G06F1/28

Electrical meter for training a mathematical model for a device using a smart plug

An electrical panel or an electrical meter may provide improved functionality by interacting with a smart plug. A smart plug may provide a smart-plug power monitoring signal that includes information about power consumption of devices connected to the smart plug. The smart-plug power monitoring signal may be used in conjunction with power monitoring signals from the electrical mains of the building for providing information about the operation of devices in the building. For example, the power monitoring signals may be used to (i) determine the main of the house that provides power to the smart plug, (ii) identify devices receiving power from the smart plug, (iii) improve the accuracy of identifying device state changes, and (iv) train mathematical models for identifying devices and device state changes.

Bi-directional power over ethernet for digital building applications

In one or more embodiments, a system includes a plurality of network devices comprising a plurality of ports, a power bus connecting the network devices, wherein power is shared between the network devices over the power bus, and a controller for identifying available power and allocating power to the ports. The ports include a plurality of PSE (Power Sourcing Equipment) PoE (Power over Ethernet) ports each operable to transmit power to a device connected to one of the PSE PoE ports, a plurality of PD (Powered Device) PoE ports each operable to receive power from a device connected to one of the PD PoE ports, and a plurality of bi-directional PoE ports each configurable to operate as a PSE PoE port to transmit power to a device connected to one of the bi-directional PoE ports or as a PD PoE port to receive power from the connected device.

Bi-directional power over ethernet for digital building applications

In one or more embodiments, a system includes a plurality of network devices comprising a plurality of ports, a power bus connecting the network devices, wherein power is shared between the network devices over the power bus, and a controller for identifying available power and allocating power to the ports. The ports include a plurality of PSE (Power Sourcing Equipment) PoE (Power over Ethernet) ports each operable to transmit power to a device connected to one of the PSE PoE ports, a plurality of PD (Powered Device) PoE ports each operable to receive power from a device connected to one of the PD PoE ports, and a plurality of bi-directional PoE ports each configurable to operate as a PSE PoE port to transmit power to a device connected to one of the bi-directional PoE ports or as a PD PoE port to receive power from the connected device.

Methods and systems for energy or resource management of a human-machine interface

A computer implemented method for energy or resource management of a human-machine interface comprises the following steps carried out by computer hardware components of the human-machine interface: determining a level of attention of a user of the human-machine interface to the human-machine interface; and setting an energy and/or resource utilization related setting of the human-machine interface based on the determined level of attention.

Methods and systems for energy or resource management of a human-machine interface

A computer implemented method for energy or resource management of a human-machine interface comprises the following steps carried out by computer hardware components of the human-machine interface: determining a level of attention of a user of the human-machine interface to the human-machine interface; and setting an energy and/or resource utilization related setting of the human-machine interface based on the determined level of attention.

Machine-learning based optimization of data center designs and risks

In exemplary aspects of optimizing data centers, historical data corresponding to a data center is collected. The data center includes a plurality of systems. A data center representation is generated. The data center representation can be one or more of a schematic and a collection of data from among the historical data. The data center representation is encoded into a neural network model. The neural network model is trained using at least a portion of the historical data. The trained model is deployed using a first set of inputs, causing the model to generate one or more output values for managing or optimizing the data center with respect to design and risk aspects.

Machine-learning based optimization of data center designs and risks

In exemplary aspects of optimizing data centers, historical data corresponding to a data center is collected. The data center includes a plurality of systems. A data center representation is generated. The data center representation can be one or more of a schematic and a collection of data from among the historical data. The data center representation is encoded into a neural network model. The neural network model is trained using at least a portion of the historical data. The trained model is deployed using a first set of inputs, causing the model to generate one or more output values for managing or optimizing the data center with respect to design and risk aspects.

Method and system for predicting resource reallocation in a power zone group

A method for managing data includes obtaining, by a first data node, a notification, wherein the first data node is associated with a first power zone group (PZG), and in response to the notification: selecting a second data node, wherein the second data node is not associated with the first PZG, sending a data processing request to the second data node, obtaining a response based on the data processing request, wherein the response specifies a confirmation by the second data node to service the data processing request, storing a ledger entry in a ledger service that indicates the confirmation, and initiating a data transfer based on the data processing request, wherein the first data node is associated with the PZG based on a primary power source of the first data node.

Systems and methods for storing FSM state data for a power control system

A system and method for logging state data from a power system control device on a computer system is disclosed. The computer system includes a power system supplying power to the computer system. The power system has a power-up sequence having a plurality of stages. The power system control device is coupled to the power system. The power system control device includes a finite state machine circuit having states corresponding to the stages of the power-up sequence. The control device also has a write controller, a storage buffer, and a communication interface. The write controller writes the state of the finite state machine circuit in the storage buffer. An external controller is coupled to the communication interface and is operable to read the stored state data.

Systems and methods for storing FSM state data for a power control system

A system and method for logging state data from a power system control device on a computer system is disclosed. The computer system includes a power system supplying power to the computer system. The power system has a power-up sequence having a plurality of stages. The power system control device is coupled to the power system. The power system control device includes a finite state machine circuit having states corresponding to the stages of the power-up sequence. The control device also has a write controller, a storage buffer, and a communication interface. The write controller writes the state of the finite state machine circuit in the storage buffer. An external controller is coupled to the communication interface and is operable to read the stored state data.