Description:SYSTEM FOR AUTOMATICALLY CONFIGURING A SOLAR MODULE MOUNTING STRUCTURE
FIELD OF INVENTION
[0001] The present disclosure relates to solar photovoltaic (PV) module mounting structures and automated planning tools for solar module installation, and more particularly to an integrated and automated system and method for configuring solar module mounting structures for specific installation sites.
BACKGROUND
[0002] The design and installation of solar module mounting structures presents a complex technical challenge. Optimizing the structural configuration and layout of solar modules for specific installation sites involves integrating multiple technical factors including geospatial data, structural engineering principles, and energy production modelling. Current approaches often rely on manual, disconnected processes that may lead to suboptimal designs.
[0003] Existing methods for designing solar mounting structures typically involve separate phases for site surveying, structural analysis, component selection, and energy modelling. These discrete steps are often performed using different tools and by different specialists. The lack of integration between phases can result in designs that fail to fully account for site factors or structural constraints.
[0004] Key challenges with current approaches include difficulty in optimizing designs across multiple technical domains, inconsistency in methodologies between projects, and inability to leverage machine learning to improve designs over time. There is a need for integrated and automated systems that can holistically optimize solar mounting structures by simultaneously considering structural, geospatial, and energy production factors.
SUMMARY
[0005] Before the present system(s) and method(s), are described, it is to be understood that this application is not limited to the particular system(s), and methodologies described, as there can be multiple possible embodiments that are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular implementations or versions or embodiments only and is not intended to limit the scope of the present application. This summary is provided to introduce aspects related to an automatically configuring a solar module mounting structure system. This summary is not intended to identify essential features of the claimed subject matter, nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[0006] According to an aspect of the present disclosure, a system for automatically configuring a solar module mounting structure may be provided. The system may include a memory and a processor coupled to the memory. The processor may be configured to execute instructions stored in the memory for receiving site data for a solar module installation site. The processor may perform simulations based on the site data. The simulations may include structural load simulations and solar irradiance simulations. The processor may generate a configuration for a solar module mounting structure based on results of the simulations. Generating the configuration may include generating a list of components required for the solar module mounting structure based on results of the simulations and determining strength of the components from the list of components. The processor may validate structural integrity of the configuration based on site data and simulation results. The processor may generate installation instructions for the solar module mounting structure based on the configuration. The system may acquire geographic information systems (GIS) data, drone survey data, and 3D modelling data of the solar module installation site. The structural load simulations may include wind load analysis, snow load calculations, and weight distribution assessments. The solar irradiance simulations may calculate optimal inclination angles for rafters and orientation of solar modules to maximize energy generation. The system may apply machine learning algorithms to recognize patterns and adaptively learn from historical installation data. The list of components may include columns, rafters, and purlins for a modular mounting framework. The system may select components of varying strengths based on the structural load simulations. The system may perform finite element analysis (FEA) on the configuration. The system may generate a 3D visualization of the solar module mounting structure based on the configuration. The installation instructions may include detailed component lists, assembly instructions, and layout schematics. The system may optimize the configuration to maximize energy yield based on the solar irradiance simulations. The system may generate recommendations for future installations based on performance data collected from installed solar module mounting structures. The system may adjust the configuration in real-time based on user input through a graphical user interface. The system may generate a cost estimate for the solar module mounting structure based on the configuration and current market prices for components. The system may integrate weather forecast data to optimize the configuration for local climate conditions.
[0007] According to another aspect of the present disclosure, a method for automatically configuring a solar module mounting structure may be provided. The method may include receiving, by a processor, site data for a solar module installation site. The processor may perform simulations based on the site data. The simulations may include structural load simulations and solar irradiance simulations. The processor may generate a configuration for a solar module mounting structure based on results of the simulations. Generating the configuration may include generating a list of components required for the solar module mounting structure based on results of the simulations and determining strength of the components from the list of components. The processor may validate structural integrity of the configuration based on site data and simulation results. The processor may generate installation instructions for the solar module mounting structure based on the configuration.
