Energyhub, Inc.

Benjamin Hertz-Shargel

Michael DeBenedittis

Seth Frader-Thompson

Varun Mehra

William Cybriwsky

Andrew Kontaxis

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

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing an electrical grid by controlling devices connected to the electrical grid. In one aspect, a method comprises: autonomously determining whether a control strategy update criterion is satisfied, and in response to determining that the control strategy update criterion is satisfied: identifying: (i) a plurality of devices connected to the electrical grid, and (ii) one or more objectives to be satisfied by the plurality of devices; generating, for the plurality of devices and for a plurality of controls, simulated data that simulates operation of the plurality of devices based on application of the plurality of controls; and identifying, by an optimization procedure and based on at least the simulated data, a respective control to be applied to each of the plurality of devices to satisfy the objectives.

Autonomous electrical grid 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

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining controls to be applied to a population of devices. In one aspect, a method comprises: determining a plurality of simulated load curves, wherein each simulated load curve simulates load generated by a respective device from a population of devices based on application of a respective control from a set of controls; adjusting values of a plurality of weights during a plurality of optimization iterations using an optimization technique to optimize a loss function, wherein each weight corresponds to a respective simulated load curve, wherein the loss function measures: a sparsity of the values of the plurality of weights, and an error between: (i) an aggregate load curve that is defined by combining the simulated load curves in accordance with the values of the plurality of weights, and (ii) a target load curve.

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Roslyn Estates, NY, United States of America

Benjamin Hertz-Shargel