Patent Application
20140288861
The present disclosure relates to measuring energy consumption. More specifically, the present disclosure relates to measuring energy consumption across a large number of devices.
Energy consumption reporting and control for a power-consuming device, such as a computer, printer, or other item of office equipment, is becoming more interesting to consumers. As electricity becomes more expensive, and consumers strive to become more environmentally conscious, accurate power consumption and modeling is becoming more important. Additionally, many states and utility providers provide rebates or tax credits for using energy efficient devices.
Typically, energy consumption for a large number of devices is estimated using Energy Star® estimates or other broad measures of energy usage measuring. Measuring the energy usage at each individual device grouped into a large collection of devices is often an inefficient use of time and a large expense.
Some companies may manage thousands of devices located at a variety of sites. In such environments, collecting, interpreting, analyzing and reporting energy usage statistics for each individual device is thus impractical, and typical energy usage estimate techniques may be too broad to provide an accurate measurement of energy usage for reporting to state or other utility providers or regulators for potential rebates or tax credits.
In one general respect, the embodiments disclose a method including determining an energy consumption level for each device in a population of power-consuming devices; clustering the population of power-consuming devices into a plurality of clusters such that each of the plurality of clusters has a similar overall energy consumption; determining an estimated total energy consumption for each cluster; determining an estimated total energy consumed by the population of devices based upon the total energy consumption for each cluster, wherein the estimated total energy consumed by the population is within an acceptable uncertainty; and determining a number of samples to measure in each cluster such that the uncertainty associated with the estimated total energy consumed by the population of devices is minimized.
In another general respect, the embodiments disclose device including a processor and a non-transitory computer readable medium operably connected to the processor. The computer readable medium contains a set of instructions configured to instruct the processor to determine an energy consumption level for each device in a population of power-consuming devices; cluster the population of power-consuming devices into a plurality of clusters such that each of the plurality of clusters has a similar overall energy consumption; determine an estimated total energy consumption for each cluster; determine an estimated total energy consumed by the population of devices based upon the total energy consumption for each cluster, wherein the estimated total energy consumed by the population is within an acceptable uncertainty; and determine a number of samples to measure in each cluster such that the uncertainty associated with the estimated total energy consumed by the population of devices is minimized.
In another general respect, the embodiments disclose a method including determining an energy consumption level for each multi-function printing device in a population of multi-function printing devices based upon the power model for an associated multi-function printing device and usage statistics for the associated multi-function printing device; clustering the population of power-consuming devices into a plurality of clusters such that each of the plurality of clusters has a similar overall energy consumption; determining an estimated total energy consumption for each cluster; determining an estimated total energy consumed by the population of multi-function printing devices based upon the total energy consumption for each cluster, wherein the estimated total energy consumed by the population is within an acceptable uncertainty; and determining a number of samples to measure in each cluster such that the uncertainty associated with the estimated total energy consumed by the population of multi-function printing devices is minimized.
This disclosure is not limited to the particular systems, devices and methods described, as these may vary. The terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope.
As used in this document, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. Nothing in this disclosure is to be construed as an admission that the embodiments described in this disclosure are not entitled to antedate such disclosure by virtue of prior invention. As used in this document, the term “comprising” means “including, but not limited to.”
As used herein, a “device” refers to an electronic device that consumes power from one or more power sources and that is configured to perform one or more specific functions. Each device has an associated power model that defines the device's power consumption during certain states as well as the device's power consumption during transitions between certain states.
A “computing device” or “processing device” refers to a device that processes data in order to perform one or more functions. A computing device may include any processor-based device such as, for example, a server, a personal computer, a personal digital assistant, a web-enabled phone, a smart terminal, a dumb terminal and/or other electronic device capable of communicating in a networked environment. A computing device may interpret and execute instructions.