[0008] According to yet another aspect of the present disclosure, an apparatus for mounting solar modules may be provided. The apparatus may include a plurality of columns configured to be anchored to a surface. The plurality of columns may include at least one of front columns having a first height, middle columns having a second height, back columns having a third height. The apparatus may include a plurality of uniform rafters configured to be attached to the columns. The rafters may be connected to the columns such that the rafters form a bridge between the at least one of the front, the middle, and the back columns. The apparatus may include a plurality of purlins configured to be attached to the rafters. The plurality of purlins may be connected laterally across multiple rafters, forming a grid-like structure. The apparatus may include a plurality of interconnecting braces between the columns and rafters to enhance lateral stability. The apparatus may include a centralized junction box mounted on one of the columns for aggregating electrical connections from multiple solar modules. The solar modules may be mounted on the plurality of purlins. In an embodiment, the apparatus may include a grid of mounting points formed by the intersections of the rafters and purlins, providing secure attachment locations for the solar modules. The plurality of columns may include a corrosion-resistant outer coating, vertical flanges with pre-formed mounting holes, and an internal reinforcement structure to enhance load-bearing capacity. The plurality of rafters may include standardized slots and fasteners for secure attachment to the columns, an internal hollow channel for routing electrical conduits, and a lightweight, high-strength alloy composition to maximize structural integrity while minimizing weight. The plurality of purlins may include pre-drilled holes for cable management and assembly, adjustable mounting brackets to accommodate various solar module sizes, and integrated and automated grounding mechanisms compliant with electrical codes. The apparatus may include adjustable feet attached to the base of each column to accommodate uneven surfaces and allow for fine-tuning of the structure's levelness post-installation. The centralized junction box may include weatherproof housing, surge protection devices, and a monitoring system for tracking the performance of individual solar modules. The apparatus may include an integrated and automated weather monitoring system comprising sensors attached to select columns to provide real-time environmental data for system optimization. The grid-like structure formed by the rafters and purlins may be configurable to support solar modules in both portrait and landscape orientations.
[0009] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.
BRIEF DESCRIPTION OF FIGURES
[0010] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating of the present subject matter, an example of a construction of the present subject matter is provided as figures, however, the invention is not limited to the specific method and system for processing holdings data of a portfolio manager disclosed in the document and the figures.
[0011] The present subject matter is described in detail with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer to various features of the present subject matter.
[0012] The figures depict an embodiment of the present disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
[0013] FIG. 1 illustrates a network architecture of a system for automatically configuring a solar module mounting structure, according to aspects of the present disclosure.
[0014] FIG. 2 depicts a flowchart of a method for designing a solar mounting structure, according to aspects of the present disclosure.
[0015] FIG. 3 illustrates a system architecture a system for automatically configuring a solar module mounting structure, according to aspects of the present disclosure.
[0016] FIG. 4 depicts a design of a solar module mounting structure generated by the system, according to aspects of the present disclosure.
[0017] FIG. 5 illustrates a neural network architecture for machine learning algorithms, according to aspects of the present disclosure.
DETAILED DESCRIPTION
[0018] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "receiving," "determining," "generating," and other forms thereof, are intended to be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any system and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, system and methods are now described.
[0019] The disclosed embodiments are merely examples of the disclosure, which may be embodied in various forms. Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments described but is to be accorded the widest scope consistent with the principles and features described herein.
[0020] The present disclosure relates to a system for automatically configuring a solar module mounting structure. This system combines a modular mounting framework with an automated method for designing and installing solar module mounting structures optimally configured for specific installation sites.
[0021] The system may include physical components such as columns, rafters, and purlins, which form the modular mounting framework. These components may be specially designed to optimize load distribution, facilitate easy assembly, and maximize solar energy capture.
[0022] In conjunction with the physical components, the system may incorporate a planning software tool that utilizes various data sources and analytical techniques. This software component may leverage geospatial data, structural simulations, and predictive analytics to determine optimal structural configurations and solar module layouts.
[0023] FIG. 1 illustrates a network architecture 100 of a system for automatically configuring a solar module mounting structure. The network architecture 100 includes a server 102, a network 106, and multiple client devices 104-1, 104-2, and 104-N.
[0024] The server 102 may include one or more processors, input/output (I/O) interfaces, and memory. In some cases, the server 102 hosts the software components and databases necessary for performing automated planning and analysis for solar module installations.
[0025] The network 106 may be any suitable communication network, such as the internet, a local area network (LAN), a wide area network (WAN), or a combination thereof. The network 106 facilitates data exchange between the server 102 and the client devices.
[0026] The client devices may include various types of computing devices, such as laptops (e.g., client device 104-1), desktop computers (e.g., client device 104-2), and mobile devices (e.g., client device 104-N). In some cases, additional client devices may be connected to the network 106, as indicated by the ellipsis between client devices 104-2 and 104-N.
[0027] The networked environment shown in FIG. 1 enables distributed processing and data sharing for the automated planning and analysis process. In some cases, the server 102 may perform complex simulations and data analysis, while the client devices may be used for data input, visualization, and user interaction with the system.
