1. Technical Field
This application relates to monitoring utility consumption and more specifically to a system that monitors individual and aggregate consumption.
2. Related Art
Energy efficiency provides a method of reducing CO2 emissions. In some methods, residential and commercial customers engage in energy efficiency efforts such as retrofitting buildings, changing incandescent bulbs to compact fluorescents, and replacing old appliances with more energy efficient replacements to conserve resources. Despite their efforts, and the efforts of others, curtailing residential and commercial energy use is still a challenge.
Today, many utility customers receive little detailed information about energy use. Some utilities provide monthly bills consisting of a total energy use and a summary of expenses. Some indicate the total energy used for the previous few months. Some utility bills provide no information about the relationship between energy consumption and weather or the age and the size of a building.
FIG 12 is an interactive graphical user interfaces for selecting and rendering a geographic area.
A publicly and privately accessible analytic system combines a visualization service and visual communication medium with a geographic mapping tool to provide a novel energy usage feedback interface for users that may include consumers and utility analysis. The system comprises a bi-level decision support system that may visualize, compare, and analyze energy and utility usage at a household level without invading the privacy of other users. It provides access to historical energy usage data and provides opportunities tor users to visually assess the correlation patterns between weather patterns and the user's energy consumption. Some systems compare the consumption of individual users to their peer group. A peer group may be formed through an automated clustering of one or many combinations of physical locations, dwelling sizes, construction characteristics, dwelling ages and/or occupancy levels or patterns. The system may render building envelope data that correlates energy or resource consumption to one or more characteristics such as the age and size of a dwelling and render visualization “heat maps” that may provide a global or regional assessment of consumption patterns over a predetermined or programmable area and period.
The publicly and privately accessible analytic system 100 shown in
In some systems, a front-end cluster (or server) 112 acquires real-time or periodic (e.g., hourly, weekly, monthly, etc.) consumption and billing data from one or more utility database machines/servers 108 at 202 as shown in
A geocoding server or service 114 converts the addresses that are included in the standard output data into geographic coordinates like a longitude and latitude that are then stored in a shared storage device or populate a shared database. On-line property assessors' data, parcel data, and other Geographic Information Systems (GIS) data are matched and geocoded by the geocoding server or service at 208. Matching geographic coordinates associated with consumption, billing, property assessor data, GIS data and/or real-time or historical weather data are joined or are associated in spatial relationships to one another via a record to render a geocoded dataset at 210. In some systems the geocoding server or service 114 may translate the geographic coordinates of the geocoded dataset into a geographical physical address, such as a street address for example, through a reverse geocoding. At this state, some alternative systems may associate or join the notes, sensitive information, and/or other data previously stripped from the original data to supplement the spatial relationships or spatial datasets. A visualization service may then render and transmit the spatial relationships, spatial datasets and/or processed information to graphical user interfaces or application interfaces at 212 within a local or remote client 122 mobile client 122, or smart meter or smart thermostat 124. The information may comprise an on-line mapping that may render information (masking sensitive information), notes, and/or other data based on the security level approved for the user.
In some systems the front-end cluster 112 provides two portals. A public portal may provide access to individual users such as providing access to utility customers. A private portal or utility portal may provide access to commercial users such as utility analysts. Some public portals restrict information access to information belonging to the individual users without exposing the identity of others, while the utility portal may provide access to commercial users such as utility planners and analysts that may require access to all of the data that comprises the geocoded and spatial datasets. Commercial access may provide additional insights about users, and allow the commercial user to understand resource allocation and use without restriction.
As described, the publicly and privately accessible and analytic system 100 may provide aggregate or granular geocoded datasets and establish spatial relationships through many mediums and smart devices including tablets, desktop computers, smart phones, portable devices, smart energy meters and/or other machines that access Web resources. The publicly and privately accessible analytic system 100 may provide access to energy usage data at periodic intervals (for example, using hourly data, using daily data, using monthly data, etc.). Smart meters and smart thermostats 124 may further enhance some alternative systems by making energy usage data available to users as energy is consumed (e.g., in real-time) and in some instances may allow direct feedback automatically or an adjustment, in which a meter or thermostat may self-adjust based on the data accessed from the publicly and privately accessible analytic system 100 and may also self-adjust based on information the smart meter 124 or smart thermostat 124 learns from a user's adjustment or the smart meter's or smart thermostat's own sensors (e.g., proximity sensors near and far that may detect is a user is actually in a room or a dwelling, a load sensor, for example). A smart meter or smart thermostat (also referred to as an intelligent meter or intelligent thermostat) may record consumption at programmable intervals and communicates that information or data to the front-end cluster 112 at regular intervals (e.g., hourly, daily, monthly). The communication may occur through one or more publicly or privately accessible distributed networks like the Intranet and Wi-Fi networks.
