A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.

A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.

A system for occupancy detection and prediction is disclosed. The system may include a modular control unit configured to control power to at least one load device, which may include a device control assembly configured to be removably coupled to a backplate. The device control assembly may include an occupancy sensor configured to detect a current occupancy state of a detection zone. The device control assembly may be configured to determine occupancy routines based on historical occupancy states of the detection zone and to predict future occupancy states. The device control assembly may be configured to control the power to the at least one load device based on the predicted future occupancy states.

An automation operating and management system consolidates and analyzes inputs from multiple machines within an automated enterprise to predict failures and provide instructions for counteractions to prevent failures during machine operation, and to identify opportunities for efficiency improvement, including actions for reduction in peak power consumption demand within a facility including multiple machines. A machine can include a machine controller and at least one base layer controller, where the base layer controller acts as a low level controller to directly control the motion of elements in communication with the base layer control, according to parameters set by the machine controller. The base layer controller collects timing data for the elements under its control, compares the timing data with the parameters and sets an alarm when the timing data is outside of tolerance limits defined by the parameters.

An automation operating and management system consolidates and analyzes inputs from multiple machines within an automated enterprise to predict failures and provide instructions for counteractions to prevent failures during machine operation, and to identify opportunities for efficiency improvement, including actions for reduction in peak power consumption demand within a facility including multiple machines. A machine can include a machine controller and at least one base layer controller, where the base layer controller acts as a low level controller to directly control the motion of elements in communication with the base layer control, according to parameters set by the machine controller. The base layer controller collects timing data for the elements under its control, compares the timing data with the parameters and sets an alarm when the timing data is outside of tolerance limits defined by the parameters.

A method of monitoring a subject via a photoplethysmography (PPG) sensor configured to detect and/or measure PPG information from the subject includes changing, via a processor, signal analysis frequency of the PPG sensor signals, optical wavelength emission of the PPG sensor, and/or PPG sensor interrogation power at predetermined times. Each predetermined time is associated with measuring at least one different biometric parameter from a plurality of biometric parameters.

A method of monitoring a subject via a photoplethysmography (PPG) sensor configured to detect and/or measure PPG information from the subject includes changing, via a processor, signal analysis frequency of the PPG sensor signals, optical wavelength emission of the PPG sensor, and/or PPG sensor interrogation power at predetermined times. Each predetermined time is associated with measuring at least one different biometric parameter from a plurality of biometric parameters.

An autonomous cleaning robot including a drive configured to move the cleaning robot across a floor surface in an area to be cleaned and a controller. The controller is configured to receive data representing an editable mission timeline including data representing a sequence of rooms to be cleaned, navigate the cleaning robot to clean the rooms following the sequence, track operational events occurring in each of the rooms, and transmit data about time spent navigating each room included in the sequence.

A mobile panel maintenance system including a mobile panel maintenance unit having a base supported for translational motion over a surface within a panel array and a carriage movably mounted to the base to position a panel maintenance assembly in relation to a panel surface for panel maintenance.

A mobile panel maintenance system including a mobile panel maintenance unit having a base supported for translational motion over a surface within a panel array and a carriage movably mounted to the base to position a panel maintenance assembly in relation to a panel surface for panel maintenance.