UC Berkeley

  • MS Civil Engineering

- University of Rhode Island

  • MA Resource Economics

- University of Connecticut

  • MS Economics

  • Ph.D. Resource Economics

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Eric Thompson

CEO

- Extensive leadership in wind and renewable energy

- Ensemble Energy (AI for wind farm O&M)

Chief Business Development Officer

- Nordic Windpower (Utility-scale WTG OEM)

  • Co-Founder 

  • VP Business Development 

- Strategy and Bizdev Consultant with multiple wind, solar and hydro technology companies

- Cornell University

  • BS Mechanical and Aerospace Engineering