A control system for identifying and responding to effective system changes in a monitored system includes data collection, change identification, classifier construction, effective system change and parameter adjustment modules. The data collection module tracks parameters of the monitored system. The change identification module, based on the parameters, identifies: during training, performance changes and first system changes; and post training, a set of performance changes and second system changes. The classifier construction module, based on the performance changes and the first system changes construct a performance-system change classifier module. The performance-system change classifier module, based on the set of performance changes, determines possible system changes and respective probability values. The effective system change module, based on the second and possible system changes, determines effective system changes and respective probability values. The parameter adjustment module controls an actuator of the monitored system based on the effective system changes and the probability values.

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 foot presence sensor system for an active article of footwear can include a sensor housing configured to be disposed at or in an insole of the article, and a controller circuit, disposed within the sensor housing, configured to trigger one or more automated functions of the footwear based on a foot presence indication. In an example, the sensor system includes a capacitive or magnetic sensor configured to sense changes in a body's proximity to the sensor in footwear. Characteristics of the sensed proximity can be used to update an automated footwear function, such as an automatic lacing function, or can be used to determine a step count, foot strike force, a rate of travel, or other information about a foot or about the footwear.

A foot presence sensor system for an active article of footwear can include a sensor housing configured to be disposed at or in an insole of the article, and a controller circuit, disposed within the sensor housing, configured to trigger one or more automated functions of the footwear based on a foot presence indication. In an example, the sensor system includes a capacitive or magnetic sensor configured to sense changes in a body's proximity to the sensor in footwear. Characteristics of the sensed proximity can be used to update an automated footwear function, such as an automatic lacing function, or can be used to determine a step count, foot strike force, a rate of travel, or other information about a foot or about the footwear.

Creating and executing flow plans by performing at least the following: obtaining a run-time flow plan that comprises a trigger, a first operation, and a second operation, wherein the first operation precedes the second operation within the run-time flow plan and one or more input values of the second operation are linked to the first operation, determining whether one or more conditions of the trigger are met, execute the first operation based at least on the determination that the one or more conditions of the trigger are met, monitoring whether the second operation is ready for execution based at least on a determination that the one or more input values of a second action operation are ready, and executing the second action operation when the second action operation has been identified as ready for execution.

Creating and executing flow plans by performing at least the following: obtaining a run-time flow plan that comprises a trigger, a first operation, and a second operation, wherein the first operation precedes the second operation within the run-time flow plan and one or more input values of the second operation are linked to the first operation, determining whether one or more conditions of the trigger are met, execute the first operation based at least on the determination that the one or more conditions of the trigger are met, monitoring whether the second operation is ready for execution based at least on a determination that the one or more input values of a second action operation are ready, and executing the second action operation when the second action operation has been identified as ready for execution.

An illustrative HVAC controller may include a communication module for wirelessly communicating with a network and wiring terminals for receiving a wired connection to a remote temperature sensor that is situated remote from the HVAC controller and in a living space of the building. A controller may be operably coupled to the communication module and the wiring terminals and may be configured to implement a thermostat control algorithm to generate one or more control signals, wherein the one or more control signals are provided by a wired connection to the HVAC system to control one or more HVAC components of the HVAC system. The thermostat control algorithm may be configured to compare a sensed temperature received from the remote temperature sensor via the wiring terminals and a temperature setpoint received from the server via the network.

A monitoring device configured to be attached to a subject includes a sensor configured to detect and/or measure physiological information and a processor coupled to the sensor. The sensor includes at least one optical emitter and at least one optical detector. The processor receives and analyzes signals produced by the sensor, and the processor changes wavelength of light emitted by the at least one optical emitter in response to detecting a change in subject activity. For example, the processor instructs the at least one optical emitter to emit shorter wavelength light in response to detecting an increase in subject activity, and the processor instructs the at least one optical emitter to emit longer wavelength light in response to detecting an decrease in subject activity. Detecting a change in subject activity may include detecting a change in at least one subject vital sign and/or subject motion.

A monitoring device configured to be attached to a subject includes a sensor configured to detect and/or measure physiological information and a processor coupled to the sensor. The sensor includes at least one optical emitter and at least one optical detector. The processor receives and analyzes signals produced by the sensor, and the processor changes wavelength of light emitted by the at least one optical emitter in response to detecting a change in subject activity. For example, the processor instructs the at least one optical emitter to emit shorter wavelength light in response to detecting an increase in subject activity, and the processor instructs the at least one optical emitter to emit longer wavelength light in response to detecting an decrease in subject activity. Detecting a change in subject activity may include detecting a change in at least one subject vital sign and/or subject motion.