Patent Application
20240429737
During power outages, it is useful to have a backup power source to provide electricity. Current methods of providing a backup power source involve connecting a backup power source directly to an electrical panel bypassing the electrical meter. However, the current National Electric Code (NEC) for transfer switch connection requires customers installing a separate electric panel to dedicate certain branch circuits to be connected to the backup generator based on the rating of the backup generator or backup power. For customers to be compliant, they must install a new transfer switch and panel as the “correct” installation in
Therefore, there is a need for a system that enables a user to connect one or more backup power sources directly to an electrical meter and/or control one or more functions of the one or more backup power sources using the electrical meter.
In some embodiments, the Backup Power Transfer Meter (BPTM) system (hereafter, the “system”) is an economical approach to simplify the NEC interconnection requirement. In some embodiments, the system allows the customers to temporarily power their home using a backup power source (e.g., combustion generator, battery, photovoltaic, electric vehicle, etc.) when utility power is unavailable or off grid. In some embodiments, the system includes an auto-transfer switch that senses and switches to an alternate electrical power source (i.e., backup power source) when utility electrical power is interrupted. In some embodiments, the system automatically switches back to utility power when it becomes available. In some embodiments, they system is installed at the customer's existing conventional electric panel. In some embodiments, the system is configured to sense and transfer one or more loads in a customer's home load to the backup power source when the utility power is off grid and vice versa.
In some embodiments, the system provides electricity customers with the most economical option to use their back up power source. In some embodiments, the system's transfer switch functionality increases the flexibility of micro-gridding. In some embodiments, the system provides customers with the option to manually switch between one or more backup power sources and a utility power source.
In some embodiments, the system includes a meter configured to couple to one or more backup power sources. In some embodiments, the system enables one or more backup power sources to be directly connected to the meter. In some embodiments, a separate electrical panel is not required between the any of the one or more backup power sources and the meter. In some embodiments, circuitry within the meter housing is configured to enable one or more of: multiple parallel generation, phase monitoring, WiFi connectivity, transfer switch selection and control, a user interface (UI, GUI), communication connections, one or more (universal) amperage adaptor plugs, and Next Generation Meter (NGM) core integration.
In some embodiments, multiple generator circuitry is configured to enable the system to draw power from the one or more backup power sources simultaneously. In some embodiments, phase monitoring circuitry is configured to provide protection for electrical devices by monitoring and/or providing notification if any phase is reversed, missing, or out of balance. In some embodiments, WiFi connectivity is configured to enable wireless communications to control and/or receive alerts from the system. In some embodiments, a transfer switch selection and control module enables a user to: select which of the one or more backup power sources are enabled automatically, to choose a sequence for bringing backup power sources online, and/or run the backup power sources in parallel. In some embodiments, a user interface enables a user to visually and/or audibly receive information from the meter and/or control one or more meter functions. In some embodiments, communication connections enable data transmission to one or more wired and/or wireless communication devices. In some embodiments, one or more universal amperage adaptor plugs enables a backup power source supply to be converted to the correct amperage for a particular load.
In some embodiments, the system includes a next generation meter (“NGM”) core. In some embodiments, an NGM core comprises a compact modular electrical component that includes faster microprocessors, expanded memory, and multiple communications pathways within a hardware package less than 8″ by 6″. In some embodiments, the NGM core is approximately the size of a conventional credit card. In some embodiments, the NGM core enables modular maintenance within the meter by enabling replacement of critical electronic components without replacing the entire meter. In some embodiments, the NGM is configured to provide submetering for the one or more backup power sources in addition to the electrical utility.
In some embodiments, the NGM is configured to produce data in the form of both accurate billing data and grid data. In some embodiments, measured parameters for billing data include +/−real power component (Watts) and reactive power component (Vars). In some embodiments, the measured parameters for grid data are magnitude and phase angle of voltage and current, voltage and current harmonic distortion, and percent harmonic distortion. In some embodiments, the NGM is configured to log and/or report events such as sag/swell, undervoltage, and overvoltage conditions. In some embodiments, the NGM is configured to generate a phasor diagram of the meter installation and identified a phase using both single and three phase power sources. In some embodiments, NGM is configured to support 1-phase AC with a range of voltage from 85V to 305V. In some embodiments, the NGM is configured to support 3-phase AC with a range of voltage from 85 to 305V. In some embodiments, the NGM is configured to support multiple phase-neutral/phase-phase combinations such as Phases A and C to neutral, Phases A, B and C to neutral, Phase A to Phase B, Phase A to Phase B to Phase C, etc.
In some embodiments, the system includes an electric meter assembly comprising a removable or portable meter and/or a meter support platform or base. In some embodiments, the meter support platform or base is configured to removably electrically couple to at least one transformer. In some embodiments, the removable or portable meter is configured to removably electrically couple to the meter support platform or base. In some embodiments, the meter support platform or base is configured to receive inputs comprising at least one of an AC power supply, a phase voltage, and a phase current from the at least one transformer.
In some embodiments, the meter support platform or base is configured to provide outputs of at least one of DC power, AC phase voltage signals, and AC phase current signals to the removable or portable meter based at least in part on at least one of the inputs. In some embodiments, the removable or portable meter comprises a data manager configured for electric service analysis and transmission of electrical data including at least one of energy usage, internal temperature, voltage, current, phase angle, electric energy kWh and kVARh values, wrong installation detection, physical location of the removable or portable meter, and tamper detection.
In some embodiments, the removable or portable meter comprises an electric meter data management process stored on a non-transitory computer-readable storage medium that when executed by a processor is configured to provide web-enabled applications providing customer access to metering services including billing, energy usage, and statistics comprising current and past energy usage. In some embodiments, the removable or portable meter comprises a customer mobile application configured to enable the customer access on a mobile laptop computer and/or mobile phone or smart phone to monitor or view at least one parameter of an energy use by an electrical utility and/or one or more backup power source.
