[Object] To realize control that, in control of facility equipment, reduces electricity charges, taking into consideration fluctuation in power amount charge unit prices, and also a relation between a prediction value of power usage and a greatest value in a relevant period.

[Solution] A control device that generates control information for controlling facility equipment of a consumer includes an information acquisition unit that acquires information of a power amount charge unit price that fluctuates and information of power usage of the consumer, and a control information generating unit that, on the basis of the power amount charge unit price in an object interval that is an interval in which control of the facility equipment is performed and information of power usage of the consumer in an interval after the object interval, generates control information for the facility equipment of the consumer in the object interval.

Methods, systems, and devices for an interactive environmental control system are described. In some examples, operating temperatures for individual zones of an environment may be determined based on inputs received from occupants of the respective zones. For example, a building may be separated into zones, and environmental conditions at each zone may be monitored and adjusted independently. Each occupant of a zone may update their environmental preference and the system may utilize the user inputs to set and adjust an operating temperature for the respective zone based on the occupants' preferences. In some examples, the system may implement machine learning techniques to predict and set operating conditions for the zones based on inputs, such as a history of inputs, from building occupants (e.g., from occupants of a respective zone).

Methods, systems, and devices for an interactive environmental control system are described. In some examples, operating temperatures for individual zones of an environment may be determined based on inputs received from occupants of the respective zones. For example, a building may be separated into zones, and environmental conditions at each zone may be monitored and adjusted independently. Each occupant of a zone may update their environmental preference and the system may utilize the user inputs to set and adjust an operating temperature for the respective zone based on the occupants' preferences. In some examples, the system may implement machine learning techniques to predict and set operating conditions for the zones based on inputs, such as a history of inputs, from building occupants (e.g., from occupants of a respective zone).

Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system receives an emissions rate forecast for a predefined future time period. Using the emissions rate forecast, a plurality of emissions differential values are created for a plurality of points in time during the predefined future time period. The emissions differential values represent a change in predicted emissions over time. Based on the plurality of emissions differential values and a predefined maximum number of emissions demand response events, an emissions demand response event is generated during the predefined future time period. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system receives an emissions rate forecast for a predefined future time period. Using the emissions rate forecast, a plurality of emissions differential values are created for a plurality of points in time during the predefined future time period. The emissions differential values represent a change in predicted emissions over time. Based on the plurality of emissions differential values and a predefined maximum number of emissions demand response events, an emissions demand response event is generated during the predefined future time period. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system obtains a history of emissions rates. Based on the history of emissions rates, a future time period of predicted high emissions is identified. An emission demand response event participation level of an account mapped to a thermostat is determined for the future time period of predicted high emissions. The emissions demand response event participation level may be one of a plurality of emissions demand response event participation levels. based on the emissions demand response event participation level of the account, an emissions demand response event is generated during the future time period of predicted high emissions. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system obtains a history of emissions rates. Based on the history of emissions rates, a future time period of predicted high emissions is identified. An emission demand response event participation level of an account mapped to a thermostat is determined for the future time period of predicted high emissions. The emissions demand response event participation level may be one of a plurality of emissions demand response event participation levels. based on the emissions demand response event participation level of the account, an emissions demand response event is generated during the future time period of predicted high emissions. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system obtains an emissions rate forecast for a predefined future time period. Using the emissions rate forecast, a future emissions rate event during the predefined future time period is identified. The future emissions rate event comprises an indication of predicted magnitude and a time period when a predicted emissions rate will be at an increased or decreased level. A confidence value indicating a certainty of the future emissions rate event occurring as predicted is determined. Based on the identified future emissions rate event and the confidence value, an emissions demand response event having a start time and an end time during the future emissions rate event is generated. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system obtains an emissions rate forecast for a predefined future time period. Using the emissions rate forecast, a future emissions rate event during the predefined future time period is identified. The future emissions rate event comprises an indication of predicted magnitude and a time period when a predicted emissions rate will be at an increased or decreased level. A confidence value indicating a certainty of the future emissions rate event occurring as predicted is determined. Based on the identified future emissions rate event and the confidence value, an emissions demand response event having a start time and an end time during the future emissions rate event is generated. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Techniques for performing an emissions demand response (EDR) event are described. In an example, a cloud-based HVAC control system may obtain a first emissions rate forecast and generate an EDR event with a start time and end time based on the first emissions rate forecast. The EDR event may then be transmitted to a thermostat and stored in a memory of the thermostat. At the start time, the thermostat may commence controlling an HVAC system according to the EDR event. After the start time and prior to the end time, the cloud-based HVAC control system may obtain a second emissions rate forecast and generate a modified EDR event with a modified end time. The modified EDR event may be transmitted to the thermostat before the end time and/or the modified end time whereupon the thermostat may control the HVAC system accordingly until the modified end time is reached.