Patent Description:
The described examples relate generally to systems and techniques for analyzing an electrical panel.

An electrical panel may operate to divide and distribute a main electrical input to various secondary circuits. Each secondary circuit may be associated with a circuit breaker and/or switch that electrically couples the secondary circuit to the main electrical input, and may also serve as an overcurrent protector (or other failsafe). The electrical panel may be capable of supporting numerous secondary circuits based on a variety of factors including panel loading. In-service electrical panels come in a variety of configurations, including having a number of secondary circuits that may be less than the maximum number of secondary circuits that the particular in-service panel is capable of supporting. As such, in-service electrical panels may be capable of supporting the electrical load and associated mechanical components for additional secondary circuits, such as a secondary circuit associated with an electric vehicle charging station. However, facility-specific electrical characteristics, such as existing loads, cable sizes, and service capacities may hinder the ability to determine whether another secondary circuit can be added and/or whether the load requirements of the circuit, such as that for an electrical vehicle charging station (or other use), are compatible with the existing infrastructure. There is a constant need for systems and techniques for analysis of electric panels.

Examples of the present invention are directed to systems and methods for analyzing an electrical panel.

In one example, a method of determining a capability of an electrical panel is disclosed. The method includes providing information relative to the electrical panel to a computer vision software. The method further incudes analyzing, using the computer vision software, an attribute of the electrical panel at least partially based on the information. The method further includes calculating an overall electrical power capacity of the electrical panel based at least in part on the attribute of the electrical panel. The method further includes calculating an electrical load on the electrical panel based at least in part on the attribute of the electrical panel. The method further includes generating a report comprising an unused electrical power capacity of the electrical panel at least partially based on the electrical load and the overall electrical power capacity of the electrical panel. The information provided to the computer vision software includes a digital image of the electrical panel. The digital image depicts a plurality of circuit breakers. Each circuit breaker of the plurality of circuit breakers has one or more symbols identifiable by the computing device.

In another example, analyzing the attribute of the electrical panel can comprise identifying characteristics of each electrical breaker electrically coupled to the electrical panel based on the one or more symbols. The characteristics of the electrical breaker can comprise at least one of a voltage rating, an amperage rating, a single pole configuration, a double pole configuration, and a tandem breaker configuration. In some cases, analyzing the attribute of the electrical panel at least partially based on the information includes at least one of: (i) identifying, using the computer vision software, operational characteristics of each circuit breaker based at least in part on the one or more symbols corresponding to each circuit breaker; and/or (ii) identifying, using the computer vision software, an amount of vacant breaker slots on the electrical panel; and/or (iii) identifying, using the computer vision software, a make, model, and/or type (e.g., residential, commercial, industrial) of the electrical panel; and/or (iv) identifying a geographic location at which the digital image was taken.

In another example, calculating the electrical load on the electrical panel may include estimating an average electrical power provided by the electrical panel to loads connected to the electrical panel. In some cases, calculating the overall electrical power capacity of the electrical panel is at least partially based on the operational characteristics of each circuit breaker and a number of vacant electrical breaker slots within the electrical panel.

In another example, calculating the unused electrical power capacity may include comparing the electrical load on the electrical panel to the overall electrical power capacity of the electrical panel. In some cases, estimating the average electrical power provided by the electrical panel is at least partially based on historical electrical power provided to the electrical panel. Further, the power capacity of the panel may be determined or influenced based on: (i) the rating of the panel itself based on the construction, the material, or design; (ii) the rating of the main breaker, typically sized based on the main service wires coming into the panel; and/or (iii) the main service wires coming in (e.g., the service capacity). In each of these cases, these are directly related to assessing whether an electrical service / panel upgrade is necessary in order to support additional loads, such as additional loads associated with electrical vehicle charging stations.

In another example, the method may further include transmitting, automatically, the report to at least one of: an owner of the electrical panel, an energy management provider, a municipality, or a supplier of electric vehicle charging equipment.

In another example, the method may further include requesting supplementary information associated with the electrical panel, such as requesting information from a utility, an owner of the panel, a municipality, and so on in order to supplement the electrical panel analysis, as described herein.

In another example, the method may further include training the computer vision software to identify a type of an electrical panel or a type of an electrical breaker.

In another example, a computing device for determining a capability of an electrical panel is disclosed. The computing device includes a processor. The computing device further includes a memory in electronic communication with the processor. The memory stores computer executable instructions that, when executed by the processor, cause the processor to perform the step of providing a digital image of the electrical panel to an image recognition module. The memory stores further computer executable instructions that, when executed by the processor, cause the processor to perform the step of identifying, using the digital image recognition module, an amperage rating of each electrical breaker of a plurality of electrical breakers disposed within the electrical panel. The memory stores further computer executable instructions that, when executed by the processor, cause the processor to perform the step of calculating an electrical capacity of the electrical panel based at least in part on the amperage rating of each electrical breaker. The memory stores computer executable instructions that, when executed by the processor, cause the processor to perform the step of generating a report comprising the capability of the electrical panel at least partially based on the electrical capacity of the electrical panel.

In another example, calculating the electrical capacity of the electrical panel may include weighting the amperage rating of each electrical breaker to determine a weighted amperage rating for each electrical breaker. Calculating the electrical capacity of the electrical panel may further include estimating an average power provided by each electrical breaker. Calculating the electrical capacity of the electrical panel may further include summing the average power provided by each electrical breaker to estimate a total average power provided by the electrical panel. Calculating the electrical capacity of the electrical panel may further include comparing the total average power to a maximum power output value of the electrical panel. The maximum power output value being at least partially based on one or more vacant electrical breaker slots within the electrical panel.

In another example, the memory may store further computer executable instructions that, when executed by the processor, cause the processor to perform the step of comparing the estimated total average power to historical power usage data of the electrical panel. The memory may store further computer executable instructions that, when executed by the processor, cause the processor to perform the step of identifying, using the image recognition module, a date the electrical panel was manufactured. The memory may store further computer executable instructions that, when executed by the processor, cause the processor to perform the step of identifying, using the image recognition module, a manufacturer of the electrical panel. The memory may store further computer executable instructions that, when executed by the processor, cause the processor to perform the step of identifying, using the image recognition module, a model of the electrical panel. The memory may store further computer executable instructions that, when executed by the processor, cause the processor to perform the step of identifying a construction date of a building to which the electrical panel provides power. The memory may store further computer executable instructions that, when executed by the processor, cause the processor to perform the step of identifying a type of building in which the electrical panel is disposed.