In some embodiments, the system includes a backup power transfer meter comprising: a housing, a circuit control board, a utility power connection, and one or more backup power connections. In some embodiments, the circuit control board is configured to be mounted in the housing. In some embodiments, the circuit control board comprises one or more utility contacts configured to isolate the utility power connection from an electrical load. In some embodiments, the circuit control board comprises one or more backup power contacts configured to isolate the one or more backup power connections from the electrical load. In some embodiments, the circuit control board comprises one or more sensors configured to detect a presence of utility power at the utility power connection and/or backup power at the one or more backup power connections.
In some embodiments, the backup power transfer meter further comprises multiple generator circuitry. In some embodiments, the multiple generator circuitry is configured to enable the circuit control board to draw power from two or more of the one or more backup power connections simultaneously. In some embodiments, the backup power transfer meter further comprises a transfer switch selection and control module. In some embodiments, the transfer switch selection and control module is configured to enable a user to select which of the one or more backup power connections to supply power to the circuit control board automatically. In some embodiments, the transfer switch selection and control module is configured to enable the user to choose a sequence for delivering electrical power from the utility power connection and/or the one or more backup power connections to the backup power transfer meter. In some embodiments, the transfer switch selection and control module is configured to enable the user to complete an electrical connection between two or more of the one or more backup power connections in parallel.
In some embodiments, the backup power transfer meter of claim 3, further comprises one or more universal amperage adaptor plugs. In some embodiments, the one or more universal amperage adaptor plugs are configured to enable the utility power and/or backup power to be converted to a correct amperage for the electrical load. In some embodiments, the backup power transfer meter further comprises a push button panel. In some embodiments, the push button panel is located on a side of the housing. In some embodiments, the push button panel comprises one or more indicator lights configured to indicate an electrical state of the backup power transfer meter. In some embodiments, the one or more indicator lights indicate: when the one or more backup power connections are in use; when the utility power connection is in use; and/or when the utility power is available. In some embodiments, the backup power transfer meter further comprises a thermo sensor. In some embodiments, the thermo sensor is configured to detect an abnormal temperature condition within the housing. In some embodiments, the utility power comprises electricity from an electrical grid configured to supply the electricity to multiple electrical meters.
In some embodiments, the backup power transfer meter comprises: a housing, a control circuit, a utility provided power connection, and one or more backup power connections. In some embodiments, the control circuit is configured to be mounted in the housing. In some embodiments, the control circuit comprises one or more sensors configured to detect a presence of utility provided electricity at the utility provided power connection and backup power electricity at the one or more backup power connections. In some embodiments, the control circuit is configured to deliver the backup power electricity to the one or more sensors when both the utility provided power connection and the one or more backup power connections are both isolated from an electrical load by the control circuit. In some embodiments, the one or more sensors are configured to continue to receive electrical power from the one or more backup power connections upon loss of the utility provided electricity.
In some embodiments, the backup power transfer meter, further comprises multiple generator circuitry. In some embodiments, the multiple generator circuitry is configured to enable the control circuit to supply electricity from two or more of the one or more backup power connections to an electrical load simultaneously. In some embodiments, the backup power transfer meter further comprises a transfer switch selection and control module. In some embodiments, the transfer switch selection and control module is configured to enable a user to select which of the utility provided power connection and/or the one or more backup power connections provides electricity to an electrical load. In some embodiments, the transfer switch selection and control module is configured to enable the user to choose a sequence for selecting which of the utility provided power connection and/or the one or more backup power connections supplies electricity to the electrical load. In some embodiments, the transfer switch selection and control module is configured to enable a user to select a delivery of electricity from the one or more backup power connections in parallel. In some embodiments, the backup power electricity is supplied from one or more of photovoltaic electrical source, a battery electrical source, and/or a fossil fuel electrical power source. In some embodiments, the backup power transfer meter further comprises a thermo sensor. In some embodiments, the thermo sensor is configured to detect an abnormal temperature condition within the housing. In some embodiments, an abnormal temperature condition includes arcing.
Some embodiments of the disclosure are directed to a method for implementing AI locally on a smart meter (e.g., a BPTM). In some embodiments, the method comprises a step of training, by a computer, an artificial intelligence (AI) model using historical data associated with electrical energy consumption to create an anomaly detection AI model. Some embodiments include a step of deploying the anomaly detection AI model to an electrical meter computer of an electrical meter as at least part of a local AI. Some embodiments include a step of executing, by the electrical meter computer, the anomaly detection AI model locally on the electrical meter. Some embodiments include a step of detecting, by the anomaly detection AI model, one or more electrical anomalies associated with one or more load devices being supplied electricity by the electrical meter. Some embodiments include a step of controlling the one or more load devices associated with the one or more electrical anomalies to mitigate effects of the one or more electrical anomalies.
In some embodiments, the electrical meter comprises a panel connection configured to enable utility power to be delivered to an electrical panel supplying the one or more load devices. In some embodiments, the electrical meter comprises a BPTM comprising one or more power source connections configured to enable electrical power to be delivered to the electrical panel.
In some embodiments, the method includes training, by a central computer, an artificial intelligence (AI) model using historical data associated with electrical energy consumption to create an energy optimization AI model. Some embodiments includes a step of deploying the energy optimization AI model to an electrical meter computer of an electrical meter as at least part of a local AI. Some embodiments includes a step of executing, by the electrical meter computer, the energy optimization AI model locally on the electrical meter. Some embodiments includes a step of receiving, by the energy optimization AI model, load device data associated with one or more load devices being supplied electricity by the electrical meter. Some embodiments includes a step of controlling the one or more load devices associated with the load device data to decrease electrical usage during a peak electrical time period.
