GNY Machine Learning Accurately Predicting California’s Energy Supply & Demand – Live Demo


  • You can now test GNY’s predictions on California’s next day energy prediction, and compare our predictions with the U.S government’s.

  • These demos are part of a larger vision for how GNY can support the work of scientists and advocates fighting for a sustainable and green planet long-term with greater impact.

What you see here is a snapshot of our GNY ML engine going head-to-head with the U.S. Energy Information Administration and outperforming it in predicting energy demand for California for 3 specific dates in January.

The predictions were made the day before the actuals. For example the Jan 18 prediction was made on the 17th. You can test these predictions yourself with our API links below.

Test GNY’s Predictions for Yourself

You can now access our ML predictions for California energy demand (24hrs in advance), supply (24 hours in advance), and our up-to-date predictions for when California will be generating 10% of their own electricity from solar produced in-state which we defined as the “peak fossil fuel” moment for this demo.

To run our ML model predicting the “peak” date for fossil fuel usage for electricity in California CLICK HERE

To run our ML model predicting California electrical demand CLICK HERE

To run our ML model that predicts California’s energy supply CLICK HERE

Our Perspective on the Role of Decentralized ML in the Fight for a Greener More Sustainable Planet.

One thing became clear to the GNY team during the research and execution of this project: the best way to ensure that green technologies replace unsustainable and polluting technologies is to ensure that they are more cost effective and favored by investors. Decentralized ML has a unique part to play in this effort, as it allows companies to showcase to investors, policy makers, and consumers the efficiencies, long term benefits and the results of a transition to green energy.

Machine learning will be crucial in predicting the negative effects of fossil fuels. Predicting their decline, and demonstrating the more promising financial rewards of going “green” will be decisive is seeing clean energy become the market disruptor. Furthermore, by creating decentralized systems for these predictions you can empower collaboration by similarly minded players across the globe, in a “trustless” and efficient data environment.

This “California Climate Predictions – Live Feed” post caps off our climate demo series of blog posts that also included the “GNY Climate Change Use Case” and “Using Machine Learning to Predict California’s “Peak” Fossil Fuel Consumption“. If you have not already discovered these posts then we suggest you review them to get a better idea of the GNY tech and its power for good.

As GNY advances its Dataplace (venture investment market in AI data) and builds relationships with other groups focused on climate change you can expect to see more powerful demos, tools, and tech releases focused on ensuring the healthy and balanced future of the human race on our planet earth.

Let us know what you think of the demo in our Telegram chat group.

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