In another example, the report includes at least one of: (i) information relating to building code compliance or noncompliance; and/or (ii) information relating to modifications to the electrical panel required to support an electric vehicle charging circuit; and/or (iii) information relating to a type or quantity of electric vehicle charging circuits that can be supported by the electrical panel.

In another example, the digital image depicts the plurality of electrical breakers. Each electrical breaker of the plurality of electrical breakers may have one or more symbols identifiable by the image recognition software. In some cases, the processor is configured to rotate a portion of the digital image depicting the electrical breaker prior to identifying the one or more symbols.

In another example, a non-transitory computer-readable storage medium storing computer executable instructions is disclosed that, when executed by a processor, cause the processor to perform the step of providing information relative to an electrical panel to an image recognition software. The computer executable instructions further cause the processor to perform the step of analyzing, using the image recognition software, an attribute of the electrical panel at least partially based on the information. The computer executable instructions further cause the processor to perform the step of calculating an electrical power capacity of the electrical panel based at least in part on the attribute of the electrical panel. The computer executable instructions further cause the processor to perform the step of generating a report comprising the capability of the electrical panel at least partially based on the electrical capacity of the electrical panel. The information provided relative to the electrical panel includes a digital image of the electrical panel. The digital image depicts a plurality of circuit breakers. Each circuit breaker of the plurality of circuit breakers has one or more symbols identifiable by the image recognition software.

In another example, analyzing the attribute of the electrical panel may include identifying characteristics of each electrical breaker electrically coupled to the electrical panel based on the one or more symbols. The characteristics of the electrical breaker may include at least one of a voltage rating, an amperage rating, a single pole configuration, a double pole configuration, tandem breakers and/or substantially any other characteristic or component of the electrical panel. In some cases, the image recognition software may include a deep learning convolutional neural network model.

In another example, calculating the electrical capacity of the electrical panel may include determining a maximum electrical power output of the panel based at least in part on the attribute of the electrical panel. Calculating the electrical capacity of the electrical panel may further include estimating an average electrical power output of the electrical panel based at least in part on the attribute of the electrical panel. Calculating the electrical capacity of the electrical panel may further include comparing the maximum electrical power output to the average electrical power output to determine the electrical capacity. In some cases, estimating an average electrical power output of the electrical panel may be further based on an estimated seasonal load of an electrical breaker electrically coupled to the electrical panel.

In another example, the report may include a recommendation for modifying the electrical panel to increase the electrical capacity.

In addition to the exemplary aspects and embodiments described above, further aspects and embodiments will become apparent by reference to the drawings and by study of the following description.

The description that follows includes sample systems, methods, and apparatuses that embody various elements of the present disclosure. However, it should be understood that the described disclosure may be practiced in a variety of forms in addition to those described herein.

The following disclosure relates generally to systems and techniques for analyzing an electrical panel. An electrical panel may broadly encompass any collections of components configured to divide an input electrical power to multiple secondary circuits. Without limitation, the electrical panel may include or be or encompass an electrical box, switchgears, disconnect switches, breakers, overcurrent protectors (or other failsafe devices), grounding components, and associated circuity that cooperate to divide the input electrical power. In one example, the electrical panel may be a residential panel, a commercial panel, an industrial panel or substantially any other type of panel that may be associated with a use or structure.

An electrical panel of any type may be generally capable of supporting numerous secondary circuits based on multiple factors including panel loading. In-service electrical panels come in a variety of configurations and may have a number of secondary circuits that may be less than the maximum number of secondary circuits that the particular panel is capable of supporting. As such, in-service electrical panels may be capable of supporting the electrical load and associated mechanical components for additional secondary circuits, such as a secondary circuit associated with an electric vehicle charging station.

For example, electric vehicles may be charged by plugging in or otherwise electrically coupling the vehicle with a charging station. Electric vehicle use and adoption may be limited by the availability of the charging station or other infrastructure for transferring power to the vehicle. It may therefore be desirable to incorporate a charging station with an existing in-service electrical panel. However, facility-specific electrical characteristics of a given panel, such as existing loads, cable sizes, and service capacities may hinder the ability to determine whether another secondary circuit can be added and/or whether the load requirements of the circuit, such as that for an electrical vehicle charging station (or other use), are compatible with the panel.

The systems and techniques of the present disclosure may mitigate such hindrances, in part, by using image recognition and analysis to determine the condition of the panel and suitability of the panel for additional secondary circuits, such as those associated with a charging station. Broadly, an analysis module, such as that of a computer vision software, may be used to analyze the physical configuration of a given in-service panel. Based on the image of the existing equipment, the analysis module may assess facility-specific electrical characteristics such as existing loads, breaker characteristics, panel characteristics, cable sizes, and service capacities. Such assessment may provide information that can be used to determine the suitability of the panel to support a charging station. Sample elements analyzed by the analysis module include, without limitation, electrical load capacity, availability at the panel for additional circuits, utility service voltage, and amperage rating, among others. The image recognition, analysis, determination, and associated functions may be supported by artificial intelligence, including deep learning modules, in order to promote a streamlined and at-least semi-automated charging station installation process.

In one implementation, the systems and techniques described herein may use deep learning image recognition technology to recognize different areas of interest on the panel. Sample areas of interest may include, without limitation, a main switch, a single switch, a double pole switch, empty slots, and so on. Each area of interest may in turn be analyzed, such as through optical character recognition (OCR), to recognize important text of the area of interest, including text associated with an amperage of the recognized component. Particular attributes of electrical panel of interest may be calculated including, without limitation, an overall electrical power capacity, an electrical load of the panel, and an unused electrical power capacity. Electrical vehicle charging station specific logic may be implement in order to make a recommendation regarding whether a specific charging station (and/or use with a particular electrical vehicle) may be suitable for installation at the electrical panel of interest. Image metadata may also be used to enhance the analysis. For example, information can be optionally extracted from the image or associated data to identify various tags, such as global positioning system (GPS)/latitude-longitude coordinates, image settings, or dates, some or all of which may be used to evaluate regional specific attributes (e.g., utility, service/building type) or improve OCR by means of preprocessing image adjustments (e.g., detecting a language or regional library/dictionary of words or abbreviations in the image).

Reference will now be made to the accompanying drawings, which assist in illustrating various features of the present disclosure. The following description is presented for purposes of illustration and description. Furthermore, the description is not intended to limit the inventive aspects to the forms disclosed herein. Consequently, variations and modifications commensurate with the following teachings, and skill and knowledge of the relevant art, are within the scope of the present inventive aspects.

