A system and method for controlling air quality within an indoor space are disclosed. An example system includes an air circulation unit that moves air through ductwork of a heating, ventilation, and air conditioning (HVAC) system and an air sanitization unit within the ductwork of the HVAC system that sanitizes air passing through the ductwork of the HVAC system. The system further includes an indoor air quality controller that controls a rate at which the air circulation unit moves the air through the ductwork of the HVAC system responsive to inputs received at the indoor air quality controller and controls an operational status of the air sanitization unit responsive to the inputs received at the indoor air quality controller.

Occupancy data over time is received for each of several spaces within a building from occupancy sensors that are disposed within each of the spaces. An occupancy value is determined for each of at least some of the several spaces based on the received occupancy data, each occupancy value representative of a percent of time that the respective space was occupied over an identified period of time. The space that had a highest occupancy value over the identified period of time is identified. A utilization value is determined for each of the spaces, wherein the utilization value is representative of a ratio of the occupancy value of the respective space and the highest occupancy value. An operation of the building is changed based at least in part on the utilization value of at least one of the plurality of spaces.

Logical boundaries enclosing a physical area are defined. A segment of the logical boundaries is defined as a directional gate, wherein traversing the gate into the physical area is defined as an ingress and traversing the gate out of the physical area is defined as an egress. The directional gate is monitored, and ingresses and egresses are detected. An occupancy count of the physical area is maintained, based on monitoring the gate and detecting ingresses and egresses. One or more conditions are tracked in addition to the occupancy count. Artificial intelligence (AI) processing is applied to the maintained occupancy count and the additional tracked condition(s), in real-time as the monitoring, maintaining and tracking are occurring. One or more responsive actions are automatically taken as a result of applying the AI processing to the maintained occupancy count and the additional tracked condition(s).

A system includes multiple HVAC systems. After receiving a demand request, a multiple-system controller a first anticipated power consumption associated with operating a first HVAC system at a first temperature setpoint during a future period of time of the demand response request and a second anticipated power consumption associated with operating a second HVAC system at a second temperature setpoint during the future period of time. Based at least in part on the first and the second anticipated power consumptions, a staging schedule is determined that indicates when to operate the first and second HVAC systems.

Methods and apparatus for an exhaust demand control system for measuring one or more contaminants at one or more exhaust locations within one or a plurality of exhaust ducts or plenums served by an exhaust fan system. Example systems and methods can include sensing the one or more contaminants within the one or more exhaust duct locations using a multipoint air sampling system having one or more sensors and comparing contaminant concentration measurements from the one or more of said exhaust duct or plenum locations against an action level to create a fan setback signal.

A method of controlling a heating, ventilation, and air-conditioning, HVAC, system. The method includes: controlling indoor environmental conditions using the HVAC system, detecting a load of the HVAC system, inputting detected indoor environmental conditions and detected outdoor environmental conditions into a load prediction model to generate a predicted load, and training the load prediction model to reduce a difference between the predicted load and the detected load of the HVAC system; determining requested indoor environmental conditions associated with a future time period; determining predicted outdoor environmental conditions within the future time period using a weather forecast; inputting the requested indoor environmental conditions and the predicted outdoor environmental conditions into the trained load prediction model to determine a predicted load for the future time period; controlling the HVAC system to reduce a load of the HVAC system within the future time period using the determined predicted load.

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.

An HVAC system includes a first air quality sensor and a second air quality sensor. The system contains a controller which calculates a differential between the first air quality sensor and the second air quality sensor. The differential is compared to a threshold. If the threshold is satisfied, a fan is engaged which may, reducing the air quality differential as sensed by the first and second air quality sensor.

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).

A visible light sensor may be configured to sense environmental characteristics of a space using an image of the space. The visible light sensor may be controlled in one or more modes, including a daylight glare sensor mode, a daylighting sensor mode, a color sensor mode, and/or an occupancy/vacancy sensor mode. In the daylight glare sensor mode, the visible light sensor may be configured to decrease or eliminate glare within a space. In the daylighting sensor mode and the color sensor mode, the visible light sensor may be configured to provide a preferred amount of light and color temperature, respectively, within the space. In the occupancy/vacancy sensor mode, the visible light sensor may be configured to detect an occupancy/vacancy condition within the space and adjust one or more control devices according to the occupation or vacancy of the space. The visible light sensor may be configured to protect the privacy of users within the space via software, a removable module, and/or a special sensor.