G05D23/1923

SMART THERMOSTAT WITH MODEL PREDICTIVE CONTROL AND DEMAND RESPONSE INTEGRATION

A system includes a plurality of thermostats corresponding to a plurality of HVAC systems that serve a plurality of spaces and a computing system communicable with the plurality of thermostats via a network. The computing system is configured to, for each space of the plurality of spaces, obtain a set of training data relating to thermal behavior of the space, identify a model of thermal behavior of the space based on the set of training data, perform a model predictive control process using the model of thermal behavior of the space to obtain a temperature setpoint for the space, and provide the temperature setpoint to the thermostat corresponding to the HVAC system serving the space. The plurality of thermostats are configured to control the plurality of HVAC systems in accordance with the temperature setpoints.

Programmable Temperature Control System For Pools And Spas

A system and method are provided for controlling water temperature in a body of water. The temperature control system includes a processor, a user interface for receiving a desired temperature and a desired time for reaching the desired temperature, a sensor interface for receiving sensor information from one or more sensors, and an actuator interface for controlling a plurality of heat sources. The processor determines one or more optimal heat sources for heating the body of water to the desired temperature by the desired time. The processor controls the one or more optimal heat sources through the actuator interface and periodically polls the sensor interface to determine whether changes in the operating environment require additional or alternate heat sources to be activated to ensure that the body of water is heated to the desired temperature by the desired time.

THERMAL CONTROL SYSTEM

The subject matter of this specification can be embodied in, among other things, a method for time shifting when a cold storage facility is cooled that includes determining a thermal model of a cold storage facility, obtaining an energy cost model that describes a schedule of variable energy costs over a predetermined period of time in the future, determining an operational schedule for at least a portion of a refrigeration system based on the thermal model, the energy cost model, and a maximum allowed temperature, and powering on the portion the refrigeration system based on the operational schedule, cooling, by the powered portion of the refrigeration system to a temperature below the maximum allowed temperature, reducing power usage of the powered portion of the refrigeration system based on the operational schedule, and permitting the facility to be warmed by ambient temperatures toward the maximum allowed temperature.

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 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.

Thermal control system

The subject matter of this specification can be embodied in, among other things, a method for time shifting when a cold storage facility is cooled that includes determining a thermal model of a cold storage facility, obtaining an energy cost model that describes a schedule of variable energy costs over a predetermined period of time in the future, determining an operational schedule for at least a portion of a refrigeration system based on the thermal model, the energy cost model, and a maximum allowed temperature, and powering on the portion the refrigeration system based on the operational schedule, cooling, by the powered portion of the refrigeration system to a temperature below the maximum allowed temperature, reducing power usage of the powered portion of the refrigeration system based on the operational schedule, and permitting the facility to be warmed by ambient temperatures toward the maximum allowed temperature.

CASCADED SYSTEMS AND METHODS FOR CONTROLLING ENERGY USE DURING A DEMAND LIMITING PERIOD

A system includes a processing circuit configured to provide an energy use setpoint based on energy data including an energy characteristic and subject to a constraint based on a variable condition of a building. The processing circuit is configured to estimate a value for the energy use setpoint that will result in the variable condition of the building satisfying the constraint and provide a control signal for equipment based on a difference between the energy use setpoint and a measured energy use.

Optimization of energy use through model-based simulations
11782465 · 2023-10-10 · ·

A facility implementing systems and/or methods for achieving energy consumption/production and cost goals is described. The facility identifies various components of an energy system and assesses the environment in which those components operate. Based on the identified components and assessments, the facility generates a model to simulate different series/schedules of adjustments to the system and how those adjustments will effect energy consumption or production. Using the model, and based on identified patterns, preferences, and forecasted weather conditions, the facility can identify an optimal series or schedule of adjustments to achieve the user's goals and provide the schedule to the system for implementation. The model may be constructed using a time-series of energy consumption and thermostat states to estimate parameters and algorithms of the system. Using the model, the facility can simulate the behavior of the system and, by changing simulated inputs and measuring simulated output, optimize use of the system.

SMART ENERGY SCHEDULING OF HVAC SYSTEM DURING ON-PEAK HOURS

Systems and techniques are described for monitoring energy use habits of consumers. In some implementations, a method includes obtaining temperature data from a monitored property. An energy model of the monitored property is generated based on the obtained temperature data. The current temperature data is obtained from the monitored property. The current temperature data is provided to the generated energy model to generate a duty-cycle for turning an HVAC system of the monitored property off an on during the on-peak hours. The HVAC system of the monitored property is instructed to cycle off and on during the on-peak hours based on the generated duty-cycle.

HVAC system with predictive airside control

A heating, ventilation, or air conditioning (HVAC) system for a building includes airside HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers are configured to generate airside energy targets for the one or more building spaces using a heat transfer model that defines a relationship between the airside energy targets, a temperature of the one or more building spaces, and a thermal capacitance of the one or more building spaces. The one or more controllers are configured to control the airside HVAC equipment in accordance with the airside energy targets.

Wireless charging method, device and system
11811245 · 2023-11-07 · ·

A method of compensating for temperature dependent Q factor variations in a wireless charger includes receiving, by the wireless charger, a reference Q factor value from a device to be charged. The method also includes the wireless charger determining a Q factor threshold value from the reference Q factor. The method further includes the wireless charger measuring a Q factor associated with a transmit coil of the wireless charger. The method also includes determining a temperature value. The method further includes applying a temperature compensation calculation to the measured Q factor using the temperature value to produce a temperature compensated Q factor. The method also includes comparing the temperature compensated Q factor with the Q factor threshold value. The method may also include compensation for temperature dependent internal power loss values.