With reference to <FIG>, a block diagram of an electrical system <NUM> is depicted. The sample electrical system <NUM> may be associated with a residential, commercial, industrial, or other appropriate use. The systems and techniques described herein may be configured to analyze the electrical system <NUM> or components thereof for determining, among other items, whether an electric vehicle charging station may be added to the electrical system.

In the example of <FIG>, the electrical system includes a meter <NUM>, an electrical panel <NUM>, and secondary circuits <NUM>. The meter <NUM> may monitor a flow of electric input power to the electrical panel <NUM>. The meter <NUM> may therefore be associated with a main power supply or other power source, such as that associated with a power grid, solar / battery array, and so on. The electrical panel <NUM>, as described herein, includes any collection of components configured to divide the input electrical power and cause or allow the divided power to reach one or more secondary circuits, such as the secondary circuits <NUM> shown in <FIG>.

The secondary circuits <NUM> of <FIG> includes a first circuit 112a, a second circuit 112b, and a third circuit 112c. Each of the secondary circuits <NUM> may be used to supply electrical power to a designated area / for a designated purpose. In the case of a residential installation, each secondary circuit may serve a particular room (e.g., kitchen), collection of rooms (e.g., upstairs bedrooms), appliance (e.g., clothes dryers), and so on. In the example system <NUM>, the first circuit 112a, second circuit 112b, and the third circuit 112c may be existing, in-use secondary circuits of the electrical panel <NUM>. The electrical panel <NUM> may or may not be capable of supporting (e.g., physically with space requirements and electrically with respect to load) additional secondary circuits, such as one or more secondary circuits associated with an electrical vehicle charging station.

Accordingly, <FIG> shows charging circuit(s) <NUM> as optionally being associated with the secondary circuits <NUM>. The charging circuit(s) <NUM> may be representative of circuits used to provide electrical power to an electrical vehicle charging station. Because the electrical panel <NUM> of <FIG> is in-use, the charging circuit(s) <NUM> may not necessarily be appropriate for installation with the electrical panel <NUM>. For example, the electrical panel <NUM> may be physically unable to accommodate an additional breaker and associated components that would facilitate the division of the power supply by the electrical panel <NUM> to the charging circuit(s) <NUM>. As another example, the electrical panel <NUM> may be unable to safely accommodate an electrical load associated with the charging circuit(s) <NUM> / charging of an electric vehicle at the charging station.

The system and techniques of the present disclosure may be used to analyze an electrical panel, such as the electrical panel <NUM>, in order to determine whether the charging circuit(s) can be added. To facilitate the foregoing, <FIG> depicts a block diagram of a panel analysis module <NUM> according to examples of the present disclosure. The analysis module <NUM> may be configured to execute one or more functions described herein for analyzing the electrical panel. The analysis module <NUM> may be executed, in one implementation, via a computing device, including a mobile computing device, such as that described in greater detail below with reference to <FIG>.

The panel analysis module <NUM> is shown as including an image recognition module <NUM>, a data module <NUM>, a calculation module <NUM>, a reporting module <NUM>, and a communication module <NUM>. For purposes of illustration, the image recognition module <NUM> may be used to collect images of electrical panels. In some cases, the images may be used to initially establish a dataset that can be used to train the deep learning image detector and/or other artificial intelligence based learning module. In other cases, commercially available, crowdsourced, or otherwise preexisting datasets may be used. Numerous images of various different styles and manufacturers of electrical panels for deep learning training may increase the reliability and accuracy of panel detection. For example, the image recognition module <NUM> may be used to train the image detector to recognize various notable aspects or features of an electrical panel (e.g., main breaker switch, single pole breakers, empty slots, double pole breakers, and stamped voltage indicators). In some cases, a deep learning convolutional neural network (CNN) model may be used for image recognition.

In this regard, the image recognition module <NUM> may also be configured to process a new image of an in-service panel and recognize one or more features of the panel, in part, using the deep learning image detector, as trained based on the panel dataset. Initially, the image recognition module <NUM> may operate to analyze the new image of the in-service electrical panel and identify one or more panel features, such as the main breaker switch, single pole breakers, empty slots, double pole breakers described above. Each respective portion of the image including the panel features may be designated, such as via a bounding box or other marker that designates the portion of the image including the panel feature. Image recognition techniques may then be performed on the portion of the image bounded by the bounding box. For example, the panel feature may include text that includes information associated with the panel feature, such as a service amperage. Image recognition may be used to identify the text and associate the text with the panel feature represented in the bounding box.

Often the text is oriented at an angle other than horizontal. For example, the text may run the length of a switch handle that is generally perpendicular with the ground or horizontal direction. In some cases, the text may be oriented more than <NUM> degrees, due to a random and unpredictable combination of text distortion due to the angle of the switch itself and/or the source image itself (e.g., due to errors and inconsistency in the angle at which the image was captured). Accordingly, and shown and described herein with reference to <FIG>, the image recognition module <NUM> may operate to rotate the portion of the image (e.g., the portion bounded by the bounding box and representative of the identified panel feature) <NUM> degrees in both a clockwise manner and <NUM> degrees in a counterclockwise manner. Each of the three images may be analyzed to determine which orientation of text is most recognizable via optical character recognition.

The results of the image recognition module <NUM> may be stored in the data module <NUM>. For example, the image recognition module <NUM> may determine a value of main breaker amperage, a quantity and type of secondary breakers and associated amperage, as well as other values, such as a quantity of empty slots of the electrical panel, and other data points. In some cases, the data module <NUM> may also include data associated with a geographic location of the panel (e.g., including geolocation of the metadata), various parties associated with the panel (e.g., an owner of the electrical panel, an energy management provider, a municipality, or a supplier of electric vehicle charging equipment, and so on), a type of electrical panel or breaker (e.g., manufacture, size, rated wattage, age, and so on), a location of the panel (e.g., residential, commercial, industrial, and so) and substantially any other information that may be used to analyze the electrical panel in conjunction with the data collected and determined by the image recognition module <NUM>.

The calculation module <NUM> may broadly be configured to analyze the output of the image detector and text recognition and determine metrics associated with the electrical panel, including a main-to-actual amperage ratio, estimated panel power availability, and available breaker slots, among other metrics. Additionally, the calculation module <NUM> may be used to determine the meaning or associate recognized labels and text on the panel, for example, such as those that may give insight to approximate baseloads, peak, or seasonal loads. Example calculations of the calculation module <NUM> are described in greater detail below with reference to the electrical panel examples of <FIG> and <FIG>.

