Honeywell International Inc.

Ademco Inc.

Jan Berka

Ondrej Holub

Marek Sikora

Jiri Rojicek

Petr Endel

Richard Finger

Methods, devices, and systems for energy consumption modeling are described herein. One device includes a memory, and a processor configured to execute executable instructions stored in the memory to assign energy consumption data to an on state and an off state of a device, generate a model configured to predict a future state of the device and a duration of the future state based on a duration of a current state of the device, a duration of a previous state of the device, and operating conditions of the device, and predict a future energy consumption of the device using the assigned energy consumption data and the generated model.

Energy consumption modeling

Devices, systems, and methods for providing demand response are described herein. One device includes instructions executable to receive thermostat data associated with an operating thermostat of a heating, ventilation, and air conditioning (HVAC) system in a structure over a period of time, determine a first portion of the thermostat data corresponding to steady-state operation periods of the thermostat, determine a second portion of the thermostat data corresponding to operation periods of the thermostat that are responding to temperature setpoint changes, determine a third portion of the thermostat data corresponding to operation periods of the thermostat that are transitions between the steady-state operation periods and the operation periods that are responding to temperature setpoint changes, and create a steady-state model of the thermostat based on the first portion of the thermostat data, a setpoint-change model of the thermostat based on the second portion of the thermostat data, and a transition model of the thermostat based on the third portion of the thermostat data.

Providing demand response

A method includes obtaining energy consumption information representative of energy consumption behaviors of multiple customers, grouping the multiple customers into multiple different clusters based on the consumption behaviors of the multiple customers, and generating an energy consumption model for each different cluster to enable forecasting of energy demand of the multiple customers.

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Prague, Czechia

Jan Berka