In some embodiments, the disclosure is directed to a system comprising an electrical meter comprising one or more computers, the one or more computers comprising one or more processors and one or more non-transitory computer readable media, the one or more non-transitory computer readable media including program instructions stored thereon that when executed cause the one or more computers to execute one or more program steps. Some embodiments includes a step to receive, by the one or more processors, an anomaly detection AI model, the anomaly detection AI model trained to identify electrical anomalies within an electrical network coupled to the electrical meter. Some embodiments includes a step to store, by the one or more processors, the anomaly detection AI model in the one or more non-transitory computer readable media as at least part of a local AI. Some embodiments includes a step to execute, by the one or more processors, the anomaly detection AI model locally on the electrical meter. Some embodiments includes a step to detect, by the anomaly detection AI model, one or more electrical anomalies associated with one or more load devices being supplied electricity by the electrical meter in the electrical network. Some embodiments includes a step to send, by the one or more processors, a control command configured to control the one or more load devices associated with the one or more electrical anomalies to mitigate effects of the one or more electrical anomalies.
In some embodiments, the local AI comprises energy optimization AI model configured to control the one or more load devices a to decrease electrical usage during a peak electrical time period. In some embodiments, a peak electrical time period includes a time period within a day where utility power usage is greater than an average utility power usage for that day. In some embodiments, a peak electrical time period includes a time of day when energy prices are highest for a given energy service contract.
In some embodiments, controlling the one or more load devices includes automatically sending a control command from the electrical meter directly to the one or more load devices. In some embodiments, the control command is created by the local AI. In some embodiments, controlling the one or more load devices includes automatically sending a message to a central control platform remote from the electrical meter. In some embodiments, controlling the one or more load devices includes the central control platform automatically controlling the one or more load devices. In some embodiments, the central control platform automatically controlling the one or more load devices includes a central AI platform creating a control command. In some embodiments, automatically sending the message includes sending the message to the central control platform using a first network. In some embodiments, controlling the one or more load devices includes sending the control command to the one or more load devices on a second network.
The following section is provided to enable those of ordinary skill to make and use some embodiments of the system. The arrangement of physical components is not all-inclusive, and those of ordinary skill will understand that numerous configurations for the system can be derived from the present disclosure.
Referring to
In some embodiments, the BPTM 200 provides a safe, easy to use way for a user to connect one or more backup power sources to electrical loads. For example, in some embodiments, the BPTM is configured to couple to one or more VAC, 30-60 Amp generators.
In some embodiments, the system includes one or more backup power connectors (power source connections, load device connections) extending from the system housing configured to couple to one or more backup power sources and/or load devices not directly coupled to the electrical panel being fed by the meter 210. In some embodiments, one or more backup power sources (any reference to a backup power source is also a reference to a load device for the purposes of defining the metes and bounds of the system) are coupled using one or more interconnect cables 240 (see
In some embodiments, to couple the meter 210 to the socket adaptor 220, a user first identifies the two wire pairs 311 extending from the back of the meter 210 (see
In some embodiments, a description of how the system operates in different states is described as follows:
In some embodiments, the indicator lights indicate fault conditions. In some embodiments, example indicator light configurations for faults are as follows:
In some embodiments, a “meter disconnect will not open” fault caused by a meter disconnect relay results in amber and green LED lights flashing together.
In some embodiments, “a generator relay will not open” fault caused by generator relay failure or alternate house power source detection results in amber and blue LED lights flashing together.
In some embodiments, an “only one line voltage present for 100 ms” fault caused by utility distribution transformer failure results in amber and green LED lights flashing alternately.
In some embodiments, an “only one load voltage present for 100 ms” fault caused by meter disconnect relay or generator relay failure results in amber and blue LED lights flashing alternately.
In some embodiments, an “unexpected load voltage present” fault caused by and alternate power source wired to a load (e.g., house, panel) results in green and blue LED lights flashing together.
In some embodiments, if any fault condition exist, then no power will be supplied to electrical components connected to the system. In some embodiments, if any fault condition exists, the watchdog timer generates a system reset. In some embodiments, a watchdog timer includes a hardware time that automatically generates a processor reset if the system does not reset the timer before it counts down. In some embodiments, the watchdog timer is a conventional watchdog timer. In some embodiments, the system is configured to then check again after the reset to see if any fault condition still exists.
In some embodiments, the isolated supply module 1112 is powered by utility provided power and the generator isolated supply 1204 supplied by one or more connected backup power source 1140. In some embodiments, the isolated supply module 1204 output 1207 powers both the control module 1130 and generator relays 1111 when the backup power source 1140 is energized with electrical power. In some embodiments, without power supplied from a backup power source 1140, the generator stabs 1114 would never be connected in the event of a loss of utility power as the meter would be the only source of power for the meter disconnect utility power relays 1115. In some embodiments, the isolated supply module 1204 is configured to supply generator power to the control supplies 1208 via an electrical line 1207. In some embodiments, the control supplies 1208 is configured to receive electrical power from the backup power source such that it is not dependent on the generator relays 1111 or the utility power relays 1115 for electricity. In some embodiments, this arrangement ensures that the disconnect monitor, control, and drive unit 1131 located within the control module 1130 are always supplied electrical power when there is no utility power source 1140 supplying power through the utility power relays 1115.
In some embodiments, the BPTM includes a thermo sensor 1117 as shown in
In some embodiments, The BPTM has the ability to support up to 60 A. In order to achieve 60 A, the general purpose power relay is modified from 30 A to 60 A and the multi-conductor cable is modified from No. 10 AWG to at least No. 6 AWG.
In some embodiments, the computer system 410 comprises at least one processor 432. In some embodiments, the at least one processor 432 can reside in, or coupled to, one or more conventional server platforms (not shown). In some embodiments, the computer system 410 can include a network interface 435a and an application interface 435b coupled to the least one processor 432 capable of processing at least one operating system 434. Further, in some embodiments, the interfaces 435a, 435b coupled to at least one processor 432 can be configured to process one or more of the software modules (e.g., such as enterprise applications 438). In some embodiments, the software application modules 438 can include server-based software and can operate to host at least one user account and/or at least one client account and operate to transfer data between one or more of these accounts using the at least one processor 432.