The reporting module <NUM> may be configured, in cooperation with the calculation module <NUM>, to propose one or more courses of action with respect to the electrical panel and/or with respect to whether an electrical vehicle charging station may be installed at the charging station. As one example, the reporting module <NUM> may determine whether an electrical panel upgrade is needed. This may be the case where the electrical panel may be unable to support the anticipated electrical load or other requirements of charging circuit(s) associated with a charging station. If the electrical panel is recommended for upgrade, the reporting module <NUM>, in part based on a street address of the panel and/or other geolocation marker, may determine the electric utility company, permitting authority entity, and/or other actions associated with a panel upgrade.

Additionally or alternatively, the reporting module <NUM> may propose that certain circuits be combined with tandem breakers in order to make room for additional circuits, approximate baseloads vs peak or seasonal loads from labels for engineers to further analyze, estimate panel service upgrade case, which may be based using the geolocation/satellite imagery. Other determinations may be made using the reporting module <NUM>, including but not limited to: (i) identifying the likelihood of another upstream load center in between the panel and service meter, (ii) parsing / classifying a panel make / model / type (e.g., residential, commercial, and so on) for purposes of determining compatible new hardware or the requirement of the panel needing to be replaced (due to new code requirements); and/or (iii) reading, using OCR, identification labels (including, for example, handwritten labels), paired with breaker size/configuration to estimate periodicity impact of the downstream load to the net service load; and/or (iv) reading, using OCR, the meter identifier(s) to use to correlate and request historical data from online data sources. In other cases, other insights may be determined. The communication module <NUM> may allow these and other determination to reported to members of a network, including an owner of the electrical panel, an energy management provider, a municipality, or a supplier of electric vehicle charging equipment, among others.

With reference to <FIG>, an analysis and evaluation of a sample electrical panel is shown and described according to the systems and techniques described herein. For example, <FIG> shows an image <NUM>. The image <NUM> may an image captured by a user, such as with a camera of a portable electronic or other device. The image <NUM> shows an electrical panel <NUM>, such as any of the electrical panels described herein. The electrical panel <NUM>, for purposes of illustration, may be a residential electrical panel with a 240V service voltage. The electrical panel <NUM> is shown as including a main breaker <NUM>. The main breaker <NUM> may operate as a main service disconnect. The electrical panel <NUM> is further shown as including a collection of secondary circuits which may be represented, visually, by a first single breaker 312a, a second single breaker 312b, a third single breaker 312c, a fourth single breaker 312d, a fifth single breaker 312e, a sixth single breaker 312f, and a <NUM>-pole breaker <NUM>. The electrical panel <NUM> is also shown with a collection of empty slots <NUM>. The empty slots <NUM> may be configured to accommodate addition breakers or other hardware to support additional secondary circuits, such as those associated with an electric vehicle charging station.

The systems and techniques described herein, for example, through operation of the panel analysis module <NUM>, may be configured to identify portions of the image <NUM> associated electrical components of the panel <NUM>. For example, and as described above, deep learning-based image recognition may be applied to the image <NUM> to identify which portion of the image may correspond to an electrical component (e.g., the main breaker <NUM>, breakers 312a-312f, and so on). With reference to <FIG>, a representation <NUM> of the image <NUM> is depicted in which a collection of overlays or bounding boxes (e.g., <NUM> and 332a-e) are shown over particular portions of the image. The bounding boxes may be a portion of the image <NUM> that the panel analysis module <NUM>, or computer vision software more generally, determines includes an image of an electrical component of the electrical panel <NUM>. For example, the panel analysis module <NUM> may determine a bounding box <NUM> as a portion of the image indicative of the main breaker <NUM>. As a further example, the panel analysis module <NUM> may determine a bounding box 332a as a portion of the image indicative of or associated with the first single breaker 312a. As a further example, the panel analysis module <NUM> may determine a bounding box 332b as a portion of the image indicative of the second single breaker 312b. As a further example, the panel analysis module <NUM> may determine a bounding box 332c as a portion of the image indicative of the third single breaker 312c. As a further example, the panel analysis module <NUM> may determine a bounding box 332d as a portion of the image indicative of the fourth single breaker 312d. As a further example, the panel analysis module <NUM> may determine a bounding box 332e as a portion of the image indicative of the fifth single breaker 312e. As a further example, the panel analysis module <NUM> may determine a bounding box 332f as a portion of the image indicative of the sixth single breaker 312f. As a further example, the panel analysis module <NUM> may determine a bounding box <NUM> as a portion of the image indicative of the <NUM>-pole breaker <NUM>. As a further example, the panel analysis module <NUM> may determine a collection of bounding boxes <NUM> as a portion of the image indicative of the collection empty slots <NUM>, including having bounding boxes for each individual slot, as shown in the example of <FIG>.

One result of the analysis of <FIG> may be the indication of the number and type of electrical components of the electrical panel <NUM>. The panel analysis module <NUM> may further operate to determine information for each of the electrical components, such as amperage or other value that may be used to assess the suitability of the panel for additional secondary circuits. As one example, a portion of the image represented by the bounding box is analyzed for numerical information. The numerical information may then be associated with a property or characteristic of the identified electrical component.

For example, and as shown in <FIG>, a portion of image <NUM> represented by the bounding box <NUM> is shown. As described above, the bounding box <NUM> includes a portion of the image <NUM> indicative of the <NUM>-pole breaker <NUM>. The <NUM>-pole breaker <NUM> may include a handle <NUM> having a first text portion 318a and second text portion 318b associated therewith. In this case, each of the first text portion 318a and the second text portion 318b include the text "<NUM>," which may be indication of a <NUM> amp service amperage. As shown in <FIG>, the text portions 318a, 318b are arranged along a text axis <NUM>. The text axis <NUM> may be arranged at an angle from a horizontal axis <NUM> (e.g., a horizontal axis of the image) by an angle σ. The angle σ may be larger than ninety degrees, due to a number of factors including, for example, the orientation of the captured image, lens distortion, and the orientation of the handle <NUM> in the electrical panel <NUM>. The arrangement of the text of the first and second text portion 318a, 318b along the text axis <NUM> at the angle σ may reduce the accuracy of the optical character recognition to recognize the text as "<NUM>.