[0028] The network architecture 100 allows for real-time data exchange, enabling rapid updates to design parameters and immediate feedback on proposed solar module mounting structures. In some cases, the client devices may access cloud-based resources through the server 102, facilitating scalable computing power for intensive simulations and machine learning processes.
[0029] The distributed nature of the network architecture 100 may enable collaborative work on solar module installation projects, with multiple users accessing and contributing to the same project data through different client devices. In some cases, the network architecture 100 may also facilitate integration with external data sources, such as geographic information systems (GIS) or weather databases, to enhance the accuracy of the automated planning and analysis process.
[0030] The automated method employed by the system may utilize geographic information systems (GIS) data, drone-based surveys, and 3D modelling to gather site information. This data may be used in conjunction with simulation algorithms to calculate site-specific loads, slope angles, and component strengths. Additionally, solar simulation and machine learning techniques may be employed to maximize energy output.
[0031] By integrating the physical mounting components with the automated planning and analysis method, the system may reduce manual input, mitigate errors, and yield efficient, cost-effective, and structurally sound solar installations. This integrated and automated approach may streamline the process of designing and implementing solar installations on residential rooftops.
[0032] The modular mounting framework of the configured solar module mounting structure may include columns, rafters, and purlins designed to optimize structural integrity, facilitate assembly, and maximize solar energy capture.
[0033] In some cases, the columns of the modular mounting framework may feature a tapered design. This tapered design may reduce material usage while maintaining the required strength for supporting the solar module array. The columns may also incorporate integrated and automated flanges or tracks. These integrated and automated features may allow for quick and secure connection with the rafters, potentially simplifying the assembly process and reducing installation time.
[0034] The rafters of the modular mounting framework may be designed to provide optimal support for the solar modules while maximizing energy capture. In some cases, the rafters may be sloped at an angle determined by solar simulation software. This angle may be calculated to maximize energy capture based on the specific installation site's geographic location, orientation, and other relevant factors. The structural design of the rafters may be optimized using Finite Element Analysis (FEA). FEA may be employed to ensure that the rafters have adequate rigidity and load capacity to support the solar modules and withstand environmental forces such as wind and snow loads.
[0035] Purlins may be included in the modular mounting framework to provide direct support and attachment points for the solar modules. In some cases, the purlins may feature pre-drilled holes. These pre-drilled holes may serve multiple purposes, including facilitating cable management and simplifying the assembly process. The pre-drilled holes may allow for easy routing of electrical cables, potentially reducing clutter and improving the overall aesthetics of the installation. Additionally, these pre-drilled holes may expedite the assembly process by eliminating the need for on-site drilling.
[0036] The combination of components selected from an inventory of standardised components of varying strengths, based on the various simulations including structural simulations may contribute to a modular mounting framework that is efficient to install, structurally sound, and optimized for solar energy capture. The components may be at least one of tapered columns with integrated and automated connection features, angle-optimized rafters, and pre-configured purlins.
[0037] The system for automatically configuring a solar module mounting structure may incorporate an automated planning method that utilizes various data sources and analytical techniques to optimize the design and installation of solar module mounting structures.
[0038] In some cases, the automated planning method may leverage geospatial data to gather site information. This geospatial data may include geographic coordinates, climate data, and solar irradiance patterns. The method may integrate this information from mapping and geographic databases to create a comprehensive understanding of the installation site's characteristics.
[0039] The automated planning method may also employ structural simulations to ensure the integrity and efficiency of the mounting structure. These simulations may include wind load analysis, snow load calculations, and weight distribution assessments. By simulating these various forces and conditions, the method may determine the optimal configuration of columns, rafters, and purlins for a given installation site. In an embodiment, the wind load may be determined using an integrated database comprising wind speed data provided by the National Building Code (NBC).
[0040] Predictive analytics may be utilized within the automated planning method to forecast energy yield and guide placement decisions. In some cases, the method may use historical weather data and solar irradiance patterns to estimate annual energy production for different configurations of solar modules.
[0041] The automated planning method may determine optimal structural configurations by analysing the collected geospatial data and simulation results. For example, the method may calculate the ideal number, spacing, strength, and height of columns based on the roof load capacity, anticipated wind forces, and local building codes. Similarly, the method may determine the optimal angle and orientation of rafters to maximize solar energy capture while ensuring structural stability.
[0042] In some cases, the automated planning method may utilize drone-based surveys to capture precise building dimensions, identify roof obstructions, and map shading elements. This detailed site information may be used to create a 3D model of the installation site, which may then be used for further analysis and optimization.
[0043] The method may employ solar simulation software to calculate the optimal inclination of rafters and orientation of modules for maximum energy generation. This simulation may take into account factors such as the site's latitude, local weather patterns, and potential shading from nearby structures or vegetation.
