A Thermal Performance Forecast approach is described that can be used to forecast heating and cooling fuel consumption based on changes to user preferences and building-specific parameters that include indoor temperature, building insulation, HVAC system efficiency, and internal gains. A simplified version of the Thermal Performance Forecast approach, called the Approximated Thermal Performance Forecast, provides a single equation that accepts two fundamental input parameters and four ratios that express the relationship between the existing and post-change variables for the building properties to estimate future fuel consumption. The Approximated Thermal Performance Forecast approach marginally sacrifices accuracy for a simplified forecast. In addition, the thermal conductivity, effective window area, and thermal mass of a building can be determined using different combinations of utility consumption, outdoor temperature data, indoor temperature data, internal heating gains data, and HVAC system efficiency as inputs.

Methods for controlling temperature in a conditioned enclosure such as a dwelling are described that include an “auto-away” and/or “auto-arrival” feature for detecting unexpected absences which provide opportunities for significant energy savings through automatic adjustment of the setpoint temperature. According to some preferred embodiments, when no occupancy has been detected for a minimum time interval, an “auto-away” feature triggers a changes of the state of the enclosure, and the actual operating setpoint temperature is changed to a predetermined energy-saving away-state temperature, regardless of the setpoint temperature indicated by the normal thermostat schedule. The purpose of the “auto away” feature is to avoid unnecessary heating or cooling when there are no occupants present to actually experience or enjoy the comfort settings of the schedule, thereby saving energy.

A system for allocating resources across equipment that operate to serve one or more loads of a building. The system includes one or more memory devices storing instructions that cause one or more processors to receive operational data defining at least one of planned loads to be served by the equipment or utility rates for one or more time steps within a simulation period, determine whether the operational data define the planned loads or the utility rates for each time step within the simulation period, and in response to a determination that the operational data do not define the planned loads or the utility rates for each time step within the simulation period, identify one or more time steps for which the planned loads or the utility rates are not defined and initiate an action to define the planned loads or the utility rates for the identified time steps.

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).

Climate control systems and related methods and systems therefore a disclosed. In an embodiment, the climate control system includes a heat exchanger configured to discharge conditioned air to an indoor space. In addition, the climate control system includes a display and a controller coupled to the display. The controller is to generate an operation selection option on the display. The operation selection option includes a plurality of selections for operating of the climate control system based on operational efficiency or occupant comfort within the indoor space. The controller is to adjust a temperature of the heat exchanger relative to a user selection from the plurality of selections.

The disclosure provides a method for controlling air conditioner temperature. The method comprises the following steps: acquiring the operating mode of an air conditioner at the current moment, wherein the operating mode comprises refrigerating mode and heating mode; acquiring the historical duration of operation of the air conditioner in the operating mode; judging whether the historical duration is greater than the first set duration or not; and if so, setting the current operating temperature of the air conditioner based on the set operating temperature and the optimal reference temperature of the air conditioner at the first specified historical moment. The method has the advantages that the current operating temperature can be set according to the historical operating temperature of the air conditioner, and body feeling delay caused by the fact that a user has to manually set the temperature is avoided.

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 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.