An information processing apparatus incudes a schedule determination unit configured to determine a schedule of an operating status for causing a temperature of an air-conditioned space of each of a plurality of load-side units to reach a specified set temperature at a specified set time, based on a learning model representing an input-output relationship between input data indicating an influencing factor of a thermal load of each of the load-side units and output data indicating the thermal load.

An information processing apparatus incudes a schedule determination unit configured to determine a schedule of an operating status for causing a temperature of an air-conditioned space of each of a plurality of load-side units to reach a specified set temperature at a specified set time, based on a learning model representing an input-output relationship between input data indicating an influencing factor of a thermal load of each of the load-side units and output data indicating the thermal load.

A plurality of active filter devices (41, 42, 43) that each have an output connected to a harmonic-generating load device (2) and are capable of generating a compensating current for performing at least one of reduction of a harmonic current of the harmonic-generating load device (2) and improvement of the power factor of the fundamental wave are provided. The plurality of active filter devices (41, 42, 43) provide two or more types of capacities, and the number and combination of operating active filter devices among the active filter devices (41, 42, 43) change in accordance with the magnitude of the compensating current.

A plurality of active filter devices (41, 42, 43) that each have an output connected to a harmonic-generating load device (2) and are capable of generating a compensating current for performing at least one of reduction of a harmonic current of the harmonic-generating load device (2) and improvement of the power factor of the fundamental wave are provided. The plurality of active filter devices (41, 42, 43) provide two or more types of capacities, and the number and combination of operating active filter devices among the active filter devices (41, 42, 43) change in accordance with the magnitude of the compensating current.

In some implementations, a system performs proactive performance tests for an appliance before a time for an operational change in usage of the appliance. Usage data for an appliance associated with a property may be obtained. The obtained usage data indicates past activity of the appliance and present operational status of the appliance. Weather forecast data associated with a location of the property can be obtained. A time for an operational change in usage of the appliance can be predicted based at least on the obtained usage data for the appliance and the obtained weather forecast data. An operation directed to conducting one or more performance tests on the appliance can be performed before the predicted time for the operational change in usage of the appliance. One or more communications related to the one or more performance tests of the appliance can be provided to a client device.

In some implementations, a system performs proactive performance tests for an appliance before a time for an operational change in usage of the appliance. Usage data for an appliance associated with a property may be obtained. The obtained usage data indicates past activity of the appliance and present operational status of the appliance. Weather forecast data associated with a location of the property can be obtained. A time for an operational change in usage of the appliance can be predicted based at least on the obtained usage data for the appliance and the obtained weather forecast data. An operation directed to conducting one or more performance tests on the appliance can be performed before the predicted time for the operational change in usage of the appliance. One or more communications related to the one or more performance tests of the appliance can be provided to a client device.

Systems and methods are configured to control operation of an HVAC system providing climate control for a zone of a structure. In various embodiments, a constrained optimization problem is performed to set control commands for controlling operation of the HVAC system to achieve one or more objectives while providing a supply air flow at an air temperature and a humidity ratio for the zone at a future time. For instance, the optimization problem may include a cost function having constraints based on a desired temperature setpoint and a humidity ratio for the zone. A value is set for each control command based on the performance of the constrained optimization problem to achieve at least one of the objectives and as a result, the supply air flow at the air temperature and humidity ratio is provided by the HVAC system at the future time to the zone based on the values.

A demand response system includes a mobile application of a mobile device that is configured to initiate altering an operating condition of a network device disposed at a site using location based services. A demand response application interface module is configured to enable access between a utility company and the network device to communicate energy management information therebetween. The network device is configured to be remotely altered by each of the demand response application interface module and the mobile application separately based on the location based services and the energy management information. A method of managing a demand response system includes detecting a user being disposed away from a site, detecting energy management information from a utility company associated with the site, and initiating a reduction in energy use at the site in response to the relative location of the user and the energy management information.

An air conditioning apparatus including a storage unit that stores therein a setting table in which a schedule of an air conditioning operation for one week is set, and a control unit that executes operation control according to the schedule in the setting table, the air conditioning apparatus including a remote controller that sets operation information of a target time slot as a part of one week, and a temporary storage unit that temporarily stores therein the set operation information of the target time slot, wherein the control unit executes operation control based on the operation information of the target time slot stored in the temporary storage unit in priority to the schedule in the setting table.

A model predictive control system is used to optimize energy cost in a variable refrigerant flow (VRF) system. The VRF system includes an outdoor subsystem and a plurality of indoor subsystems. The model predictive control system includes a high-level model predictive controller (MPC) and a plurality of low-level indoor MPCs. The high-level MPC performs a high-level optimization to generate an optimal indoor subsystem load profile for each of the plurality of indoor subsystems. The optimal indoor subsystem load profiles optimize energy cost. Each of the low-level indoor MPCs performs a low-level optimization to generate optimal indoor setpoints for one or more indoor VRF units of the corresponding indoor subsystem. The indoor setpoints can include temperature setpoints and/or refrigerant flow setpoints for the indoor VRF units.