Humans cannot accurately predict wind speeds higher than 10m (residential height). Current prediction models are based off knowingly flawed modeled data alone. Until recently, observed data did not exist in a consumable format. We’ve been able to create one of the very first prediction models using actual observed data rather than just modeled.

Renewable Wind Energy:
Right now, only 59% of the wind that goes into turbines is usable as renewable energy. Companies have started to move toward other forms of renewable energy. We know this percentage can be improved upon.

Drone Delivery and Advanced Air Mobility:
If winds are higher at 100m than on the surface, because they are battery operated, they may not last the duration of their flight. The drone would need to either scratch the mission completely, or land and be manually located, have the battery switched out and restart the mission resulting in lost time and income and unsatisfied customers.

Licensing and integrating with WindtelligentAI’s API solution that accurately predicts wind speeds at 100m from 1 hour to 2 weeks in advance. 

If high or low winds are expected, we can alert wind farms to adjust for as much power output as possible. We can also help to identify the best places in the world for wind farms to be located.

Drone Delivery and Advanced Air Mobility:
If high winds are predicted, we can alert the Drone company to wait to launch until the winds die down saving time, resources, money and improve customer satisfaction and delivery times.

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Potential Use Case:
We spoke to TruWeather, a drone company, about a use case on how we could save them money. They told us if there was a $10,000,000 drone ops budget, 20% (2MM) of that budget is dedicated to unforeseen weather issues.  TruWeather estimated that out of that 2 million we could recover approximately 35% of that cost. Meaning we could save a company like TruWeather ~$700,000 a year aka 7% savings on their overall ops budget.

Business Verticals:
Renewable Wind Energy - Can help with utilities, transmission operations, and can maximize renewable power output. Energy trading is also a consideration. $77.7 Billion market worth
Drone Industry - Helping to improve mission success and save companies on their weather-related costs.  $27.4 Billion market worth projected to grow to $58.4 Billion by 2026.

Aviation - For air refueling, emergency landings and when to reopen airports after extreme weather events. $359.3 Billion market worth. 

Construction and Infrastructure - Improving worker safety on tall towers and to properly weight tall towers against wind speeds in high-risk areas.  $1.36 Trillion market worth.

Business Model:
API first solution that served 1 hour to 2 weeks predictions on wind speeds at 100m and above.
SaaS Model-
Individual (Hobbyists) - 100,000 calls per month at $150.
Small Business (Aviation and Infrastructure) - 10,000,000 calls per month at $1,500.
Enterprise (Renewable Wind and Drones) - 100,000,000 calls per month at $15,000.

Total Available Market: $1.82 Trillion
Serviceable Available Market (top 1000 companies in mind): $180,000,000.
Serviceable Obtainable Market (70% of SAM): $126,000,000.

Competitive Advantage:
-Right now, each potential customer independently predicts using data known to be full of errors. This dataset, ECMWF, only has real time data at 100m in a MODELED format and charges companies $200,000 for access to this data (no predictions).
-In contrast, we’ve compiled approx. 86 billion rows of OBSERVED wind readings at 100m and above dating back to 1932. This data was extremely fragmented and notoriously hard to work with. But we’ve managed to make it work!

Machine Learning and AI:
-Using a combination of qualitative and quantitative data, we developed a new predictive model.

-After JUST a month in business, we’ve absolutely exceeded expectations. 

-Mean Average Error should be 0 we are at 0.6 so at any given time we could be .6 units off in our predictions

-Mean Squared Error of 1 is a perfect prediction, we are currently at 0.97

-But we need your help to take it to the next level.

Meet the team!
Katelyn Hertel - Data Engineer, NASA Data Ambassador, Climate Hacker, and CEO.
Shwetha Jayaraj - Machine Learning Engineer, Quantum Computing Expert at NYIT.