to_the_moon.py

 💎 Idea

Hydro powerplants in Scandinavia fail to harness a potential profit of 6 million EUR annually,

because current modeling techniques are not enough for the rate of ice melting.

We deliver Machine Learning techniques for these operators to respond in time,

capture potential profit that while increasing energy efficiency.

🚀 EU space technologies

Copernicus Sentinel 2 satellite data. Datasets on ice cover, and water flow monitoring

Copernicus Marine Environment Monitoring Service data

❄️ Connecting the Arctic

We approached the theme #2 Life on Land  for climate change adaptation.

The Artic region increases production capacity from hydro power, one source of renewable energy.

🤖 Technical aspect

Using machine learning to predict top k-best locations for water potential.

Data: time-series data of coordinates of present power plants are indicated as good locations, along with the other land-related oriented.

• Features: satellite images, coordinates of good locations, snow coverage, ice peak, water velocity, elevation
• Label: k-best locations -> most optimal one

Team 

Our team consists of a Product-owner, a Designer, a Bussines hustler, a Tech deep-mind,

• Phung Tri Anh - Account Manager / Product-owner (pulling all of us together)
• Nguyen Thanh Tuan -  Machine Learning enthusiast (a humble guy refusing the title "ML engineer")
• Do Tien Dung - Business analyst (barraging the team with his Devil Advocate)
• Thai Thi My An - Graphic Designer (drawing alluring arts)