Intelligent System of Information Broker under a Data and Energy Storage Internet Infrastructure

• US 20170257450A1
• Filed: 03/01/2016
• Published: 09/07/2017
• Est. Priority Date: 03/01/2016
• Status: Abandoned Application

• Current Assignee: Futurewei Technologies Incorporated (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: Futurewei Technologies Incorporated (Huawei Investment & Holding Co., Ltd.)
• Inventors: Rao, Lei, Li, Jian, Zhu, Jiafeng, Hu, Ron-Chung
• Application Number:
  US15/057,665
• Publication Number:
  US 20170257450A1
• Time in Patent Office: Days
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 13/00004 characterised by the power network being locally controlled
• CPC Class Codes: H02J 13/00028 involving the use of Internet protocols
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2310/12 The local stationary network supplying a household or a building
• CPC Class Codes: H02J 2310/60 Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 40/128 involving the use of Internet protocol
• CPC Class Codes: Y04S 40/18 Network protocols supporting networked applications, e.g. including control of end-device applications over a network
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

A method implemented in a network element (NE) configured to operate as an Ensemble Information Broker (EIB) within a data and energy storage internet architecture, the method comprising collecting device data, human presence data, and human activity data; determining predicted human behaviors for the user; determining a predicted energy metric for the smart system during a future time slot; calibrating weighted objective metrics of an operating status of the devices, a human comfort level, and a human productivity level according to the predicted human behaviors and user defined preference levels defined for the smart system; generating a set of control commands for the devices within the smart system by executing the dynamic human-centric Objective Function on the predicted energy metric; and transmitting, via a transmitter, the set of control commands to corresponding devices within the smart system.

Claims

1. A network element (NE) configured to operate as an Ensemble Information Broker (EIB) within a distributed data and energy storage internet architecture, the NE comprising:

a receiver configured to;

collect device data regarding a flow of energy during a time slot through a plurality of devices within a smart system associated with the NE;

collect a presence of a user within the smart system during the time slot as human presence data; and

collect human activity data regarding user inputs to the devices and user interaction with the devices during the time slot;

a processor coupled to the receiver and configured to;

preform a human behavior analysis of the human activity data and the human presence data to determine temporal human behaviors for the user;

determine a projected energy consumption and a projected energy generation for the devices during a future time slot based on the device data and the temporal human behaviors for the user;

determine a projected energy metric for the smart system during the future time slot based on a difference between the projected energy consumption and the projected energy generation of the smart system during the future time slot;

calibrate weighted objective metrics of an operating status of the devices, a human comfort level, and a human productivity level according to the temporal human behaviors and user defined preference levels defined for the smart system, wherein the weighted objective metrics are included in a dynamic human-centric Objective Function;

execute the dynamic human-centric Objective Function on the projected energy metric to generated a result; and

generate a set of control commands for the future time slot for the devices according to the result; and

a transmitter coupled to the processor and configured to transmit the set of control commands to corresponding devices within the smart system.

2. The NE of claim 1, wherein the dynamic human-centric Objective Function comprises a human-centric multimodal representation of a weighted sum of an energy Cost Function and a user Productivity Function, wherein the energy Cost Function is a representation of an operating status of the devices, and wherein the user Productivity Function is a representation of a human comfort level and a human productivity level within the smart system and comprises a noise function, a cool function, a warm function, a fresh function, and a humidity function each regarding a corresponding operating status of the devices.

3. The NE of claim 1, wherein the control commands are for future configuration updates to the devices.

4. The NE of claim 1, wherein the processor is further configured to generate a request for energy resource when the projected energy metric indicates a shortage of energy for the smart system during the future time slot, and where the transmitter is further configured to transmit the request to an external NE configured to operate as an external EIB in the distributed data and energy storage internet architecture.

5. The NE of claim 1, wherein the processor is further configured to generate a message indicating a surplus of energy when the projected energy metric indicates a surplus of energy for the smart system during the future time slot, and where the transmitter is further configured to transmit the message to an external NE configured to operate as an external EIB in the distributed data and energy storage internet architecture.

6. The NE of claim 1, wherein the processor is further configured to generate an alert when a maximum of a data to energy consumption ratio exceeds a threshold based on an adaptive threshold mechanism.

7. The NE of claim 6, wherein the receiver is further configured to receive a request to update the threshold based on the alert, wherein the processor is further configured to update the threshold to a current maximum of data to energy consumption ratio.

8. The NE of claim 1, wherein the device data includes an amount of energy consumed by the devices during the time slot and an amount of energy generated by the devices during the time slot.