With the above embodiments in mind, it is understood that the system can employ various computer-implemented operations involving data stored in computer systems. Moreover, the above-described databases and models described throughout this disclosure can store analytical models and other data on computer-readable storage media within the computer system 410 and on computer-readable storage media coupled to the computer system 410 according to various embodiments. In addition, in some embodiments, the above-described applications of the system can be stored on computer-readable storage media within the computer system 410 and on computer-readable storage media coupled to the computer system 410. In some embodiments, these operations are those requiring physical manipulation of physical quantities. Usually, though not necessarily, in some embodiments these quantities take the form of one or more of electrical, electromagnetic, magnetic, optical, or magneto-optical signals capable of being stored, transferred, combined, compared and otherwise manipulated. In some embodiments, the computer system 410 can comprise at least one computer readable medium 436 coupled to at least one of at least one data source 437a, at least one data storage 437b, and/or at least one input/output 437c. In some embodiments, the computer system 410 can be embodied as computer readable code on a computer readable medium 436. In some embodiments, the computer readable medium 436 can be any data storage that can store data, which can thereafter be read by a computer (such as computer 440). In some embodiments, the computer readable medium 436 can be any physical or material medium that can be used to tangibly store the desired information or data or instructions and which can be accessed by a computer 440 or processor 432. In some embodiments, the computer readable medium 436 can include hard drives, network attached storage (NAS), read-only memory, random-access memory, FLASH based memory, CD-ROMs, CD-Rs, CD-RWs, DVDs, magnetic tapes, other optical and non-optical data storage. In some embodiments, various other forms of computer-readable media 436 can transmit or carry instructions to a remote computer 440 and/or at least one user 431, including a router, private or public network, or other transmission or channel, both wired and wireless. In some embodiments, the software application modules 438 can be configured to send and receive data from a database (e.g., from a computer readable medium 436 including data sources 437a and data storage 437b that can comprise a database), and data can be received by the software application modules 438 from at least one other source. In some embodiments, at least one of the software application modules 438 can be configured within the computer system 410 to output data to at least one user 431 via at least one graphical user interface rendered on at least one digital display.
In some embodiments, the computer readable medium 436 can be distributed over a conventional computer network via the network interface 435a where the system embodied by the computer readable code can be stored and executed in a distributed fashion. For example, in some embodiments, one or more components of the computer system 410 can be coupled to send and/or receive data through a local area network (“LAN”) 439a and/or an internet coupled network 439b (e.g., such as a wireless internet). In some embodiments, the networks 439a, 439b can include wide area networks (“WAN”), direct connections (e.g., through a universal serial bus port), or other forms of computer-readable media 436, or any combination thereof.
In some embodiments, components of the networks 439a, 439b can include any number of personal computers 440 which include for example desktop computers, and/or laptop computers, or any fixed, generally non-mobile internet appliances coupled through the LAN 439a. For example, some embodiments include one or more of personal computers 440, databases 441, and/or servers 442 coupled through the LAN 439a that can be configured for any type of user including an administrator. Some embodiments can include one or more personal computers 440 coupled through network 439b. In some embodiments, one or more components of the computer system 410 can be coupled to send or receive data through an internet network (e.g., such as network 439b). For example, some embodiments include at least one user 431a, 431b, is coupled wirelessly and accessing one or more software modules of the system including at least one enterprise application 438 via an input and output (“I/O”) 437c. In some embodiments, the computer system 410 can enable at least one user 431a, 431b, to be coupled to access enterprise applications 438 via an I/O 437c through LAN 439a. In some embodiments, the user 431 can comprise a user 431a coupled to the computer system 410 using a desktop computer, and/or laptop computers, or any fixed, generally non-mobile internet appliances coupled through the internet 439b. In some embodiments, the user can comprise a mobile user 431b coupled to the computer system 410. In some embodiments, the user 431b can connect using any mobile computing 431c to wireless coupled to the computer system 410, including, but not limited to, one or more personal digital assistants, at least one cellular phone, at least one mobile phone, at least one smart phone, at least one pager, at least one digital tablets, and/or at least one fixed or mobile internet appliances.
In some embodiments, the system is directed to methods and computer implemented program steps to execute a maintenance test on the backup power transfer meter. In some embodiments, the maintenance test is configured to cause a computer to execute a series of test program instructions to verify one or more safety features are executed properly during a switch (transfer) from utility power to a backup power source (e.g., generator). Some embodiments include a step for a user to connect a backup power source to the BPTM and turn on the backup power source. In some embodiments, a program step is configured to cause a generator power indicator (e.g., light) to initiate, displaying the status of the available generator power on the housing. In some embodiments, the BPTM computer (local computer) is configured to receive a user input to start the test (e.g., press the RESET button 5 times rapidly).
In some embodiments, the BPTM computer is configured to execute a switch command upon receiving the user input. In some embodiments, the switch command is configured to cause the disconnect switch inside the BPTM to toggle to an open position. Some embodiments include a program step to cause the disconnect switch to remain open (not connected to the utility or backup power source relay) during a home voltage safety test. In some embodiments, the home voltage safety test includes detecting if there is any voltage present (e.g., any loads) within the connection circuitry on the power output side (load side) of the BPTM. Some embodiments include a step for the computer to cause the disconnect switch to engage (connect to) the backup power source relay after no voltage is detected on the power output side of the BPTM. Some embodiments include a step to display an indicator (e.g., led illumination) on the BPTM housing once the load side is connected to the BPTM. In some embodiments, the instructions cause the computer to execute the test for a programmed time limit (e.g., 10 minutes) and/or until the backup power source is turned off.