A common and fundamental problem in statistics is to determine a sampling strategy to improve the likelihood of obtaining reasonable estimates of population parameters. Since sampling is often costly, there is a point at which improved accuracy in estimating a population parameter is offset by the economic cost of obtaining information. In such situations, it is important to sample intelligently so as to maximize the information obtained with respect to the cost expended. In a situation in which there is a large population, and there is reason to believe that there are subpopulations that are more or less homogeneous, then a method of stratified sampling can be a sensible approach. Stratified sampling strategies have traditionally relied on approaches that allocate samples in proportion to the ratio of the subpopulation to that of the total population, or allocating samples in proportion to the standard deviations of the subpopulations.
The present disclosure is directed to a method and device for providing an optimal stratified sampling model. The model is used to divide a population of devices and identify subpopulations, or clusters, of devices that are relatively homogeneous. A numerical method is then applied to identify a number of clusters, and the number of samples to obtain from each cluster, so as to minimize the variance of the overall estimated population mean. The model may be used to increase the efficiency of monitoring for companies tasked with accurately estimating and reporting the energy consumption of a large number of devices.
More specifically, as described herein, model based energy consumption data provides an estimate of clusters of “similar” devices, when considering energy consumption. Each cluster may then be sampled to estimate the true cluster means. Based upon the number of measurement devices available for the entire population, the optimal number of measurement devices to include in each cluster may be determined such that the precision of the total energy consumption estimate is maximized. It should be noted that, as used herein, the term mean refers to any mathematical operation in which multiple data inputs are summarized in a smaller number of data outputs.
During clustering, each device may be assigned to a specific cluster based upon that device's associated energy consumption level. In order to provide a high precision estimate of the number of measurement devices to use for each cluster, division of the devices into clusters should occur such that each cluster may have a similar overall energy usage as the other clusters. Similar may mean, for example, no more than a 1% difference in energy consumption, no more than a 3% difference in energy consumption, no more than a 5% difference in energy consumption, or no more than 10% difference in energy consumption. Thus, when creating clusters, a larger number of low energy devices may be clustered together, as shown in cluster 102, while a smaller number of higher energy devices may be clustered together, as is shown in clusters 104 and 106. As a result, each individual cluster 102, 104 and 106, while having varying numbers of individual devices, has a similar overall energy consumption.
In order to accurately measure the power consumption amongst the devices in the population, while still maintaining a high precision and reducing the overall cost, a limited number of measurement devices should be allocated among the clusters to get high quality estimates of mean energy consumption of each cluster.
The processing device may cluster 304 the devices, similar to the description of clustering provided above in regard to
Once the devices are clustered 304, the processing device may estimate the total energy consumed by the entire population. To calculate the exact total energy, the following equation may be used:
E
Total
=N
1η1+N2η2+N3η3+N4η4+N5η5+N6η6
where Ni is the number of devices in cluster i, and ηi is the true average for energy consumption within cluster i. However, since the true average is not known, an average energy per cluster Ēi may be used. Thus, the following equation may be used to estimate the total energy in all clusters:
E
Total
≈N
1
Ē
1
+N
2
Ē
2
+N
3
Ē
3
+N
4
Ē
4
+N
5
Ē
5
+N
6
Ē
6
Additionally, Ēi, being the average of ηi measurement, is more nearly normal (e.g., the larger the number ηi, the better the normal approximation) when calculating cluster deviation.
Depending on the application of the energy estimation being calculated, the processing device may determine 308 acceptable error bounds. Alternatively, a user or administrator may set the level of acceptable error bounds for the estimation calculations. In this example, it is assumed that a normal approximation is adequate and so Ēi is within the true means by
with approximate 68% probability.
It should be noted that, in this example, the actual standard deviation σi of each cluster is unknown since this methodology uses power device models to obtain an initial estimate of power consumption. The methodology therefore uses the model based standard deviation. Thus, the processing device may estimate 310 the total energy, with the 68% error bounds, using the following equation:
For example, in a population where the average is estimated, the true average will be within 1 standard deviation of the estimated average 68% of the time, within two standard deviations 95% of the time, within three standard deviations 99% of the time, and so on.
