Monitoring system

• US 10,107,657 B2
• Filed: 10/05/2015
• Issued: 10/23/2018
• Est. Priority Date: 04/26/2013
• Status: Active Grant

• Current Assignee: Remoni APS
• Original Assignee: Remoni APS
• Inventors: Madsen, Bo Eskerod
• Primary Examiner(s): Diallo, Mamadou
• Application Number:
  US14/874,781
• Publication Number:
  US 20160025536A1
• Time in Patent Office: 1,114 Days
• CPC Class Codes: G01D 2204/12 Determination or prediction of behaviour, e.g. likely power consumption or unusual usage patterns
• CPC Class Codes: G01D 2204/14 Displaying of utility usage with respect to time, e.g. for monitoring evolution of usage or with respect to weather conditions
• CPC Class Codes: G01D 4/02 Details
• CPC Class Codes: G01F 1/56 by using electric or magnetic effects G01F1/66 takes precedence
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• 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: Y04S 20/30 Smart metering, e.g. specially adapted for remote reading

Abstract

A system (2) for monitoring resource flows at a number of devices (D1, D2, D3) includes a receiving unit (4) which receives data from a number of sensors (S1, S2, S3, S4, S5) configured to detect the flow rate and/or the change in the flow rate at device level, and a number of meters (M1, M2, M3) configured to measure the flow for at least a part of the devices (D1, D2, D3). The system (2) has a calculation module (8) configured to receive information from the sensors (S1, S2, S3, S4, S5) and the meters (M1, M2, M3), and the calculation module (8) includes a mathematical statistical model (38) configured to estimate and/or predict flow of resource and/or performance (e.g., activity) of at least a selection of the devices (D1, D2, D3).

Claims

1. A system useful for monitoring resource flows at a number of devices characterized by a latent stochastic process, the system comprising:

at least one sensor selected from the group consisting of piezoelectric sensors, induction sensors, temperature sensors, and lux sensors, configured to detect a flow rate and/or a change in a flow rate at said devices, wherein said at least one sensor is attached non-intrusively to said devices, and wherein said at least one sensor is configured to wirelessly communicate data;

a receiving unit configured to wirelessly receive said data from said at least one sensor;

at least one meter configured to measure an overall flow for at least a part of the devices; and

a calculation module configured to receive information from the at least one sensor and the at least one meter, wherein the calculation module is in communication said at least one sensor;

wherein said calculation module comprises a mathematical statistical model, wherein the mathematical statistical model is configured to estimate and/or predict flow of resources and/or performance of at least a selection of the devices based on data from the at least one sensor and from the at least one meter;

wherein the calculation module is further configured to calibrate said at least one sensor based on said mathematical statistical model operating on said information from said at least one sensor and said at least one meter;

wherein the data from the at least one sensor and from the at least one meter at time t are given as a vector (Y_t), wherein the latent stochastic process at time t is a vector (θ

_t), wherein the mathematical statistic model uses a noise vector (ε

_t) of the data from the at least one sensor and from the at least one meter at time t and a noise vector (ϑ

_t) of the latent stochastic process at time t, and wherein a statistical distribution of the noise vectors (ε

_t, ϑ

_t) is estimated based on the data from the at least one sensor and from the at least one meter.

2. A system according to claim 1, further comprising:

a user interface configured and arranged to provide access to the estimated and/or predicted resource flows of at least a selection of the devices.

3. A system according to claim 1, further comprising:

at least one router;

wherein the at least one router comprises the calculation module.

4. A system according to claim 1, wherein at least one of the at least one sensor comprises an energy harvester.

5. A system according to claim 4, wherein:

the at least one sensor is configured to use energy harvested by the energy harvester to detect resource flow;

or

the system is configured to use energy harvested by the energy harvester to change resource flow rate of at least one of the devices.

6. A system according to claim 1, wherein the calculation module and the mathematical statistic model are integrated in an apparatus of the system.

7. A system according to claim 1, wherein the calculation module is a cloud service comprising a mathematical statistic model and a data storage.

8. A system according to claim 1, further comprising:

a control unit configured to change the activity of one or more of the devices.

9. A system according to claim 1, wherein the calculation module is configured to estimate the actual and/or future activity of at least a selection of the devices.

10. A system according to claim 2, wherein the user interface is a remote user interface.

11. A system according to claim 1, wherein:

vector Y_t is given by

Y_t=f_t(θ

_t)+ε

_t;

vector θ

_t is modelled by an underlying process

θ

_t=g_t(θ

_t-1)+ϑ

_t; and

f_t and g_t are mathematical functions.

12. A system according to claim 1, wherein the at least one meter is configured to measure a total power flow for a group of electrical devices, or to measure a total water flow, or to measure energy consumption.

13. A method for monitoring resource flows at a number of devices with a receiving unit which wirelessly receives data from a number of sensors selected from the group consisting of piezoelectric sensors, induction sensors, temperature sensors, and lux sensors, wherein said number of sensors are attached non-intrusively to said devices, wherein said number of sensors are configured to wirelessly communicate data to said receiving unit, wherein said number of sensors are configured to detect the flow rate and/or the change in the flow rate at device level, and a number of meters configured to measure the flow for at least a part of the devices characterized by a latent stochastic process, the method comprising:

estimating and/or predicting loss and/or flow of resources of at least a selection of the devices by using a calculation module configured to receive information from the sensors and the meters, wherein the calculation module is in communication with and configured to calibrate said number of sensors by applying a mathematical statistical model, whereby the mathematical statistical model is configured to estimate and/or predict flow of resources and/or performance of at least a selection of said number of devices based on data from said sensors and said meters;

wherein data from said sensors and from said meters at time t are given as a vector (Y_t), wherein the latent stochastic process at time t is a vector (θ

_t), wherein the mathematical statistic model uses a noise vector (ε

_t) of data from the sensors and from the meters at time t and a noise vector (ϑ

_t) of the latent stochastic process at time t, and wherein a statistical distribution of the noise vectors (ε

_t, ϑ

_t) is estimated on the basis of the data from the sensors and from the meters; and

calibrating said sensors based on said mathematical statistical model operating on said data from the sensors and from the meters in said calculation module.

14. A method according to claim 13, wherein the resource flows at the number of devices is characterized by a latent stochastic process, and wherein said estimating and/or predicting is performed with a system comprising:

at least one sensor configured to detect a flow rate and/or a change in a flow rate at said devices;

a receiving unit configured to receive data from said at least one sensor;

at least one meter configured to measure an overall flow for at least a part of the devices; and

a calculation module configured to receive information from the at least one sensor and the at least one meter, wherein the calculation module comprises a mathematical statistical model configured to estimate and/or predict flow of resources and/or performance of at least a selection of the devices;

wherein the data from the at least one sensor and from the at least one meter at time t are given as a vector (Y_t), wherein the latent stochastic process at time t is a vector (θ

_t), wherein the mathematical statistic model uses a noise vector (ε

_t) of the data from the at least one sensor and from the at least one meter at time t and a noise vector (ϑ

_t) of the latent stochastic process at time t, and wherein a statistical distribution of the noise vectors (ε

_t, ϑ

_t) is estimated based on the data from the at least one sensor and from the at least one meter.

15. A method according to claim 13, further comprising:

controlling the activity of one or more of the devices based on said estimating and/or predicting.

16. A method according to claim 14, wherein:

vector Y_t is given by

Y_t=f_t(θ

_t)+ε

_t;

vector θ

_t is modelled by an underlying process

θ

_t=g_t(θ

_t-1)+ϑ

_t; and

f_t and g_t are mathematical functions.