Abstract: A facility providing systems and methods for managing and optimizing thermal comfort is provided. The facility is a software algorithm that intelligently detects the thermal comfort preferences of residential smart thermostat users, where a user's “comfort preference” is defined to refer to an estimate of a measurement of the user's comfort across varying values of some set of exogenous factors, including but not limited to indoor temperature, the time of day, the day of the week, and weather conditions. This facility encompasses the use of this “comfort preference” for the creation of an optimal schedule of setpoints for a residential thermostat which is configured to optimize some objective, including potentially user comfort, energy efficiency, load shift, or cost.

Abstract: A facility providing systems and methods for managing and optimizing energy consumption and/or production is provided. The facility provides techniques for optimizing energy-consuming and energy-producing systems to meet specified demands or goals in accordance with various constraints. The facility relies on models to generate an optimization for an energy system. In order to use generic models to simulate and optimize energy consumption for an energy system, the generic models are calibrated to properly represent or approximate conditions of the energy system during the optimization period. After the appropriate models have been calibrated for a given situation using one or more modeling parameter sets, the facility can simulate inputs and responses for the corresponding system. The facility uses the generated simulations to generate a plan or control schedule to be implemented by the energy system during the optimization period.

Abstract: A facility providing systems and methods for managing and optimizing thermal comfort is provided. The facility is a software algorithm that intelligently detects the thermal comfort preferences of residential smart thermostat users, where a user's “comfort preference” is defined to refer to an estimate of a measurement of the user's comfort across varying values of some set of exogenous factors, including but not limited to indoor temperature, the time of day, the day of the week, and weather conditions. This facility encompasses the use of this “comfort preference” for the creation of an optimal schedule of setpoints for a residential thermostat which is configured to optimize some objective, including potentially user comfort, energy efficiency, load shift, or cost.

Abstract: A facility providing systems and methods for managing and optimizing energy consumption and/or production is provided. The facility provides techniques for optimizing energy-consuming and energy-producing systems to meet specified demands or goals in accordance with various constraints. The facility relies on models to generate an optimization for an energy system. In order to use generic models to simulate and optimize energy consumption for an energy system, the generic models are calibrated to properly represent or approximate conditions of the energy system during the optimization period. After the appropriate models have been calibrated for a given situation using one or more modeling parameter sets, the facility can simulate inputs and responses for the corresponding system. The facility uses the generated simulations to generate a plan or control schedule to be implemented by the energy system during the optimization period.

Abstract: A facility providing systems and methods for managing and optimizing thermal comfort is provided. The facility is a software algorithm that intelligently detects the thermal comfort preferences of residential smart thermostat users, where a user's “comfort preference” is defined to refer to an estimate of a measurement of the user's comfort across varying values of some set of exogenous factors, including but not limited to indoor temperature, the time of day, the day of the week, and weather conditions. This facility encompasses the use of this “comfort preference” for the creation of an optimal schedule of setpoints for a residential thermostat which is configured to optimize some objective, including potentially user comfort, energy efficiency, load shift, or cost.

Abstract: A facility providing systems and methods for managing and optimizing thermal comfort is provided. The facility is a software algorithm that intelligently detects the thermal comfort preferences of residential smart thermostat users, where a user's “comfort preference” is defined to refer to an estimate of a measurement of the user's comfort across varying values of some set of exogenous factors, including but not limited to indoor temperature, the time of day, the day of the week, and weather conditions. This facility encompasses the use of this “comfort preference” for the creation of an optimal schedule of setpoints for a residential thermostat which is configured to optimize some objective, including potentially user comfort, energy efficiency, load shift, or cost.

Abstract: A facility providing systems and methods for managing and optimizing energy consumption and/or production is provided. The facility provides techniques for optimizing energy-consuming and energy-producing systems to meet specified demands or goals in accordance with various constraints. The facility relies on models to generate an optimization for an energy system. In order to use generic models to simulate and optimize energy consumption for an energy system, the generic models are calibrated to properly represent or approximate conditions of the energy system during the optimization period. After the appropriate models have been calibrated for a given situation using one or more modeling parameter sets, the facility can simulate inputs and responses for the corresponding system. The facility uses the generated simulations to generate a plan or control schedule to be implemented by the energy system during the optimization period.

Abstract: A facility providing systems and methods for managing and optimizing thermal comfort is provided. The facility is a software algorithm that intelligently detects the thermal comfort preferences of residential smart thermostat users, where a user's “comfort preference” is defined to refer to an estimate of a measurement of the user's comfort across varying values of some set of exogenous factors, including but not limited to indoor temperature, the time of day, the day of the week, and weather conditions. This facility encompasses the use of this “comfort preference” for the creation of an optimal schedule of setpoints for a residential thermostat which is configured to optimize some objective, including potentially user comfort, energy efficiency, load shift, or cost.

Abstract: A facility providing systems and methods for managing and optimizing energy consumption and/or production is provided. The facility provides techniques for optimizing energy-consuming and energy-producing systems to meet specified demands or goals in accordance with various constraints. The facility relies on models to generate an optimization for an energy system. In order to use generic models to simulate and optimize energy consumption for an energy system, the generic models are calibrated to properly represent or approximate conditions of the energy system during the optimization period. After the appropriate models have been calibrated for a given situation using one or more modeling parameter sets, the facility can simulate inputs and responses for the corresponding system. The facility uses the generated simulations to generate a plan or control schedule to be implemented by the energy system during the optimization period.

Abstract: A facility providing systems and methods for managing and optimizing energy consumption and/or production is provided. The facility provides techniques for optimizing energy-consuming and energy-producing systems to meet specified demands or goals in accordance with various constraints. The facility relies on models to generate an optimization for an energy system. In order to use generic models to simulate and optimize energy consumption for an energy system, the generic models are calibrated to properly represent or approximate conditions of the energy system during the optimization period. After the appropriate models have been calibrated for a given situation using one or more modeling parameter sets, the facility can simulate inputs and responses for the corresponding system. The facility uses the generated simulations to generate a plan or control schedule to be implemented by the energy system during the optimization period.