In some systems the publicly and privately accessible analytic system's 100 records may record building characteristics—the age and size of a dwelling, the number of rooms, and the number of appliances and allow users to compare current consumption to prior consumption, and execute normative comparisons—by comparing one household to another based on common attributes. A disaggregation process may also provide user specific recommendations about consumption in some systems without information about the devices and appliances consuming the resources.
An Empirical Mode Decomposition (EMD) for analyzing energy usage signals, for example, may be executed in some publicly and privately accessible analytic systems 100. If given electricity usage data for a predetermined period, the system may decompose the dataset into its mode functions such that those mode functions are identified as fluctuations in the base load due to the resistance of the aggregate devices or power delivered to the devices. The devices may include lighting, appliances, healing, or cooling, or fluctuations due to activities in bedrooms, family room, kitchen, game room, etc. without any knowledge about the occupants of the dwelling and appliances in the dwelling. If smart meter or smart thermostat data is used, the smart data may validate the interpretation of these mode functions.
Some EMD techniques analyze signals from non-linear, non-stationary processes; and thus the process may be applied in several domains for signal processing. The EMD process decomposes the original signal into several intrinsic mode functions (or IMFs). Given a one-dimensional signal Xj, sampled at times tj, j=I, . . . N the EMD technique may decompose the signal into a finite and small number of fundamental oscillatory modes. The mode functions (or IMFs) into which the signal is decomposed are obtained from the signal itself, and they are defined in the same time domain as the original signal. The modes are nearly orthogonal with respect to each other, and are linear components of the given signal. In some systems, the following two conditions must be satisfied for an extracted signal to be called an IMF: first, the total number of extremes of the IMF should be equal to the number of zero crossings, or they should be differ by one, at most; and second, the mean of the upper envelope and the lower envelope of the IMF should be zero.
The process to obtain the IMFs from the given signal is called sifting. A sifting process may include one or more of the following acts; 1. Identification of the maxima and minima of Xj. 2. Interpolation of the set of maximal and minimal points (by using cubic splines) to obtain an upper envelope (Xjup) and a lower envelope (Xflow), respectively. 3. Calculation of the point-by-point average of the upper and lower envelopes, mj=(Xjup+Xflow)/2. 4. Subtraction of the average from the original signal to yield, dj=xj−mj; 5. Testing whether satisfies the two conditions for being an IMF, steps 1 to 4 are repeated until dj satisfies two conditions; 6. Once an IMF is generated, the residual signal rj=xj−dj is regarded as the original signal, and steps 1 to 5 are repeated to generate the second IMF, and so on.
The sifting process is complete when either the residual function becomes monotonic, or the amplitude of the residue falls below a pre-determined small value (for example, when the error is below about 0.0005, for example) so that further sitting would not yield any useful components. The features of the EMD process may assure that the computation of a finite number of IMFs within a finite number of iterations. At the end of the process, the original signal, xy, may be represented as:
where rj, M is the final residue that has near zero amplitude and frequency, M is the number of IMFs, and dj,i are the IMFs.
As an illustration of the sifting process, consider the monthly electricity consumption data for three users over twenty four months. The original data is shown in
When analysing the IMFs for electricity usage, the publicly and privately accessible analytic system 100 shows the magnitude of the 4th IMF in
From a user's perspective, the publicly and privately accessible analytic system 100 may be divided into profiles of energy usage, comparison of energy usages among peers, and self-analysis of energy consumption patterns that may be rendered through the visualization service and visual communication medium. Once a user is registered on the publicly and privately accessible analytic system 100 (an exemplary flow is shown in
As shown in
To help users understand how their energy usage compares to others customers, a comparative graphical user display compares usage data among peers based on what is known about the user's energy consumption and an assessment of their property. The property assessment data used for this purpose may include the year the dwelling was built, the square footage of the dwelling, the number of rooms in the dwelling, and other property-specific data that is automatically mined from property assessors' database machines/servers 102, parcel data database machines/servers 104, and/or other remote third party sources 110, The front-end cluster 112 harvests the data and aggregates the data through a peer classifications or aggregations. Peer-classes may be formed through one or more attributes such as the size and age of a dwelling within a programmed tolerance range, the dwelling's style, the materials it is built with (e.g., vinyl or brick), household size, number of stories, etc. Thus, the classification allows users living in a twenty-five year old house of size 1000 sq. ft., for example, to be compared to other houses aged between twenty-three and twenty-seven years old and between 800 sq. ft. and 1200 sq. ft. All the houses that satisfy this criterion may be classified as the user's peers.
