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.

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 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.

Systems and methods are provided herein for determining motion in a volume using a lighting based sensor. A status of a light is determined with which a motion sensor is associated. Motion measurements are received from the motion sensor. Based on the motion measurements, a motion score is determined. A room status is adjusted based on the motion score.

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 (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.

Air-cleaning control is performed in accordance with a situation of motion of a person and brightness in a room. An air-cleaning device (100) includes: a first determination unit (11) that determines, from a detection signal from a person detection sensor (31), whether a state of an air-cleaning target room is at least any of a state where a person is absent in the air-cleaning target room, a state where a person is present and a motion amount is small, and a state where a person is present and the motion amount is large; a second determination unit (12) that determines, from a detection signal from as illuminance sensor (32), whether the state of the air-cleaning target room is at least any of a state where it is bright inside the air-cleaning target room and a state where it is dark inside the air-cleaning target room; and an operation control unit (13) that controls an operation of an air-cleaning function by using determination results of the first determination unit (11) and the second determination unit (12).

A system including at least one computerized device with voice command capability processed remotely includes a low power processor, executing a loose algorithmic model to recognize a wake word prefix in a voice command, the loose model having a low false rejection rate but suffering a high false acceptance rate, and a second processor which can operate in at least a low power/low clock rate mode and a high power/high clock rate mode. When the first processor determines the presence of the wake word, it causes the second processor to switch to the high power/high clock rate mode and to execute a tight algorithmic model to verify the presence of the wake word. By using the two processors in this manner, the average overall power required by the computerized device is reduced, as is the amount of waste heat generated by the system.

The air conditioning system comprises: an air conditioner; an inference device to infer data representing a total amount of a power demand value exceeding a set value for a prediction target period from input data including at least one of operation data of the air conditioner for a period prior to the prediction target period, state data of a user of the air conditioner for the period prior to the prediction target period, weather prediction data for the prediction target period, or characteristic data of the room in which the air conditioner is installed; and a control device to cause the air conditioner to perform a heat storage operation depending on a predicted value of the total amount of the power demand value.

Environmental control system diagnostics and optimizations using intelligent lighting networks are provided. One or more intelligent lighting modules (ILMs) can be deployed in intelligent lighting fixtures, intelligent lighting zone controllers, and other intelligent lighting network devices to collect ambient environmental data (e.g., temperature, pressure, and humidity) in addition to occupancy and ambient light sensing used for lighting control. In this manner, embodiments of the present disclosure address diagnostics and improve performance of environmental control systems (e.g., heating ventilation and air conditioning (HVAC) systems) by offering a secondary set of sensors for HVAC systems at a lower cost than traditional approaches. In particular, the ILMs or other processing circuitry in communication with the ILMs analyze the collected ambient environmental data to diagnose the health and function of the environmental control system, and communicate the diagnoses to users and/or the HVAC system.