In some embodiments, the BPTM computer is configured to receive a notification (e.g., using an AMI network) of a scheduled power outage. In some embodiments, the BPTM is configured to automatically switch to backup power upon sensing a loss of utility power. In some embodiments, the BPTM is configured to execute a planned switch to backup power based on a time associated (e.g. received) with the scheduled power outage notification. In some embodiments, the planned switch includes program instructions to execute the planned switch a predetermined amount of time before the scheduled power outage time as to avoid loss of power to the load devices. In some embodiments, the BPTM computer is configured to send a notification (e.g., App, text, call, email, etc.) to a user that includes details of the scheduled power outage, which may include an outage time and/or one or more reminders of the outage time. In some embodiments, this allows a user to manually change to backup power before the scheduled power outage. In some embodiments, the BPTM is configured to switch back to utility power when power is available and/or when power is scheduled to be restored according to an end of the received power outage timeframe. In some embodiments, the BPTM is configured to turn off one or more backup power sources and/or redirect electricity from one or more backup power sources (e.g., solar, generator) to one or more other backup power sources (e.g., EV, home battery cells) once utility power is available and/or after switching to utility power. In some embodiments, the BPTM computer is integral (i.e., local; within the housing) to the BPTM itself, and the instructions are executed at the BPTM, eliminating the need for remote control of the BPTM over a network connection, further ensuring no power interruption to the electrical devices in the event network communication is lost.
In some embodiments, the BPTM computer includes instructions stored on one or more non-transitory computer readable media configured to cause the BPTM to determine a disconnect status (DS) of the BPTM before switching to backup power. In some embodiments, the BPTM is configured not to automatically switch to one or more backup power sources if utility power to the BPTM has been disconnected by the utility provider (e.g., for non-payment), which enables prevention of the use of the BPTM if service has not been purchased. In some embodiments, the BPTM includes program instructions that is configured to cause the BPTM to schedule a switch to generator power based on expected utility provider disconnection, enabling the customer to use the BPTM to supply power to electrical loads even when the utility connection is remotely disconnected. In some embodiments, the BPTM computer is configured to execute the same instructions for a utility provider disconnect status (DS) as for a planned power outage. In some embodiments, the BPTM is configured to detect BPTM DS original status before transferring to generator when utility is not available and return to utility power with the original DS status.
In some embodiments, the BPTM is configured to detect and switch multiple (e.g., four) available backup generation sources. Upon one source exhaustion, such as low generator fuel, in some embodiments, the BPTM is configured to automatically switch to another backup source, such as battery, PV power, EV and generator. Some embodiments use local AI to monitor and predict fuel availability (e.g., sun, hydrocarbon, battery) and create a usage plan to maximize available fuel resources, as well as execute the control commands described herein. In some embodiments, the circuitry in the BPTM is configured to directed each of a plurality of backup power sources to specific load devices and/or the utility grid itself. In some embodiments, the BPTM (or any smart meter described herein) is configured to execute and AI algorithm locally on the meter computer to create and implement a load forecasting and/or energy optimization control schedule to shift device loads from one period of time to another.
In some embodiments, the system is configured to initiate a micro-gridding protocol. In some embodiments, micro-gridding includes suppling power to localized power grids and/or device loads so that they may operate independently and/or in conjunction with the larger traditional grid. In some embodiments, one or more backup power sources that may be connected to a BPTM include, as non-limiting examples, solar panels, wind turbines, generators, and/or batteries, where the BPTM is configured to distribute and manage electricity within a defined area. For example, in some embodiments, the BPTM is configured to direct a solar system turn on and supply home loads and/or send excess power to an electric vehicle (i.e., battery storage). Upon receiving a request for power from a utility provider, or upon loss of utility power within a microgrid, the BPTM is configured to enable at least a portion of the power generated from the backup power source into the microgrid through the utility connection. In some embodiments, the BPTM is configured supply power from one or more backup power sources to both the electrical utility grid and one or more device loads connected to an electrical panel simultaneously. In some embodiments, the BPTM is configured to transfer electrical energy from one (backup) power source connection to another.
In some embodiments, the BPTM acts like a hub configured to supply power to new loads when there are no breaker spaces available for new loads in an electrical panel. In some embodiments, the BPTM is able to distribute loads intelligently, such that a new electrical panel is not needed to support new loads. Such panels are costly to purchase and install. As a non-limiting example, a new electrical panel is not needed if an electric vehicle is purchased, as the BPTM itself is configured to directly supply, monitor, and meter (i.e., record electrical usage) electricity supplied to the electric vehicle (and/or any load device) through one or more of the BPTM backup power source connections. In some embodiments, the BPTM is configured to receive a status of a distribution transformer load (e.g., from a utility command center and/or other smart meters) and/or trigger turn ON/OFF new loads accordingly to prevent overloaded transformer conditions.
In some embodiments, the BPTM is configured to communicate with one or more other BPTM (a reference to a BPTM is also a reference to a smart meter in general) in a distributed network. In some embodiments, the BPTM is configured to calculate the load on a transformer using information received from one or more other BPTMs. In some embodiments, the BPTM is configured to connect and/or disconnect one or more source connections supplying an electrical load (e.g., EV charging source) to reduce load on a transformer. In some embodiments, the BPTM is configured to monitor distribution transformer loads and/or control (e.g., turn ON/OFF, set temperature) device loads on the load side of the meter to prevent overloaded transformer conditions.
In some embodiments, the BPTM is compatible with standard panels and smart panels and/or capable of monitoring and adjust loads (i.e., adjust 240V level 2 charger to 120V level 1) to prevent overload conditions. In some embodiments, the BPTM system includes one or more software applications configured to enable customers to control and/or prioritize loads corresponding to one or more backup power sources.
In some embodiments, the BPTM includes one or more local artificial intelligence (AI) models (collectively referred to herein as local AI) stored and/or executed locally on the BPTM computer. In some embodiments, locally executed AI models are used to analyze various BPTM conditions and control one or more load devices without sending the pre-analyzed data over a network for analysis by a central AI platform (e.g., AWS®, Azure®, Watson®). In some embodiments, the local AI and central AI platforms form an artificial intelligence network that significantly enhances load monitoring within a home by leveraging advanced algorithms and real-time data processing capabilities, and while described in association with a BPTM, can be used with any type of smart meter. Non-limiting functionality executed by the local AI includes one or more of (near) real-time load analysis, predictive analytics, energy optimization, transitions between backup power sources (as described above), optimization of backup power sources, external load management, user engagement and feedback (Q&A), security and privacy, learning, and/or adaptation.
