Energy Management AI Using Time Series Forecasting for Power Load Prediction
Summary
The USPTO published patent application US20260097684A1 disclosing an AI-based energy management system that creates synthetic training datasets to forecast power load using deep learning models. The system predicts energy storage device state of charge and controls charging operations based on projected load.
What changed
The USPTO published a patent application disclosing a method for optimal energy management using time series forecasting. The system generates synthetic training datasets, trains deep learning models to predict power load, determines projected state of charge for energy storage devices, and controls charging operations accordingly. The technology relates to electric vehicle charging infrastructure and power grid optimization.
This patent publication establishes intellectual property rights but does not impose compliance obligations. Organizations developing AI-based energy management systems or EV charging solutions should monitor this patent landscape to assess potential licensing implications or design-around requirements.
What to do next
- Monitor intellectual property landscape for related patents
- Assess potential licensing needs if developing similar energy management systems
Archived snapshot
Apr 9, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
SYSTEMS AND METHODS FOR OPTIMAL ENERGY MANAGEMENT BASED ON TIME SERIES FORECASTING OF POWER LOAD
Application US20260097684A1 Kind: A1 Apr 09, 2026
Inventors
Satvik KHUNTIA, Athar HANIF, Qadeer AHMED, Maarten MEIJER, Charles SWART, John LAHTI, Iner JORGENSEN, Shweta HARDAS
Abstract
An example method of optimized energy management includes creating a synthetic training dataset, where the synthetic training dataset includes a activity profiles for a period of time; training a deep learning model using the synthetic training dataset; predicting, using the trained deep learning model, a power load for the period of time; determining a projected state of charge (SOC) of an energy storage device during the period of time based, at least in part, on the predicted power load; and controlling charging operations for the energy storage device based on the projected SOC.
CPC Classifications
B60L 58/12 G06N 3/0442 G06Q 10/04 G06Q 50/06 G06Q 50/40 B60L 2200/36 B60L 2260/46 B60Y 2200/91 B60Y 2200/92
Filing Date
2023-09-21
Application No.
19113690
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