[0044] Machine learning algorithms may be incorporated into the automated planning method to refine component selection and layouts over time. These algorithms may analyse data from previous installations, including performance metrics and installation challenges, to improve recommendations for future projects. For example, the method may learn to suggest specific component sizes or materials based on patterns observed in successful installations with similar site characteristics.
[0045] The automated planning method may generate actionable installation plans as output. These plans may include detailed component lists, assembly instructions, and layout schematics. In some cases, the method may provide a user-friendly interface that allows non-expert users to input minimal data and receive optimized solutions for their specific installation site.
[0046] By integrating these various analytical techniques and data sources, the automated planning method may reduce the complexity of solar installation planning, enhance accuracy in design and component selection, and ultimately contribute to more efficient and cost-effective solar installations.
[0047] In some embodiments, the system for automatically configuring a solar module mounting structure may be specifically designed for residential rooftop installations, where diverse obstacles and varying roof layouts are common. The system may utilize a predefined and predesigned set of standard components to efficiently configure a mounting system tailored to each unique residential rooftop. This approach may offer significant advantages over traditional methods by streamlining the design and installation process.
[0048] The system may maintain an inventory of standardized components, including columns, rafters, purlins, and connectors of various sizes and strengths. When designing a mounting system for a specific residential rooftop, the system may access this inventory to determine the optimal configuration that can be installed most efficiently. The strength of the components may be selected based on the available inventory and the required system strength, which may be calculated using factors such as local wind load data and roof characteristics.
[0049] In one embodiment, the system may operate by first receiving detailed rooftop data, including dimensions, slope, orientation, and obstacle locations such as chimneys, vents, and skylights through drone surveys or 3D modelling. Based on the site's geographic location, the system may calculate the expected wind and snow loads for the installation.
[0050] The system may then query the current inventory of standardized components. Using the site data, load analysis, and available inventory, the system may generate multiple possible configurations for the mounting structure. These configurations may be evaluated based on factors such as structural integrity, energy production potential, installation speed, and component availability.
[0051] After evaluation, the system may select the optimal configuration and the specific components from the inventory, ensuring that the chosen components meet or exceed the required strength based on the calculated loads. The system may then generate a detailed installation plan, including a list of required components, their locations, and assembly instructions.
[0052] In a practical application of this embodiment, the system may process data for a residential rooftop with a complex layout including multiple roof planes, a chimney, and several vents. The system may determine that a configuration using twelve front columns of height 2 meters, twelve back columns of height 2.5 meter, twelve rafters of 3 meter length, and forty-eight purlins of 3.6 meter length would provide optimal coverage while avoiding obstacles. The system may then check the inventory to confirm the availability of these components.
[0053] In this example, if the calculated wind load requires columns with a minimum strength rating of medium strength, but the inventory only contains columns of light strength and heavy strength, the system may automatically select the heavy strength columns to ensure structural integrity. The system may then generate an installation plan that specifies the exact placement of each component, taking into account the roof's unique features and obstacles. In an embodiment, the system may modify strengths of other components to compensate for extra strength of heavy strength columns selected instead of medium strength columns.
[0054] This approach may allow for rapid design and installation of customized mounting systems using standardized components, potentially reducing costs, minimizing errors, and accelerating the overall installation process for residential rooftop solar systems. The use of standardized components from a predefined inventory may also improve supply chain efficiency and reduce manufacturing costs through economies of scale.
[0055] In some implementations, the system may categorize the strength of components such as columns, rafters, and purlins into predefined categories of light, medium, and heavy. The thickness of these components may vary based on their assigned strength category. For example, columns categorized as "light" strength may have a smaller thickness compared to those categorized as "medium" or "heavy" strength. Similarly, rafters and purlins may be manufactured with varying thicknesses corresponding to their strength category. This variation in thickness based on strength category may allow the system to optimize the structural integrity of the mounting structure while minimizing material usage and cost. The system may select the appropriate strength category for each component based on the results of structural load simulations and site-specific requirements. In some cases, the system may utilize a combination of components with different strength categories within a single mounting structure to achieve the optimal balance between structural support and material efficiency. In an embodiment, the system may calculate structural loads including tension, compression, shear and bending moment and check different strength categories of the components to determine the minimum strength category required to sustain the stresses and deflections induced by the structural loads.
[0056] In some embodiments, the system for automatically configuring a solar module mounting structure may include a solar irradiance simulation module. This module may be designed to provide comprehensive analysis of solar potential for residential rooftops, taking into account various factors that influence solar energy production.