A graphical user display showing a collection of the outputs of such a comparison for a customer to peers in a same subdivision is shown in
From the snapshot shown in
From a user's perspective, self-analysis of energy consumption patterns oilers the users an opportunity to compare their consumption to that of their peers in other geographical areas too. To achieve this, users can specify the subdivision, zip code, or county of interest to them through a graphical user interface that initiates a comparison at the front-end cluster 112, hike the prior perspective, the comparison of consumption may be made relative to their peers. When differences are detected or deficiencies within the comparisons are found, the font-end cluster 112 may deliver the comparisons with on-line advertising that notifies the users of product(s) or service(s) and the reasons why the user should select or learn more about the product or service in question. The advertising may be monitored in real-time and is preferably target to the viewer's needs or viewing history.
As shown by the exemplary graphical user interface of
In an alternative graphical user interface a user may generate customized maps, by drawings on or editing an existing map to identify a desired location. As shown in
Once an area is selected, multiple outputs are presented for the users. One output may comprise a map that shows the location of the user's house and the boundary for the search area. An example of this output is also shown in
A second output may comprise a graphical user interface that compares the user's consumption to the average consumption of peers in this zip code area as shown at 1202 in
An optional usage diary allows users to “tag and track” their consumption pattern and perform some dwelling-specific analysis. The “tag and track” capability enables users to keep a diary of known events or add annotations to events during a monitoring period such as each month that could have resulted in a higher or lower overall consumption in that time period. A user may select the time of Interest (e.g., the month of interest) and make a note for their use, it may include for example a note such as. “hosted more guests”, “on vacation”, “replaced an old appliance”, “sealed the windows”, etc.
Some publicly and privately accessible analytic system 100 may process the tagged information or identify the tag as an event to identify trends that may begin at a particular time of the tagging using the tagged information. For example, if an energy-efficient event occurs, the information may be tagged to the appropriate time, and the savings in consumption, if any, is then automatically tracked by the publicly and privately accessible analytic system 100 thereafter.
Besides providing individual users with access to data, a private portal or utility portal may provide access to commercial users such as utility analysts. The private portal allows commercial users to query the publicly and privately accessible analytic system 100 for specific customers as well as for a group of customers. To query for a specific customer, the commercial user may enter information about a specific user. This information may include for example an account number or similar unique identifier or address of a house that may be entered in the dialog box shown in
Once an account number or similar information is submitted, the visualisation services of the application may enlarge a selected portion of a graphical image such as location of the dwelling of the desired user (or customer) on a map (not shown). In addition, the blending mode may render information about that dwelling above or below the map or via a separate dialog box, The information may include the address for the account number, the subdivision it is located, the year the house was built, and the size of the house.
Through another graphical user interface a commercial user can generate one or more real-time “heat maps” of year built or age as shown in
A utility view of another exemplary implementation of a publicly and privately accessible analytic system 100 is shown in
From a public view of one exemplary implementation shown in
New data analysis algorithms developed for understanding the spatial patterns of energy usage over time for comparison may be applied to the analysis herein. Exploratory data analysis may be applied for implicit knowledge in data sets. Tendencies or patterns in the data are analyzed using clustering techniques such as K-means, a fast clustering algorithm. Unfortunately, traditional K-means analysis only clusters observation vectors in feature space. Here, the combination of K-means algorithm with spatial features for an online spatial constrained K-means may generate spatially related datasets. Based on the detected patterns, the publicly and privately accessible analytic system 100 may rank consumption data to identify consumers with similar patterns. In some applications the rank may be applied as a pattern threshold for each consumer in each cluster. When a customer exceeds their pattern threshold, a negative alarm or message may issue (or be transmitted from the front-end cluster 112). A positive alarm or message may similarly issue (or be transmitted from the front-end cluster 112) if consumption moves to a lower ranking. Such additional information may assist users to determine what activities are responsible for their new ranking. For ranking the detected patterns, a distance measure such as variant of Kullback-Leibler divergence may be used. A suitable distance measure for energy consumption data may also be used. Furthermore, when the system 100 accesses LandScan Global population database, the system 100 may link patterns in consumption to demographic and socio-economic factors such as the number of rooms in a house, the number of occupants, and per capital income for rendering additional analysis into the usage data.