The processing device may then use the estimated 310 total energy to determine 312 a number of measurement devices K to use in each cluster. It should be noted there may be several constraints used to define K . For example, 0≦K≦Kmax where Kmax is the maximum number of measurement devices allocated for a specific population. Additionally, n1+n2+n3+n4+n5+n6=K where ni is the number of measurement devices allocated for cluster i.
Thus, to determine 312 the number of measurement device to use for each cluster, the task becomes how to allocate n1, n2, n3, n4, n5 and n6 to minimize the uncertainty associated with ETotal such that the overall uncertainty associated with ETotal is within an acceptable uncertainty range. As this is the sum of a set of random variables, the problem statement becomes how to determine the integers, the following equation may be used:
subject to the constraint that n1+n2+n3+n4+n5+n6=K. Thus, referring back to the equation for calculating ETotal, and based upon the values for {circumflex over (σ)}1, {circumflex over (σ)}2, {circumflex over (σ)}3, {circumflex over (σ)}4, {circumflex over (σ)}5 and {circumflex over (σ)}6, one can determine approximately how many measuring device should be allocated to each cluster.
For example, if {circumflex over (σ)}1 is considerably larger than each of the other values, then a larger percentage of measuring devices should be allocated to cluster 1 as compared to the other clusters.
It should be noted that J, the variance of the sum ETotal, holds approximate since the distributions for each cluster are finite. Additionally, it should be noted that, as used herein, the terms minimum and maximum should be construed broadly, with reference to a practical solution that may change over time, and should not be construed to require a mathematically proven minimum or maximum.
For example, to determine the integers n1, n2, n3, n4, n5 and n6, a basic exhaustive but efficient counting search may be conducted. A counting search operates by counting the number of objects that have a distinct key value, and using a specific algorithm on those counts to determine the positions of each key value in the output sequence. Provided only those integers in conformance with the constraints are evaluated, the computational complexity scales efficiently as well (the number of computations is bounded by the square of the arithmetic sum with difference 1). Additionally, starting near values that are proportional to the numerator terms are generally close, though not identical, to the optimal values.
It should be noted that the equations as included herein are shown by way of example only, and additional statistical analysis and estimation techniques may be incorporated as appropriate.
A controller 420 interfaces with one or more optional memory devices 425 to the system bus 400. These memory devices 425 may include, for example, an external or internal DVD drive, a CD ROM drive, a hard drive, flash memory, a USB drive or the like. As indicated previously, these various drives and controllers are optional devices. Additionally, the memory devices 425 may be configured to include individual files for storing any software modules or instructions, auxiliary data, incident data, common files for storing groups of contingency tables and/or regression models, or one or more databases for storing the information as discussed above.
Program instructions, software or interactive modules for performing any of the functional steps associated with the processes as described above may be stored in the ROM 410 and/or the RAM 415. Optionally, the program instructions may be stored on a tangible computer readable medium such as a compact disk, a digital disk, flash memory, a memory card, a USB drive, an optical disc storage medium, such as a Blu-ray™ disc, and/or other recording medium.
An optional display interface 430 may permit information from the bus 400 to be displayed on the display 435 in audio, visual, graphic or alphanumeric format. Communication with external devices may occur using various communication ports 440. A communication port 440 may be attached to a communications network, such as the Internet or a local area network.
The hardware may also include an interface 445 which allows for receipt of data from input devices such as a keyboard 450 or other input device 455 such as a mouse, a joystick, a touch screen, a remote control, a pointing device, a video input device and/or an audio input device.
It will be appreciated that various of the above-disclosed and other features and functions, or alternatives thereof, may be desirably combined into many other different systems or applications or combinations of systems and applications. Also that various presently unforeseen or unanticipated alternatives, modifications, variations or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the following claims.
