Predictive Energy Management Patent Application

ChangeBridge: Patent Apps - Power Supply (H02J)

Published September 24th, 2025

Detected March 26th, 2026

Summary

The USPTO has published a patent application (US20260088627A1) for a predictive energy management system. The application, filed on September 24, 2025, describes methods for optimizing energy resources using historical data and predictive algorithms, potentially incorporating generative AI.

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What changed

This document is a publication of a patent application, US20260088627A1, filed by inventors Gordon Winston, John-Philip Galinski, Nigel Walker, Mark Thompson, and Patrick J.D. Santos. The application details a predictive energy management system that collects data on energy resources and utilization to optimize energy usage. It aggregates intelligent distributed energy resources (IDERs), generates an intelligent energy profile, applies a predictive algorithm to forecast future states, and provides optimization recommendations. Some embodiments utilize generative AI and can automatically implement recommendations via computer script, scaling from single buildings to national levels.

As this is a patent application, it does not impose direct regulatory obligations or compliance deadlines on entities. However, the technology described could influence future energy management practices and potentially lead to new product development or service offerings in the energy and technology sectors. Companies involved in energy resource management, smart grid technology, or AI-driven optimization solutions may find this patent application relevant for competitive analysis and potential licensing opportunities.

Source document (simplified)

← USPTO Patent Applications

Predictive Energy Management

Application US20260088627A1 Kind: A1 Mar 26, 2026

Inventors

Gordon Winston, John-Philip Galinski, Nigel Walker, Mark Thompson, Patrick J.D. Santos

Abstract

Predictive energy management comprising systems and methods to collect information about the configuration of energy resources and historical data about energy utilization as to make recommendations on how to optimize those energy resources are disclosed. Intelligent distributed energy resources (IDERs) which are devices that are energy producers or consumers that are automatable with application programming interfaces are aggregated together. An intelligent energy profile, which comprises a summary of energy resources and historical data about utilization is generated. A predictive algorithm is applied to the intelligent energy profile thereby generating a predicted future state, and a recommendation on how to optimize against that predicted future state is generated. In some embodiments generative artificial intelligence techniques are utilized, and in some embodiments the recommendations are automatically performed via the generation of computer script embodying the recommendations. The predictive energy management techniques scale from a single building, through microgrids, to the national level.