In some embodiments, the local AI is configured to execute (real-time) load analysis. In some embodiments, load analysis includes dynamic load profiling. In some embodiments, the AI is configured to continuously analyze power consumption patterns of various load devices (e.g., appliances) on the load side of the BPTM meter, which includes load devices connected to an electrical panel and the one or more backup power sources. In some embodiments, the AI is configured to create load profiles for one or more load devices.
In some embodiments, load analysis includes anomaly detection. In some embodiments, the AI is configured to detect unusual consumption patterns that may indicate faulty appliances, energy wastage, potential safety issues (e.g., arc faults, overheating, etc.), and/or energy theft.
In some embodiments, the local AI is configured to execute predictive analytics. In some embodiments, predictive analytics includes load forecasting. In some embodiments, the AI is configured to predict future energy consumption based on one or more of historical data, weather forecasts, and/or user behavior. This functionality allows the local AI to optimize energy usage and/or planning for peak demand periods. In some embodiments, predictive analytics includes preventive maintenance. By identifying trends and potential issues early, the local AI, in some embodiments, is configured to suggest maintenance for load devices (e.g., EV batteries, solar panels) before they fail, reducing downtime and repair costs and improving safety.
In some embodiments, the local AI is configured to execute energy optimization. In some embodiments, the local AI is configured to monitor and analyze customers' usage (peak vs. off peak). In some embodiments, the local AI is configured to shift peak device loads to off peak times (e.g., shift peak pricing to off peak pricing). In some embodiments, energy optimization includes demand response. In some embodiments, the BPTM is configured to enable the local AI to manage and/or control the operation of one or more load devices (e.g., HVAC systems, water heaters) to reduce load during peak times, helping to lower energy costs and mitigate grid overload.
In some embodiments, energy optimization includes load shifting which includes control of one or more smart appliances. In some embodiments, the local AI is configured to schedule the operation of certain appliances (e.g., washing machines, dishwashers) during off-peak hours when electricity rates are lower, optimizing energy consumption and cost.
In some embodiments, the local AI is configured to execute backup power source management. In some embodiments, the local AI is configured to manage the transition between grid power and backup power sources (e.g., batteries, generators) during outages or peak load times, ensuring continuous power supply. In some embodiments, the local AI is configured to manage the flow of electricity to and/or from backup power sources. In some embodiments, flow management includes determining the best times to charge and discharge batteries based on load forecasts and electricity rates. In some embodiments, flow management includes determining the most optimum arrangement for parallel backup power source and/or charging one backup power source with another backup power source and/or utility power. In some embodiments, the local AI is configured to create and/or manage charging schedules to minimize costs and reduce load during peak hours for battery charging. In some embodiments, the AI is configured to optimize the use of renewable energy sources (e.g., solar panels) by balancing generation and consumption, storing excess energy in batteries, and/or feeding electricity back to the grid.
In some embodiments, the local AI is configured to interact with one or more remote computers (e.g., smart phone, laptop, tablet) and display an AI engagement interface. In some embodiments, the local AI is configured to communicate with a user (e.g., via an App) and/or provide homeowners with energy usage reports, highlighting areas where they can save energy and reduce costs, and/or answer questions about suggested and/or implemented optimization. Based on user behavior and preferences, the local AI, in some embodiments, is configured to display personalized energy-saving tips and suggestions for more efficient appliance usage.
In some embodiments, the local AI includes one or more of the forecasting model, anomaly detection model, and energy optimization model. In some embodiments, each model is a separate AI model configured to work in unison as local AI. In some embodiments, this allows for modularity, where each model can be deployed, updated, executed, and/or removed from a smart meter based on the needs of the customer and/or the demand from the smart meter.
The following describes a non-limiting workflow and computer executed program steps for implementing load forecasting on a smart meter (e.g., BPTM) using local AI according to some embodiments:
In the training phase, historical energy consumption data and inputs such as load, weather conditions, and time of day are collected in accordance with some embodiments. In some embodiments, the data is preprocessed by normalizing it to a suitable range and/or handling any missing data points through interpolation and/or other imputation methods. In some embodiments, the preprocessed data is then used to train a time-series forecasting model. The trained model is validated and tested to ensure its accuracy and performance. Subsequently, the trained model is converted to a local AI format suitable for execution on a smart meter computer, which includes a microcontroller in some embodiments.
In the deployment phase, the local AI model is flashed onto the smart meter's computer in accordance with some embodiments. In some embodiments, this involves loading the model into the smart meter's computer memory during the deployment process. In some embodiments, this loading is done over an AMI network connected to the smart meter, allowing continuous improvements to the local AI model to be deployed as needed.
Once the model is deployed, the inference phase begins, which is executed repeatedly in real-time according to some embodiments. During the inference phase, in some embodiments, real-time inputs such as current load, weather conditions, and time of day are collected by the smart meter. The real-time data is preprocessed by normalizing it to the same range used during training and/or by handling any missing data points as described above. Upon the initial power-on or reset of the smart meter, the local AI model is loaded from storage into the smart meter's computer RAM for execution. This initial loading ensures that the model has the necessary resources to run, and sufficient memory allocation is provided for model parameters and input data.
Subsequently, in some embodiments, relevant features are extracted from the real-time input data. In some embodiments, these features are then fed into the pre-trained forecasting model to generate predictions for the next time interval. In some embodiments, the model's output is post-processed by denormalizing it back to the original scale. In some embodiments, the forecasted load is then saved for further use in optimization, load device control, and/or is displayed to the user.
