A space controller includes a housing, a sensor, a communications interface, a camera lens, a camera, a display, and a processing circuit. The sensor and the communications interface are coupled to the housing. The sensor is configured to generate sensor data that is indicative of a condition of an environment surrounding the housing. The communications interface is configured to communicate with a controlled device. The camera and the processing circuit are positioned within the housing. In particular, the camera is aligned with the camera lens. The display is configured to display images received from the camera and to receive user input. The processing circuit is configured to identify the environmental condition based on the sensor data and to operate the controlled device based on the environmental condition and the user input.

A ceiling-mounted sensing unit includes (i) one or more air temperature sensors; (ii) an infrared sensor having a field of view oriented towards a floor of the room; and (iii) a microcontroller receiving readings from both the air temperature sensors and the infrared sensor, the microcontroller providing an estimated temperature at a predetermined distance above the floor of the room based on a model of the room. The model may be based on a double-exponential smoothing function obtained by matching a Kalman filter model. Alternately, the model may be itself a Kalman filter model or a machine learning trained linear model obtained using a linear regression technique, such as L2 regularization. The Kalman filter model uses a state vector that includes both the estimated temperature and a rate of change in the estimated change in temperature. The machine-trained model may be verified using a k-fold cross-validation technique.

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).

An air purifier includes a main body having an inlet and a discharge device mounted on the main body and configured to receive air introduced into the main body through the inlet and discharge the air from the air purifier, wherein the discharge device includes a discharge opening, an opening and closing member including a portion having a size and shape corresponding to the discharge opening, and configured to move out of the discharge opening and into the discharge opening to open and close the discharge opening, and a discharge portion including a plurality of discharge holes, wherein each discharge hole of the plurality of discharge holes has a size smaller than a size of the discharge opening, and wherein the discharge portion has an air discharging area that is variable.

An environmental control system for controlling a personal environment at habitable space, the system including: a user occupied comfort subsystem having at least one of a user component, an illumination subsystem, and a microclimate subsystem, wherein the microclimate subsystem provides at least one of heating, cooling and ventilation to the personal environment; a sensory experience subsystem in network communication with the comfort subsystem; a controller in network communication with each of the comfort subsystem and the sensory experience subsystem, the controller configured to monitor and control at least one environmental parameter of the environmental control system.

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.

Disclosed is a load-predicting and control system for a subway heating, ventilation and air conditioning system. In one aspect, a load-predicting and control system for a subway heating, ventilation and air conditioning system is provided. The system includes a basic database, a sensing system, a load predicting unit, and a controller; the basic database stores historical data; the sensing system provides measured data; the load predicting unit calculates a predicted load value of the subway heating, ventilation and air conditioning based on the historical data and the measured data, and transmits the predicted load value to the controller; the controller issues a control command based on the predicted load value. Also provided is a load-predicting and control method for the subway heating, ventilation and air conditioning system. The present disclosure solves problems such as poor accuracy of conventional load prediction and inadequate control of the air conditioning system.

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.

Computing device and method using a neural network to adjust temperature measurements. The computing device comprises a temperature sensing module, one or more processor and a display. The neural network receives as inputs a plurality of consecutive temperature measurements performed by the temperature sensing module, a plurality of consecutive utilization metrics of the one or more processor, and a plurality of consecutive utilization metrics of the display. The neural network outputs an inferred temperature, which is an adjustment of the temperature measured by the temperature sensing module to take into consideration heat dissipated by the one or more processor and the display when using the temperature sensing module for measuring the temperature in an area where the computing device is deployed. An example of computing device is a smart thermostat. A corresponding method for training a neural network to adjust temperature measurements is also disclosed.

A method and system for creating and maintaining a profile for a smart building comprising is disclosed. A method includes detecting a presence of a user at the smart building; monitoring actions of the user in the smart building with respect to each of a plurality of aspects; receiving feedback via a human machine interface from the user regarding one or more of the plurality of aspects; and building the profile for the user based on the actions and feedback; wherein: monitoring actions comprises using one or more sensors to detect movement of the user through the smart building; receiving feedback includes determining a satisfaction level of the user with respect to thermal comfort; and adjusting the one or more of the plurality of aspects based on the profile.