Accordingly, and as described herein, the panel analysis module <NUM> may operate to rotate the image within bounding box <NUM> in a first clockwise direction a produce a first modified bounding box <NUM>', as shown in <FIG>. The first modified bounding box <NUM>' may be a ninety degree rotation, clockwise, of the bounding box <NUM>. As shown in <FIG>, the rotation causes the text axis <NUM> to correspondingly rotate. The text axis <NUM> is therefore shown in <FIG> at an almost parallel orientation to the horizontal axis <NUM> with the numerals "<NUM>" generally in an upright position. By re-orientating the image in this manner, the accuracy of the optical character recognition may be enhanced. For example, the optical character recognition is more likely to recognize the text portions 318a, 318b as including the text "<NUM>" in the orientation of the first modified bounding box <NUM>' as compared with that of the bounding box <NUM> of the original orientation.

The panel analysis module <NUM> may also operate to rotate the image within bounding box <NUM> in a second counterclockwise direction to produce a second modified bounding box <NUM>", as shown in <FIG>. The second modified bounding box <NUM>" may be a ninety degree rotation, counterclockwise, of the bounding box <NUM> shown in <FIG>. As shown in <FIG>, the rotation causes the text axis to correspondingly rotate. The text axis <NUM> is therefore shown in <FIG> at an almost parallel orientation to the horizontal axis <NUM>. However, with the rotation of the bounding box in this manner, the numerals "<NUM>" are generally in an upside-down position. As such, the optical character recognition may be unable to determine the specific text of the first and second text portions 318a, 318b. The panel analysis module <NUM> may therefore compare a result of the image recognition for the bounding box <NUM>, first modified bounding box <NUM>', and the second modified bounding box <NUM>" to determine which orientation is best suited for determining a value of the text portions 318a, 318b as including the text "<NUM>.

The panel analysis module <NUM> may further operate to associate the text "<NUM>" with an amperage value of the breaker <NUM>. For example, the panel analysis module <NUM>, using the image-recognition techniques described above, may identify the text "<NUM>" as being the physically largest markings on the breaker <NUM>. The panel analysis module <NUM> may apply a set of rules to associate the physically largest text or markings on the breaker <NUM> as text that is indicative of a service amperage of the breaker <NUM>. Additionally or alternatively, the panel analysis module <NUM>, using the image-recognition techniques described above, may identify the text "<NUM>" as being arranged on the handle <NUM>. The panel analysis module <NUM> may then apply a set of rules to detect a handle in the image and to associate the text positioned on the handle as text that is indicative of a service amperage of the breaker <NUM>. In some cases, where multiple numbers are detected, the panel analysis module <NUM> may filter numbers for possible standard amperages (e.g., <NUM>, <NUM>, <NUM>, <NUM>, and so) and select the number that is the largest if multiple numbers remain. Additionally or alternatively, the panel analysis module <NUM> may obtain information associated with the location of the amperage information based on the panel type, brand, make and so on. Additionally or alternatively, a Convolution Neural Network (or other related software) may be used in connection with the panel analysis module <NUM> to analyze the region of/around the breaker switch and detect/draw a bounding box around the "amperage indication area. " This bounding box may be defined on the breaker itself (e.g. on the switch) or near the breaker (e.g. a label adjacent to the breaker), as appropriate for a given application.

The panel analysis module <NUM>, using the calculation module <NUM> and/or various other modules, may proceed to associate the recognized value of the text with a characteristic of the associated electrical component of the bounding box for which the text was recognized within. In the present example, the panel analysis module <NUM> may recognize the text and as amperage values for breaker components as appear in Table <NUM>.

The panel analysis module <NUM> may use the values represented in Table <NUM> to calculate, among other items, an overall electrical power capacity of the electrical panel, an electrical load of the panel, and suitability of the electrical panel for additional secondary circuits, such as circuits associated with a charging station. Broadly, while many calculations are possible and contemplated herein, for purposes of illustration, an example set of calculations is presented herein below to demonstrate the functionality of the calculation module <NUM> and panel analysis module <NUM> more generally. In this regard, rather than limiting, it will be appreciated that such calculations are illustrative of the calculation module <NUM> operating in conjunction with the other module of the analysis module <NUM> to analyze an electrical panel and determine one or more characteristics of the panel.

In one example, the baseline load of the electrical may be defined as:.

In other cases, other weighting metrics may be used for determining a baseline load or other pattern usage estimate technique.

The maximum load for the electrical panel may be defined as:
Min [ Σ(Double pole breaker loads, single pole breaker loads >= 30A, <NUM>. 5factor single pole breaker loads < 30A), Summation of breaker powers ].

In other cases, other calculations may be used to determine a maximum load of the electrical panel.

The panel analysis module <NUM>, using the calculation module <NUM> and/or various other modules, may proceed to calculate power associated with each breaker. In the present example, the panel analysis module <NUM> may, using a safety factor of <NUM>, calculate power associated with each breaker, as appears in Table <NUM>.

The assumed 240V of Table <NUM> may be determined or obtained via direct input and/or by analyzing a variety of factors, such as panel location, image location, facility type, panel board characteristics, and so on.

The panel analysis module <NUM>, using the calculation module <NUM> and/or various other modules, may proceed to calculate the maximum load of the electrical panel. The steps of such example appear in Table <NUM>.

The panel analysis module <NUM>, using the calculation module <NUM> and/or various other modules, may proceed to determine a variety of calculate attributes associated with the electrical panel that appear in Table <NUM>.

The service voltage of <NUM> V may be determined or assumed based on the electrical panel being a residential panel. The panel may be determined to be a residential panel based on identified panel information (make/model/lineup) and/or GPS info extracted from the image and then referenced or recognized in a panel library/database or looked up through similar channels.

The information represented in Table <NUM> may be used by the analysis module to evaluate the suitability of the electrical panel <NUM> for additional secondary circuits. For example, the panel analysis module <NUM> may compare the estimated available capacity with the estimated capacity required for one or more secondary circuit associated with the charging station. If the electrical panel is found to not support additional secondary circuits, the panel analysis module <NUM> may generate one or more reports to recommend courses of action for upgrading the panel.

It will be appreciated that the analysis of <FIG> is described with respect to one example electrical panel. The systems and techniques described herein are configured to analyze a plurality of electrical panels of various different types and configurations, including residential, commercial, and industrial panels having different components and characteristics. In this regard, the systems and techniques may be used to determine one or more panel characteristics, including available slots and power capacity, for adaptation for electrical vehicle charging stations in a variety of settings.