9. The NE of claim 1, wherein the dynamic human-centric Objective Function provides a mechanism for a reduction of energy cost within the smart system and an optimization of a productivity of user devices that interact with the smart system.

10. The NE of claim 1, wherein the weighted objective metrics are updated by weighting the Objective Function according to user preferences received from a user interface coupled to the NE and within the smart system.

11. A computer program product comprising computer executable instructions stored on a non-transitory computer readable medium such that when executed by a processor cause a network element (NE) configured to operate as an Ensemble Information Broker (EIB) within a distributed data and energy storage internet architecture to:

collect device data regarding a flow of energy during a time slot through a plurality of devices within a smart system associated with the NE and data generated by the devices related to the flow of energy during the time slot, human presence data regarding a presence of a user within the smart system during the time slot; and

human activity data regarding user interaction with the devices during the time slot;

determine predicted human behaviors for the user based a human behavior analysis of the human activity data and the human presence data;

update an energy data consumption pattern with the device data, wherein the energy data consumption pattern is an internal model that contains historical data regarding an amount of energy consumed and data generated regarding the energy consumed by the devices;

determine a predicted energy consumption and a predicted energy generation of the smart system during a future time slot through an application of a charge model associated with the devices to the energy data consumption pattern and the predicted human behaviors;

determine a predicted energy metric for the smart system during the future time slot based on a difference between the predicted energy consumption and the predicted energy generation of the smart system during the future time slot;

generate a request for energy resources during the future time slot when the predicted energy metric indicates a shortage of energy for the smart system during the future time slot; and

transmit, via a transmitter, the request to an external NE configured to operate as an external EIB in the distributed data and energy storage internet architecture.

12. The computer program product of claim 11, wherein the charge model is represented as x̂

(t+1)=xt+ut−

qt, wherein xt is an inventory level of an energy storage device at a time t, ut is an amount of energy charged to the energy storage device at the time t, and qt is an amount of energy discharged from the energy storage device at the time t, wherein the energy storage device is included in the plurality of devices within the smart system.

13. The computer program product of claim 11, further comprising sending an alert to a user interface when a maximum value for a data consumption ratio exceeds a threshold, wherein the data consumption ratio is determined based on an Internet providing devices consumption pattern.

14. The computer program product of claim 11, wherein the human presence data comprise information contained in a calendar associated with a user that interacts with the devices within the smart system.

15. The computer program product of claim 11, wherein the human presence data comprise information indicating an estimated number of connections to a Wi-Fi device within the smart system at given time.

16. A method implemented in a network element (NE) configured to operate as an Ensemble Information Broker (EIB) within a data and energy storage internet architecture, the method comprising:

collecting device data regarding a flow of energy during a time slot through a plurality of devices within a smart system associated with the NE, human presence data regarding a presence of a user within the smart system during the time slot, and human activity data regarding user interaction with the devices during the time slot;

determining predicted human behaviors for the user based on a human behavior analysis of the human activity data and the human presence data;

determining a predicted energy metric for the smart system during a future time slot based on a difference between a predicted energy consumption and a predicted energy generation of the smart system during the future time slot determined according to an analysis of the device data and the predicted human behaviors for the user;

calibrating weighted objective metrics of an operating status of the devices, a human comfort level, and a human productivity level according to the predicted human behaviors and user defined preference levels defined for the smart system, wherein the weighted objective metrics are included in a dynamic human-centric Objective Function, and wherein the dynamic human-centric Objective Function comprises a human-centric multimodal representation of a Cost Function and a Productivity Function weighted according to the weighted objective metrics;

generating a set of control commands for the devices within the smart system by executing the dynamic human-centric Objective Function on the predicted energy metric; and

transmitting, via a transmitter, the set of control commands to corresponding devices within the smart system.

17. The method of claim 16, wherein the Cost Function is a representation of an operating status of the devices, and wherein the weighted Productivity Function is a representation of a human comfort level and a human productivity level within the smart system.

18. The method of claim 17, wherein the dynamic human-centric Objective Function provides a mechanism for a reduction of energy cost within the smart system and an optimization of a productivity of user devices that interact with the smart system.

19. The method of claim 16, further comprising sending a charge request to external NEs configured to operate as EIBs within the data and energy storage internet architecture when the predicted energy metric indicates a need for energy for the smart system.

20. The method of claim 16, further comprising sending a discharge request to external NEs configured to operate as EIBs within the data and energy storage internet architecture when the predicted energy metric indicates a surplus of energy for the smart system.