As shown in
The processes and systems described may execute software encoded in a non-transitory signal bearing medium, or may reside in a memory resident to or interfaced to one or more processors or controllers that may support a tangible communication interface, wireless communication interface, or a wireless system. The memory may retain an ordered listing of executable instructions for implementing logical functions and may retain one or more database engines that access files composed of records, each of which contains fields, together with a set of operations for searching, sorting, recombining, and/or other functions that are also retained in memory. A logical function may be implemented through digital circuitry, through source code, or through analog circuitry. The software may be embodied in any non-transitory computer-readable medium or signal-bearing medium, for use by, or in connection with an instruction executable system, apparatus, and device, resident to system that may maintain a persistent or non-persistent connection with a destination. Such a system may include a computer-based system, a processor-containing system, or another system that includes an input and output interface that may communicate with a publicly accessible distributed network and/or privately accessible distributed network through a wireless or tangible communication bus through a public and/or proprietary protocol.
The on-line cloud storage resources 120 may include nonvolatile memory (e.g., memory cards, flash drives, solid-state devices, ROM/PROM/EPROM/EEPROM, etc.), volatile memory (e.g., RAM/DRAM, etc.), that may retain a database or are part of database server(s) 116 that retains data in a database structure and supports a database sublanguage (e.g., structured query language, for example) that may be used for querying, updating, and managing data stored in a local or distributed memory of the databases. The database is accessible through database engine or a software interface between the database and user that handles user requests for database actions and controls database security and data integrity requirements. A client device 120 (that includes mobile ceil phones, wireless phones, personal digital assistants, two-way pagers, smartphones, portable computers, tablets, etc. in some systems 100) may be configured to communicate alone or with or through one or more tangible devices, such as a personal computer, a laptop computer, a set-top box, a customized computer system such as a game console, and other devices, for example.
A “computer-readable medium,” “machine-readable medium.” “propagated-signal” medium, and/or “signal-bearing medium” may comprise a non-transitory medium that contains, stores, communicates, propagates, or transports software tor use by or in connection with an instruction executable system, apparatus, or device. The machine-readable medium may selectively be, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. A non-exhaustive list of examples of a machine-readable medium would include: an electrical connection having one or more wires, a portable magnetic or optical disk, a volatile memory such as a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM or Flash memory), or an optical fiber. A machine-readable medium may also include a tangible medium upon which software is printed, as the software may be electronically stored as an image or in another format (e.g., through an optical scan), then compiled, and/or interpreted or otherwise processed. The processed medium may then be stored in a computer and/or machine memory.
The term “coupled” disclosed in this description may encompass both direct and indirect coupling. Thus, first and second parts are said to be coupled together when they directly contact one another, as well as when the first pan couples to an intermediate part which couples either directly or via one or more additional intermediate parts to the second part. The term “position,” “location.” or “point” may encompass a range of positions, locations, or points. The term “substantially” or “about” may encompass a range that is largely, bin not necessarily wholly, that which is specified. It encompasses all but a significant amount. When devices are responsive to commands events, and/or requests, the actions and/or steps of the devices, such as the operations that devices are performing, necessarily occur as a direct or indirect result of the preceding commands, events, actions, and/or requests. In other words, the operations occur as a result of the preceding operations. A device that is responsive to another requires more than an action (i.e., the device's response to) merely follow another action. The abbreviation “GIS” refers to the software embodied in a non-transitory medium used for processing spatial data. The term “GIScience” refers to the techniques and methods that drive the software in the non-transitory medium.
While various embodiments of the invention have been described, it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the invention. Accordingly, the invention is not to be restricted except in light of the attached claims and their equivalents.
This application claims the benefit of priority of U.S. Provisional Patent Application No. 61/543,830 filed Oct. 6, 2011 and titled “Citizen Engagement for Energy Efficient Communities.” which is incorporated by reference.
This application was made with United States government support under Contract No. DE-AC05-00OR22725 awarded by the United States Department of Energy. The United States government has certain rights in these inventions.
Number | Date | Country | |
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61543830 | Oct 2011 | US |