Energyhub, Inc.

Michael DeBenedittis

Seth Frader-Thompson

Benjamin Hertz-Shargel

Andrew H Martin

Jonah Gold

Michele Tepper

William Cybriwsky

Laura Seidman

Joshua Oberwetter

Varun Mehra

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a thermostat-based demand response event. In one aspect, a method includes accessing, for sites, historical readings of HVAC activity, indoor temperature, and outdoor temperature and building a model for each of the sites using the historical readings of HVAC activity, indoor temperature, and outdoor temperature. The method also includes using a simulation engine to achieve a target load shed and load reduction shape for a thermostat-based demand response event, and performing the thermostat-based demand response event based on results of the simulation engine.

Demand response technology utilizing a simulation engine to perform thermostat-based demand response simulations

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing thermal modeling. In one aspect, a method includes receiving monitoring data comprising temperature data measured inside a site, mode data, and state data, receiving weather data descriptive of weather at the site, and aligning the received temperature data, mode data, and state data with the received weather data. The method also includes determining an internal heat gain representing an amount of heat generated at the site irrespective of the heating or cooling system, determining at least one of a thermal product for the site or a thermal potential for the heating or cooling system, generating, based on the internal gain and the thermal product or the thermal potential, a thermal model for the site, and providing, as output, the generated thermal model.

Thermal modeling technology

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing load reduction optimization. In one aspect, a method includes accessing load reduction parameters for a load reduction event, accessing energy consumption models for multiple systems involved in the load reduction event, and performing, based on the load reduction parameters and the energy consumption models, a plurality of simulations of load reduction events that simulate variations in control parameters used to control the multiple systems. The method also includes optimizing, against a load reduction curve, the load reduction event by iteratively modifying the control parameters used in the plurality of simulations of load reduction events, and outputting the optimal load reduction event with optimized control parameters.

Load reduction optimization

Techniques for energy management including enrollment using verification technology. In some implementations, a computing device receives applicant data to execute an enrollment application for an applicant applying for a particular energy management program. The computing device receives grid operator data from a grid operator to verify against the applicant data. The computing device determines whether the applicant data verifies to the grid operator data. The computing device generates an enrollment recommendation that corresponds to the applicant for the particular energy management program in response to determining whether the applicant data verifies to the grid operator data. The computing device provides the enrollment recommendation to a grid operator for review of whether to accept or reject the applicant to the particular energy management program.

Enrollment verification in energy management

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a load adjustment simulation and a capacity forecast. In one aspect, a method comprises: generating, using a simulation system, a load adjustment simulation that characterizes predicted energy consumption by a population of energy consuming devices based on performance of a current load adjustment event; generating, using the simulation system, a baseline load simulation that characterizes predicted energy consumption by the population of energy consuming devices based on non-performance of the current load adjustment event; determining, using the baseline load simulation, a load adjustment simulation error that is an estimate of an error between: (i) the load adjustment simulation, and (ii) actual energy consumption by the population of energy consuming devices based on performance of the current load adjustment event; and updating the load adjustment simulation using the load adjustment simulation error.

Capacity forecasting

Enhancements to premises monitor or control device (e.g., thermostats, etc.) are detailed herein. For example, enhanced relating to simplification of scheduling, are described as well as other enhancements relating to reducing device costs or consumption costs, providing richer features or more robust feature sets, and/or delivering associated product or services with increased simplicity, convenience, or ease.

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Brooklyn, NY, United States of America

Michael DeBenedittis