The following describes a non-limiting workflow and computer executed program steps for implementing anomaly detection and/or control on a smart meter (e.g., BPTM) using local AI according to some embodiments:
In the training phase, historical energy consumption data and inputs such as load, weather conditions, and time of day are collected in accordance with some embodiments. In some embodiments, the data is preprocessed by normalizing it to a suitable range and/or handling any missing data points through interpolation and/or other imputation methods. In some embodiments, the preprocessed data is then used to train an anomaly detection model. The trained model is validated and tested to ensure its accuracy and performance. Subsequently, the trained model is converted to a local AI format suitable for execution on a smart meter's computer and/or microcontroller in accordance with some embodiments.
In the deployment phase, the local AI model is loaded onto the smart meter's computer, according to some embodiments, which includes loading the model into the computer's memory or storage during the deployment process. In some embodiments, the local AI loading is done over an AMI network connected to the smart meter, allowing continuous improvements to the local AI model to be deployed as needed through the electrical utility communication network.
Once the model is deployed, the inference phase begins, which is executed repeatedly in real-time according to some embodiments. During the inference phase, in some embodiments, real-time energy consumption data is continuously collected by the smart meter. In some embodiments, this data is aggregated over short intervals, such as every minute, as a non-limiting example, to enable detailed analysis. In some embodiments, the real-time data is preprocessed by normalizing it to the same range used during training and/or by handling any missing data points as described above. Upon the initial power-on or reset of the smart meter, the local AI model is loaded from storage into the computer's RAM for execution in accordance with some embodiments. This initial loading ensures that the model has the necessary resources to run, and sufficient memory allocation is provided for model parameters and input data.
Subsequently, in some embodiments, relevant features are extracted from the real-time input data. These features may include sudden spikes or drops in consumption, deviations from typical consumption patterns, and correlations with external factors, such as weather changes, as non-limiting examples according to some embodiments. In some embodiments, these features are then fed into the pre-trained anomaly detection model executed locally by the smart meter to identify potential anomalies. In some embodiments, the anomaly detection model is configured to determine if the current consumption pattern deviates significantly from the norm, thereby identifying potential anomalies.
In some embodiments, in the alert generation phase, if an anomaly is detected, an alert is generated for the homeowner and/or the event is logged for further analysis according to some embodiments. In some embodiments, automated actions are triggered by the local AI, such as shutting down a potentially faulty appliance and/or sending a notification to the user.
In some embodiments, the automated actions (any described herein) are executed by the local AI and/or one or more models that form the local AI. In some embodiments, the local AI on the smart meter is configured to directly control smart appliances (load devices). In some embodiments, when an anomaly is detected, and/or a specific load forecast is made, the local AI is configured trigger actions such as shutting down a potentially faulty appliance or adjusting the operation of devices to optimize energy usage.
In some embodiments, the local AI executing on the smart meter generates recommendations or alerts, which are then sent to a central platform (e.g., SCADA, central AI platform), a user App, and/or other programs responsible for controlling smart appliances. In some embodiments, these programs can evaluate the AI's recommendations and decide on the appropriate actions. This approach allows for an even more compact local AI model, as the recommendations send over the AMI and/or internet are a fraction of the size of the raw data. In some embodiments, the central AI platform is configured to execute further analysis and/or adjust the recommendations based on larger datasets and/or the recommendations of other smart meter local AI. Commands can then be sent through the AMI network and/or the internet (to minimize AMI traffic) to make the appropriate adjustments to load devices. In some embodiments, the central AI platform is configured to make recommendations to adjust the local AI model (which includes one or more models) for improved accuracy and/or consistency. The retrained local AI and/or specific AI model can be deployed as previously described in an update.
The following describes a non-limiting workflow and computer-executed program steps for implementing energy optimization and/or control on a smart meter (e.g., BPTM) using local AI according to some embodiments:
In the data collection phase, historical energy consumption, which includes backup power sources, are collected in accordance with some embodiments. In some embodiments, historical consumption data is used to understand typical usage patterns. In some embodiments, loads are analyzed to identify high-energy-consuming devices in accordance with some embodiments. In some embodiments, loads are categorized into essential and non-essential to prioritize optimization actions.
In the model loading phase, in some embodiments, the pre-trained optimization model is loaded into the smart meter as part of the local AI. In some embodiments, the local AI loading is performed upon the initial power-on or reset of the smart meter, ensuring that the model is ready for real-time optimization.
In some embodiments, once the model is loaded, the optimization decision phase begins, which is executed repeatedly in real-time according to some embodiments. During this phase, in some embodiments, real-time data is fed into the optimization model. In some embodiments, the local AI is configured to determine one or more actions to optimize energy usage, such as adjusting thermostat settings, scheduling appliance operations during off-peak hours, and switching to backup power sources.
In some embodiments, in the action execution phase, the optimization decisions are executed by sending control signals to one or more load devices (which include backup power sources) in accordance with some embodiments. In some embodiments, the impact of the optimization actions is continuously or periodically monitored and adjusted by the local AI as necessary. In some embodiments, the continuous monitoring of the load devices enables a feedback loop to refine the energy optimization model based on real-world performance.
In some embodiments, the automated actions described herein are executed by the local AI. In some embodiments, the AI on the smart meter directly controls smart appliances (load devices). When an optimization decision is made, the AI is configured to trigger actions such as adjusting thermostat settings, scheduling appliance operations during off-peak hours, and switching to backup power sources.
In some embodiments, instead of directly controlling load devices, the local AI executing on the smart meter generates recommendations or alerts, which are then sent to a central platform (e.g., SCADA, central AI platform, etc.) and/or other programs responsible for controlling smart appliances. In some embodiments, the central platform and/or other programs are configured to evaluate the AI's recommendations and decide on the appropriate actions. This approach allows for an even more compact local AI model, as the recommendations sent over the AMI are a fraction of the raw data.
In some embodiments, the central AI platform is configured to execute further analysis and/or adjust the recommendations based on larger datasets and/or the recommendations of other smart meter local AIs. In some embodiments, control commands are sent through the AMI network and/or the internet (to minimize AMI traffic) to make the appropriate adjustments to load devices. In some embodiments, the central AI platform is configured to make recommendations to adjust the local AI model for improved accuracy and/or consistency. In some embodiments, the retrained local AI model is deployed as previously described.