For purposes of illustration, another example analysis and evaluation of a sample electrical panel is shown with reference to <FIG> and <FIG>. With reference to <FIG>, an image <NUM> is shown including an electrical panel <NUM>. The electrical panel <NUM> may be a different type of electrical panel and/or have different characteristics (e.g., with respect to panel components, capacity, service-type, and so on) as compared with the panel <NUM>. For example, the electrical panel <NUM> may include a breaker <NUM>. The breaker <NUM> may be a different type of breaker, including having a different amperage or rating as compared with the breakers shown and described with reference to <FIG>. The electrical panel <NUM> may also include a different number of breakers or other components, which may occupy a larger amount of space on the electrical panel <NUM>, for example, due to a service-type of the panel <NUM>.

Notwithstanding the foregoing distinctions, the systems and techniques described in relation to <FIG> may be used to analyze the panel <NUM> and determine one or more characteristics of the panel <NUM>. For example, the panel analysis module <NUM> described herein, may be configured to identify portions of the image <NUM> that are associated with electrical components of the panel <NUM>. The deep learning-based image recognition may be applied to the image <NUM> to identify which portion of the image may correspond to an electrical component (e.g., the breaker <NUM>). With reference to <FIG>, the image <NUM> may be overlaid with a bounding box <NUM>. The bounding box <NUM> may be a portion of the image <NUM> that the panel analysis module <NUM>, or computer vision software more generally, determines includes an image of an electrical component of the electrical panel <NUM>, such as being a portion of the image <NUM> indicative of the breaker <NUM>.

The panel analysis module <NUM> may further operate to determine information for each of the identified electrical components of the panel <NUM>, such as the breaker <NUM>. For example, and as shown in <FIG>, a detail view of the portion of the image <NUM> represented by the bounding box <NUM> is shown. As shown in <FIG>, the breaker <NUM> may include a handle <NUM> and a text portion <NUM>. The text portion <NUM> may indicate one or more characteristics of the breaker <NUM>, such as amperage. The panel analysis module <NUM> may be configured to identify the text portion <NUM> as including the text "<NUM>," which may be indicative of a <NUM> amp service amperage. The text portion <NUM> may be arranged along a text axis <NUM> that is at an angle that is greater than <NUM> degrees from a horizontal axis <NUM>. In this regard, the panel analysis module <NUM> may be configured to rotate the bounding box <NUM> in order to increase an accuracy of the optical character recognition of the text portion <NUM> to recognize the text as "<NUM>," substantially analogous to the techniques described in relation to <FIG>.

The panel analysis module <NUM>, using the calculation module <NUM> and/or various other modules, may further proceed to associate the recognized value of the text with a characteristic of the associated electrical component of the bounding box for which the text was recognized within. In the present example, the panel analysis module <NUM> may recognize the text as amperage values for breaker component <NUM>. The panel analysis module <NUM> may conduct similar image recognition operations for each identified component of the panel. The panel analysis module <NUM> may further use the characteristic of the identified component to calculate, among other items, an overall electrical power capacity of the electrical panel, an electrical load of the panel, and suitability of the electrical panel for additional secondary circuits, such as circuits associated with a charging station.

To facilitate the reader's understanding of the various functionalities of the embodiments discussed herein, reference is now made to the flow diagram in <FIG> and <FIG>, which illustrates processes <NUM> and <NUM>, respectively. While specific steps (and orders of steps) of the methods presented herein have been illustrated and will be discussed, other methods (including more, fewer, or different steps than those illustrated) consistent with the teachings presented herein are also envisioned and encompassed with the present disclosure.

With reference to <FIG>, a method <NUM> is shown directed to determining a capability of an electrical panel. At operation <NUM>, a panel analyzing software is optionally trained to identify types of electrical panels and related components. For example, and with reference to <FIG>, the image recognition module <NUM> may be trained using a collection of images of electrical panels. In some cases, the images may be used to initially establish a dataset that can be used to train the deep learning image detector. Training images of electrical panel components may also be used.

At operation <NUM>, information is provided to the panel analyzing software associated with or relative to the electrical panel. For example, and with reference to <FIG>, the image recognition module <NUM> may receive information associated with a new image of an in-service electrical panel. The image may be captured using a camera of an electronic device, including a portable electronic device.

At operation <NUM>, attributes of the electrical panel are analyzed, using the panel analyzing software, at least partially based on the information. For example, and with reference to <FIG> and <FIG>, the panel analysis module <NUM> is configured to analyze the panel <NUM> and determine various electric components of the panel <NUM>. As shown in <FIG>, the panel analysis module <NUM> may recognize one or more switches, breakers or other components. The panel analysis module <NUM> may designate a portion of the image as a portion including the identified electrical component (e.g., designated by a bounding box or other overlay). As described herein, the designated portion of the image may be rotated in various manner in order to identify any text included in that portion of the image.

At operation <NUM>, an electrical capacity of the electrical panel is calculated using the panel analysis software. For example, and with reference to <FIG> and <FIG>, the panel analysis module <NUM> may be used to determine an electrical capacity of the electrical panel. For example, the panel analysis module <NUM> may associate recognized text values with a characteristic of an electrical panel component (e.g., a "<NUM>" corresponding to a service amperage of <NUM> amps for a breaker included in the respective image). The panel analysis module <NUM> may use this information as input for one or more algorithms for determining an electrical panel capacity, as described herein in relation to <FIG>.

At operation <NUM>, a report is generated based on the electrical capacity of the electrical panel. The report may include information associated with the capacity of the electrical panel. The report may further include information associated with an evaluation of whether the electrical panel could support one or more additional secondary circuits. At operation <NUM>, the report is optionally transmitted. The report may be transmitted to an owner of the electrical panel, an energy management provider, a municipality, or a supplier of electric vehicle charging equipment, among other appropriate parties.

With reference to <FIG>, a method <NUM> is shown directed to analyzing an electrical panel and evaluating the panel for a potential upgrade. At operation <NUM>, information is provided to a panel analyzing software that is associated with or relative to the electrical panel. For example, and with reference to <FIG>, the image recognition module <NUM> may be trained using a collection of images of electrical panels. In some cases, the images may be used to initially establish a dataset that can be used to train the deep learning image detector. Training images of electrical panel components may also be used.

At operation <NUM>, attributes of the electrical panel are analyzed, using the panel analyzing software, at least partially based on the information. For example, and with reference to <FIG>, the image recognition module <NUM> may receive information associated with a new image of an in-service electrical panel. The image may be captured using a camera of an electronic device, including a portable electronic device.

