Consumption breakdown monitoring through power state sensing
Systems and methods are provided for estimating power breakdowns for a set of one or more appliances inside a building by exploiting a small number of power meters and data indicative of binary power states of individual appliances of such set. In one aspect, a breakdown estimation problem is solved within a tree configuration, and utilizing a single power meter and data indicative of binary power states of a plurality of appliances. Based at least in part on such solution, an estimation quality metric is derived. In another aspect, such metric can be exploited in a methodology for optimally placing additional power meters to increase the estimation certainty for individual appliances to a desired or intended level. Estimated power breakdown and energy breakdown—individually or collectively referred to as consumption breakdown—rely on measurements and numerical simulations, and can be evaluated in exemplary electrical network utilizing binary sensors.
1. A method, comprising:
(i) collecting, at a predetermined sampling interval, data indicative of power consumption of at least one appliance of a plurality of appliances from at least one power meter, wherein a number of power meters of the at least one power meter is less than a number of outlets of a plurality of outlets coupled to the plurality of appliances;
(ii) collecting, over a first time interval, data indicative of a time-stamped set of binary power states of a plurality of appliances, wherein a binary power state is an ON power state or an OFF power state; and
(iii) estimating an energy consumption of at least one appliance of the plurality of appliances over the first time interval.
2. The method of claim 1 , further comprising repeating steps (i) through (iii) during a second time interval greater than the first time interval.
3. The method of claim 2 , wherein the estimating step comprises computing a first estimate of an average power consumption of the at least one appliance based on cumulative data collected over the second time interval.
4. The method of claim 3 , wherein the estimating step further comprises computing a second estimate of the average power consumption of the at least one appliance based on current data collected over the first time interval.
5. The method of claim 4 , wherein the estimating step further comprises selecting one of the first estimate or the second estimate based at least on an outcome of computing a mean square error for each appliance of the plurality of appliances for the first metric or the second metric.
6. The method of claim 5 , wherein the estimating step further comprises estimating the energy consumption of the at least one appliance by multiplying a magnitude of a third time interval spanning the duration of an ON power state of the at least one appliance and one of the first estimate for the at least one appliance or the second estimate for the at least one appliance.
7. The method of claim 1 , wherein the estimating step comprises estimating the energy consumption of each appliance of the plurality of appliances.
8. The method of claim 1 , wherein the power consumption of the at least one appliance of the plurality of appliances is a total power consumption of the plurality of power appliances, and the at least one meter comprises a single power meter.
9. The method of claim 1 , wherein the estimating step comprises solving a weighted linear optimization problem within a three-tier tree structure representation of the plurality of appliances and the plurality of outlets coupled to the plurality of appliances.
10. The method of claim 9 , wherein the estimating step further comprises generating a set of one or more weights, each weight of the set of one or more weights being equal to a number of state samples obtained in the predetermined time interval divided by a weighted sum of a plurality of variances of active power for respective appliances of the plurality of appliances.
11. The method of claim 1 , further comprising determining an outlet of the plurality of outlets coupled to the plurality of appliances to incorporate a power meter into in response to an estimation performance metric being less than a predetermined value.
12. The method of claim 11 , wherein the determining step comprises minimizing a cost function that quantifies a trade-off between estimation accuracy and number deployed power meters.
13. A system, comprising:
a memory comprising data and computer-executable instructions; and
a processor functionally coupled to the memory and configured by the computer-executable instructions to perform steps comprising:
(a) collecting, at a predetermined sampling interval, data indicative of power consumption of at least one appliance of a plurality of appliances from at least one power meter, wherein a number of power meters of the at least one power meter is less than a number of outlets of a plurality of outlets coupled to the plurality of appliances;
(b) collecting, over a first time interval, data indicative of a time-stamped set of binary power states of a plurality of appliances, wherein a binary power state is an ON power state or an OFF power state; and
(c) estimating an energy consumption of at least one appliance of the plurality of appliances over the first time interval.
14. The system of claim 13 , the processor is further configured to perform the step of determining an outlet of the plurality of outlets coupled to the plurality of appliances to incorporate a power meter into in response to an estimation performance metric being less than a predetermined value.
15. The system of claim 14 , wherein the processor is further configured to perform the step of determining the outlet of the plurality of outlets the processor is configured to minimize a cost function that quantifies a trade-off between estimation accuracy and number deployed power meters.
16. The system of claim 13 , wherein the processor is further configured to perform the step of repeating steps (a) through (c) during a second time interval greater than the first time interval.
17. The system of claim 16 , wherein the processor is further configured to perform the step of computing a first estimate of an average power consumption of the at least one appliance based on cumulative data collected over the second time interval.
18. The system of claim 17 , wherein the processor is further configured to perform the step of computing a second estimate of the average power consumption of the at least one appliance based on current data collected over the first time interval.
19. The system of claim 18 , wherein the processor is further configured to perform the step of selecting one of the first estimate or the second estimate based at least on an outcome of computation of a mean square error for each appliance of the plurality of appliances for the first metric or the second metric.
20. The system of claim 19 , wherein the processor is further configured to perform the step of estimating the energy consumption of the at least one appliance by multiplying a magnitude of a third time interval spanning the duration of an ON power state of the at least one appliance and one of the first estimate for the at least one appliance or the second estimate for the at least one appliance.
21. The system of claim 13 , wherein the processor is further configured to estimate the energy consumption of each appliance of the plurality of appliances.
22. The system of claim 21 , wherein the processor is further configured to perform the step of generating a set of one or more weights, each weight of the set of one or more weights being equal to a number of state samples obtained in the predetermined time interval divided by a weighted sum of a plurality of variances of active power for respective appliances of the plurality of appliances.
23. The system of claim 13 , wherein the power consumption of the at least one appliance of the plurality of appliances is a total power consumption of the plurality of power appliances, and the at least one meter comprises a single power meter.
24. The system of claim 13 , wherein the processor is further configured to perform the step of solving a weighted linear optimization problem within a three-tier representation of the plurality of appliances and the plurality of outlets coupled thereto.
25. A computer-readable non-transitory medium, comprising:
a first group of computer-executable instructions that, in response to execution, cause a processor to collect, at a predetermined sampling interval, data indicative of power consumption of at least one appliance of a plurality of appliances from at least one power meter, wherein a number of power meters of the at least one power meter is less than a number of outlets of a plurality of outlets coupled to the plurality of appliances;
a second group of computer-executable instructions that, in response to execution, cause a processor to collect, over a predetermined time interval, data indicative of a time-stamped set of binary power states of a plurality of appliances, wherein a binary power state is an ON power state or an OFF power state; and
a third group of computer-executable instructions that, in response to execution, cause the processor to estimate an average energy consumption of at least one appliance of the plurality of appliances over the predetermined time interval.
26. The computer-readable non-transitory medium of claim 25 , further comprising a fourth group of computer-executable instructions that, in response to execution, cause a processor to reiterate execution of the first group of computer-executable instructions, the second group of computer-executable instructions, and the third group of computer-executable instructions.