The subject matter described herein are directed to technological improvements to the field of electrical power supply by providing a meter configured to switch between multiple power sources to eliminate the need for separate junction boxes and/or panels. The disclosure also describes the specifics of how a machine including one or more computers comprising one or more processors and one or more non-transitory computer implement the system and its improvements over the prior art. The instructions executed by the machine cannot be performed in the human mind or derived by a human using a pin and paper but require the machine to convert process input data to useful output data. Moreover, the claims presented herein do not attempt to tie-up a judicial exception with known conventional steps implemented by a general-purpose computer; nor do they attempt to tie-up a judicial exception by simply linking it to a technological field. Indeed, the systems and methods described herein were unknown and/or not present in the public domain at the time of filing, and they provide a technologic improvements advantages not known in the prior art. Furthermore, the system includes unconventional steps that confine the claim to a useful application.
It is understood that the system is not limited in its application to the details of construction and the arrangement of components set forth in the previous description or illustrated in the drawings. The system and methods disclosed herein fall within the scope of numerous embodiments. The previous discussion is presented to enable a person skilled in the art to make and use embodiments of the system. Any portion of the structures and/or principles included in some embodiments can be applied to any and/or all embodiments: it is understood that features from some embodiments presented herein are combinable with other features according to some other embodiments. Thus, some embodiments of the system are not intended to be limited to what is illustrated but are to be accorded the widest scope consistent with all principles and features disclosed herein.
Some embodiments of the system are presented with specific values and/or setpoints. These values and setpoints are not intended to be limiting and are merely examples of a higher configuration versus a lower configuration and are intended as an aid for those of ordinary skill to make and use the system.
Furthermore, acting as Applicant's own lexicographer, Applicant imparts the additional meaning to the following terms:
“Substantially” and “approximately” when used in conjunction with a value encompass a difference of 5% or less of the same unit and/or scale of that being measured. In some embodiments, “substantially” and “approximately” are defined as presented in the specification in accordance with some embodiments.
“Simultaneously” as used herein includes lag and/or latency times associated with a conventional and/or proprietary computer, such as processors and/or networks described herein attempting to process multiple types of data at the same time. “Simultaneously” also includes the time it takes for digital signals to transfer from one physical location to another, be it over a wireless and/or wired network, and/or within processor circuitry.
The use of and/or, in terms of “A and/or B,” means one option could be “A and B” and another option could be “A or B.” Such an interpretation is consistent with the USPTO Patent Trial and Appeals Board ruling in ex parte Gross, where the USPTO Board established that “and/or” means element A alone, element B alone, or elements A and B together.
As used herein, some embodiments recited with term “can” or “may” or derivations there of (e.g., the system display can show X) is for descriptive purposes only and is understood to be synonymous with “configured to” (e.g., the system display is configured to show X) for defining the metes and bounds of the system.
The previous detailed description is to be read with reference to the figures, in which like elements in different figures have like reference numerals. The figures, which are not necessarily to scale, depict some embodiments and are not intended to limit the scope of embodiments of the system.
Any of the operations described herein that form part of the invention are useful machine operations. The invention also relates to a device or an apparatus for performing these operations. The apparatus can be specially constructed for the required purpose, such as a special purpose computer. When defined as a special purpose computer, the computer can also perform other processing, program execution or routines that are not part of the special purpose, while still being capable of operating for the special purpose. Alternatively, the operations can be processed by a general-purpose computer selectively activated or configured by one or more computer programs stored in the computer memory, cache, or obtained over a network. When data is obtained over a network the data can be processed by other computers on the network, e.g., a cloud of computing resources.
The embodiments of the invention can also be defined as a machine that transforms data from one state to another state. The data can represent an article, that can be represented as an electronic signal and electronically manipulate data. The transformed data can, in some cases, be visually depicted on a display, representing the physical object that results from the transformation of data. The transformed data can be saved to storage generally, or in particular formats that enable the construction or depiction of a physical and tangible object. In some embodiments, the manipulation can be performed by a processor. In such an example, the processor thus transforms the data from one thing to another. Still further, some embodiments include methods can be processed by one or more machines or processors that can be connected over a network. Each machine can transform data from one state or thing to another, and can also process data, save data to storage, transmit data over a network, display the result, or communicate the result to another machine. Computer-readable storage media, as used herein, refers to physical or tangible storage (as opposed to signals) and includes without limitation volatile and non-volatile, removable, and non-removable storage media implemented in any method or technology for the tangible storage of information such as computer-readable instructions, data structures, program modules or other data.
Although method operations are presented in a specific order according to some embodiments, the execution of those steps do not necessarily occur in the order listed unless a explicitly specified. Also, other housekeeping operations can be performed in between operations, operations can be adjusted so that they occur at slightly different times, and/or operations can be distributed in a system which allows the occurrence of the processing operations at various intervals associated with the processing, as long as the processing of the overlay operations are performed in the desired way and result in the desired system output.
It will be appreciated by those skilled in the art that while the invention has been described above in connection with particular embodiments and examples, the invention is not necessarily so limited, and that numerous other embodiments, examples, uses, modifications and departures from the embodiments, examples and uses are intended to be encompassed by the claims attached hereto.
This application is a Continuation-in-part of U.S. patent application Ser. No. 17/550,222, filed Dec. 14, 2021, which claims the benefit of and priority to U.S. Provisional Patent Application No. 63/253,820, filed Oct. 8, 2021, U.S. Provisional Patent Application No. 63/197,762, filed Jun. 7, 2021, and U.S. Provisional Patent Application No. 63/147,055, filed Feb. 8, 2021, the entire contents of each of which are incorporated by reference herein.
| Number | Date | Country | |
|---|---|---|---|
| 63253820 | Oct 2021 | US | |
| 63197762 | Jun 2021 | US | |
| 63147055 | Feb 2021 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17550222 | Dec 2021 | US |
| Child | 18825906 | US |