At operation <NUM>, an electrical capacity of the electrical panel is calculated, using the panel analyzing software, based at least in part on the attributes of the electrical panel. For example, and with reference to <FIG> and <FIG>, the panel analysis module <NUM> may be used to determine an electrical capacity of the electrical panel. For example, the panel analysis module <NUM> may associate recognized text values with a characteristic of an electrical panel component (e.g., a "<NUM>" corresponding to a service amperage of <NUM> amps for a breaker included in the respective image). The panel analysis module <NUM> may use this information as input for one or more algorithms for determining an electrical panel capacity, as described herein in relation to <FIG>.

At operation <NUM>, a determination is made as to whether the panel needs to be upgraded, in part, based on the output from one or more of the foregoing operations <NUM>, <NUM>, <NUM>. For example, the estimated available capacity of the electrical panel may be determined. Further, physical attributes associated with the panel, such as the presence and/or number of empty slots of the panel may also be determined. Based on these and other factors, the panel analysis module <NUM> may evaluate whether the electrical panel has sufficient available capacity to support the capacity of an additional secondary circuit associated with a charging station. The panel analysis module <NUM> may further evaluate whether the electrical panel had sufficient empty slots or other physical components to accommodate the physical components associated with the additional secondary circuit.

If it is determined that an updated electrical panel is required, for example, due to a lack of electrical capacity and/or lack of empty slots, the method <NUM> may proceed to operation <NUM>. At operation <NUM>, an estimate for a panel upgrade is determined. Factors, such as size, location, and history of the panel, as well as future intended use of the upgraded panel may be factored into the estimate. The method <NUM> may further proceed to operation <NUM>, at which the estimate is sent.

If it is determined that an updated electrical panel is not required, for example, due to surplus of electrical capacity and/or surplus of empty slots, the method <NUM> may proceed to operation <NUM>. At operation <NUM>, a report including the capability of the electrical panel to support the electrical vehicle charging circuit at least partially based on the electrical capacity of the electrical panel is generated. The report may include any of the reports and associated characteristics and metric described above in relation to the reporting module <NUM> of <FIG>. At operation <NUM>, the report is optionally transmitted, automatically, to at least one of an owner of the electrical panel, a contractor, a municipality, or a supplier / installer of electrical vehicle charging equipment.

For purposes of illustration, a sample report <NUM> is shown in <FIG>. The report <NUM> may be a user interface displayed on an electronic device, such as a portable computer, including a smart phone, tablet, laptop, and/or other device. The report <NUM> may generally indicate results or outputs of the electrical panel analysis as described herein, including available breaker slots, an overall electrical power capacity of the electrical panel, an electrical load of the panel, and suitability of the electrical panel for additional secondary circuits, such as circuits associated with a charging station. In this regard, while <FIG> shows example results or outputs of the electrical panel analysis, this is for purposes of illustration, and in other cases, the report <NUM> may include different information.

In <FIG>, the example report <NUM> includes a panel component section <NUM>, a panel analysis section <NUM>, and a power capacity section <NUM>. The panel component section <NUM> may include information associated with a type of electrical component or equipment that is installed in a given electrical panel of interest. For example, and as shown in <FIG>, the panel component section <NUM> is shown as including a breaker listing <NUM> and an associated amperage listing <NUM>. The breaker listing <NUM> may include a listing of each electrical component of the given electrical panel, e.g., a listing of each breaker, and an indication of the type of breaker, such as a main breaker, a double breaker, a single breaker, and/or other characteristic. The amperage listing <NUM> may include numerical values corresponding to the amperage for each breaker listed in the breaker listing <NUM>. For purposes of illustration, the panel component section <NUM> is shown as including data <NUM> that populates the breaker listing <NUM> and the amperage listing <NUM> in the present example. The data <NUM> may be data or results or other outputs from one or more operations of the panel evaluation and analysis systems and techniques described herein. For example, the data <NUM> may include a listing of electrical components and associate amperage values based on the deep-learning and image recognition techniques described herein. For example, the data <NUM> shows a first data row 808a listing a "main" value in the breaker listing <NUM> and a "<NUM>" value in the amperage listing <NUM>. The "main" and "<NUM>" values may be the result of the image recognition techniques determining that a portion of an image corresponds to a main breaker and that the main breaker has text indicating <NUM> that is or otherwise corresponds to an amperage of the main breaker. The panel component section <NUM> may also include a spare slot output <NUM> which may be indicative of a number of space breaker slots in the given electrical panel. A technician may view the panel component section <NUM> in order to determine a configuration of the electrical components of the panel.

The report <NUM> is further shown with the panel analysis section <NUM>. The panel analysis section <NUM> may include information associated with one or more calculations of the electrical panel, based in part, on the image recognition techniques described herein. For example, using image recognition, the amperage or other characteristic of the electrical panel components may be determined. The characteristics may be used, optionally in conjunction with other information, to calculate various capacity metrics <NUM>, such as a total main service, a service / main power, a baseload, a power available, a power demand, a current available, and a main service voltage, among other characteristics.

The report <NUM> is further shown with a power capacity section <NUM>. The power capacity section <NUM> may generally provide a visual indication of the available power capacity for an electrical panel of interest. For example, based on the various capacity metrics <NUM>, a power demand 844a and a power available 844b may be determined. In some cases, as shown in <FIG>, this power demand 844a and the power available 844b may be represented by a graph. In this regard, a technician may determine the available capacity of the electrical panel may viewing the graph. A technician may further make a determination regarding the suitability of the electrical panel for one or more electric vehicle charging stations by comparing the available capacity to the additional load of the charging stations, as well as with reference to the spare slot output <NUM>.

<FIG> depicts an example schematic diagram of a computer system <NUM> for implementing various techniques in the examples described herein. A computer system <NUM> may be used to implement the panel analysis module <NUM> (in <FIG>), the representation <NUM> (in <FIG>), and/or execute the methods <NUM> or <NUM> (in <FIG> and <FIG>, respectively). More generally, the computer system <NUM> is used to implement or execute one or more of the components or operations disclosed in <FIG>. In <FIG>, the computer system <NUM> may include one or more processing elements <NUM>, an input/output interface <NUM>, a display <NUM>, sensor(s) <NUM>, one or more memory components <NUM>, a network interface <NUM>, and one or more external devices <NUM>. Each of the various components may be in communication with one another through one or more buses, communication networks, such as wired or wireless networks.

