Google patents method to tune grid energy models without sharing private data
This filing describes a way to improve how computers predict electricity usage on the power grid. It uses a two-step process where a local system adjusts a prediction model based on local data and sends only the adjustments back to a central server, rather than sending the raw usage data itself.
In the filing’s own words
Methods, systems, and apparatus, including medium-encoded computer program products, for configuring location-specific electrical load models while preserving privacy. A first machine learning model configured to predict electrical load curves of an electrical utility grid can be obtained from a server. Load values associated with a particular region of the electrical utility grid can be obtained. The load values can be applied as calibration input to the first machine learning model to produce first adjustment parameters for the first machine learning model. The first adjustment parameters can be provided to the server.
What exactly they claim (claim 1)
1. (canceled)
Facts
- Company
- Google (X Development LLC)
- Official title
- PRIVACY-PRESERVING CONFIGURATION OF LOCATION-SPECIFIC ELECTRICAL LOAD MODELS
- Publication number
- US20260291277A1
- Application number
- 19440261
- Filed
- Jan 5, 2026
- Published
- Sep 24, 2026
- Inventors
- 4
- Claims
- 21
Classified under
H02J 13/12 Electricity & electronicsG05B 13/0265 Physics & computingG05B 13/042 Physics & computingH02J 2103/30 Electricity & electronicsMore from Google in Batteries & energy
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.