The processing element <NUM> may be any type of electronic device capable of processing, receiving, and/or transmitting instructions. For example, the processing element <NUM> may be a central processing unit, microprocessor, processor, or microcontroller. Additionally, it should be noted that some components of the computer <NUM> may be controlled by a first processor and other components may be controlled by a second processor, where the first and second processors may or may not be in communication with each other.

The memory components <NUM> are used by the computer <NUM> to store instructions for the processing element <NUM>, as well as store data, such as data associated with an electrical panel (<FIG>) and the like. The memory components <NUM> may be, for example, magneto-optical storage, read-only memory, random access memory, erasable programmable memory, flash memory, or a combination of one or more types of memory components.

The display <NUM> provides visual feedback to a user. Optionally, the display <NUM> may act as an input element to enable a user to control, manipulate, and calibrate various components of the route-based precondition system <NUM> or vehicle <NUM>. The display <NUM> may be a liquid crystal display, plasma display, organic light-emitting diode display, and/or other suitable display. In embodiments where the display <NUM> is used as an input, the display may include one or more touch or input sensors, such as capacitive touch sensors, a resistive grid, or the like.

The I/O interface <NUM> allows a user to enter data into the computer <NUM>, as well as provides an input/output for the computer <NUM> to communicate with other devices or services. The I/O interface <NUM> can include one or more input buttons, touch pads, and so on. The I/O interface <NUM> may be an interface of a portable electrical device, such as a smart phone. In some cases, the I/O interface <NUM> may be a keyboard, accessory, or other components that is associated or associateable with a computing device and capable of receiving a user input.

The computer <NUM> may also include one or more sensors <NUM> that may be used to detect a touch and/or force input, environmental condition, orientation, position, or some other aspect of the computer <NUM>. In this regard, the sensors <NUM> may be used to detect an input at a touch-sensitive display (e.g., display <NUM>) and/or other surface or feature, such as an external surface of the computer device <NUM> defined by an outer enclosure or shell. Example sensors <NUM> include, without limitation, one or more accelerometers, gyrometers, inclinometers, goniometers, or magnetometers. The sensors <NUM> may also include one or more proximity sensors, such as a magnetic hall-effect sensor, inductive sensor, capacitive sensor, continuity sensor, or the like. Resistive and contact-based sensors may also be used.

The one or more sensors <NUM> may also include a camera that is configured to capture a digital image or other optical data, such as an image of a panel and optical data associated with characteristics of the panel (e.g., switches, breakers, and so on). The camera may include a charge-coupled device, complementary metal oxide (CMOS) device, or other device configured to convert light into electrical signals. The camera may also include one or more light sources, such as a strobe, flash, or other light-emitting device. The camera may be generally categorized as a sensor for detecting optical conditions and/or objects in the proximity of the computing device <NUM>. The camera may also be used to create photorealistic images that may be stored in an electronic format, such as JPG, GIF, TIFF, PNG, raw image file, or other similar file types. The camera may be integrated with the electronic device <NUM>, such as where the camera is an onboard camera of a smart phone or other portable electronic device.

The network interface <NUM> provides communication to and from the computer <NUM> to other devices. The network interface <NUM> includes one or more communication protocols, such as, but not limited to WiFi, Ethernet, Bluetooth, and so on. The network interface <NUM> may also include one or more hardwired components, such as a Universal Serial Bus (USB) cable, or the like. The configuration of the network interface <NUM> depends on the types of communication desired and may be modified to communicate via WiFi, Bluetooth, and so on. The external devices <NUM> are one or more devices that can be used to provide various inputs to the computing device <NUM>, e.g., mouse, microphone, keyboard, trackpad, or the like.

The external devices <NUM> may be local or remote and may vary as desired. In some examples, the external devices <NUM> may also include one or more additional sensors.

The foregoing description has a broad application. For example, while examples disclosed herein may focus on central communication system, it should be appreciated that the concepts disclosed herein may equally apply to other systems, such as a distributed, central or decentralized system, or a cloud system. For example, some components may reside on a server in a client/server system, on a user mobile device, or on any device on the network and operate in a decentralized manner. One or more components of the systems may also reside in a controller virtual machine (VM) or a hypervisor in a VM computing environment. Accordingly, the disclosure is meant only to provide examples of various systems and methods and is not intended to suggest that the scope of the disclosure, including the claims, is limited to these examples.

The technology described herein may be implemented as logical operations and/or modules in one or more systems. The logical operations may be implemented as a sequence of processor-implemented steps directed by software programs executing in one or more computer systems and as interconnected machine or circuit modules within one or more computer systems, or as a combination of both. Likewise, the descriptions of various component modules may be provided in terms of operations executed or effected by the modules. The resulting implementation is a matter of choice, dependent on the performance requirements of the underlying system implementing the described technology. Accordingly, the logical operations making up the embodiments of the technology described herein are referred to variously as operations, steps, objects, or modules. Furthermore, it should be understood that logical operations may be performed in any order, unless explicitly claimed otherwise or a specific order is inherently necessitated by the claim language.

In some implementations, articles of manufacture are provided as computer program products that cause the instantiation of operations on a computer system to implement the procedural operations. One implementation of a computer program product provides a non-transitory computer program storage medium readable by a computer system and encoding a computer program. It should further be understood that the described technology may be employed in special purpose devices independent of a personal computer.

Other examples and implementations are within the scope of the appended claims. For example, features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations. Also, as used herein, including in the claims, "or" as used in a list of items prefaced by "at least one of" indicates a disjunctive list such that, for example, a list of "at least one of A, B, or C" means A or B or C or AB or AC or BC or ABC (i.e., A and Band C). Further, the term "exemplary" does not mean that the described example is preferred or better than other examples.

Claim 1:
A method of determining a capability of an electrical panel, the method comprising:
providing information relative to the electrical panel to a computer vision software;
analyzing, using the computer vision software, an attribute of the electrical panel at least partially based on the information;
calculating an overall electrical power capacity of the electrical panel based at least in part on the attribute of the electrical panel;
calculating an electrical load on the electrical panel based at least in part on the attribute of the electrical panel; and
generating a report comprising an unused electrical power capacity of the electrical panel at least partially based on the electrical load and the overall electrical power capacity of the electrical panel;
wherein the information provided to the computer vision software comprises a digital image of the electrical panel, the digital image depicting a plurality of circuit breakers, each circuit breaker of the plurality of circuit breakers having one or more symbols identifiable by a computing device.