[0057] The solar irradiance simulation module may utilize geographic location data of the residential rooftop as a starting point. This data may include latitude, longitude, and elevation, which are crucial for determining the sun's path across the sky throughout the year. The module may also incorporate local weather pattern data, such as average cloud cover, precipitation, and temperature variations, which can affect solar panel efficiency and energy production.
[0058] In addition to location and weather data, the module may account for the specific characteristics of the residential rooftop, including its orientation and pitch. These factors significantly influence the amount of solar radiation that reaches the roof surface and, consequently, the potential energy production of solar panels installed there.
[0059] A key feature of the solar irradiance simulation module may be its ability to consider shadows cast by objects on the roof and surrounding the residential rooftop. This shadow analysis may be crucial for accurate prediction of solar energy production, as shading can significantly reduce the efficiency of solar panels.
[0060] To perform this shadow analysis, the system may create a detailed 3D model of the residential rooftop and surrounding area. This model may be based on data from aerial surveys or LiDAR (Light Detection and Ranging) scans, providing a high-resolution representation of the property and its surroundings.
[0061] Using this 3D model, the system may identify objects that may cast shadows, including roof features such as chimneys or vents, nearby buildings, and vegetation like trees or tall shrubs. The system may then simulate sun positions throughout the day and year based on the geographic location of the property.
[0062] With the sun position data and the 3D model, the system may calculate shadow projections from the identified objects onto the residential rooftop for different sun positions. This may allow the system to determine the duration and intensity of shading at various points on the residential rooftop throughout the year.
[0063] The impact of shadows on potential solar energy production may be quantified by comparing shaded and unshaded irradiance levels at affected areas of the residential rooftop. This detailed shadow analysis may provide crucial information for optimizing the placement of solar panels.
[0064] Using all of this data, the solar irradiance simulation module may calculate expected solar energy production at multiple points across the residential rooftop over different times of day and seasons. The results of these calculations may be used to generate a heat map of solar irradiance levels across the residential rooftop, providing a visual representation of the solar potential of different areas of the roof.
[0065] Based on this heat map and the other analysed factors, the solar irradiance simulation module may determine optimal placement locations for solar modules on the residential rooftop to maximize solar energy capture. This optimization may take into account not only the areas with the highest solar irradiance but also practical considerations such as the layout of the roof and the dimensions of the solar panels.
[0066] The processor of the system may use the output from the solar irradiance simulation module to generate the configuration for the solar module mounting structure. This may ensure that the mounting structure is designed to support solar panels in the locations where they will be most effective.
[0067] In some embodiments, the solar irradiance simulation module may be integrated with the processor to enable real-time optimization of energy production based on current shadow conditions. This could allow for dynamic adjustments to the system, such as altering the angle of panels or redirecting energy flow, to maximize energy production throughout the day and year as lighting conditions change.
[0068] The system for automatically configuring a solar module mounting structure may incorporate various methods for acquiring site data to optimize the design and installation of solar module mounting structures. This site data acquisition process may utilize geographic information systems (GIS) integration, drone surveys, and 3D modelling techniques.
[0069] In some cases, the system may integrate GIS data to gather comprehensive information about the installation site. This GIS integration may include collecting geographic coordinates, climate data, and solar irradiance patterns from mapping and geographic databases. The system may use this information to create a baseline understanding of the site's characteristics, including its location, orientation, and potential environmental factors that could impact solar energy production.
[0070] Drone surveys may be employed to capture more detailed and up-to-date information about the installation site. In some cases, these drone surveys may be used to obtain precise building dimensions, identify roof obstructions, and map shading elements. The drones may be equipped with high-resolution cameras and other sensors to capture detailed imagery and measurements of the site. This data may be particularly valuable for identifying potential challenges or opportunities that may not be apparent from satellite imagery or traditional surveys.
[0071] For example, a drone survey may reveal the presence of roof vents, chimneys, or other structures that could affect the placement of solar modules. The survey may also identify areas of the roof that receive more or less sunlight due to shading from nearby trees or buildings. This detailed information may allow for more accurate planning and optimization of the solar module layout.
[0072] The system may utilize 3D modelling techniques to create a comprehensive digital representation of the installation site. This 3D model may be constructed using data from both the GIS integration and drone surveys. In some cases, the 3D model may be used for structural load analysis, ensuring that the proposed mounting structure is compatible with the site's specific characteristics.
[0073] The 3D model may include detailed representations of the building's roof structure, including its slope, material composition, and load-bearing capacity. This information may be crucial for determining the optimal placement and configuration of the mounting structure components, such as columns, rafters, and purlins.
[0074] In some cases, the 3D model may also incorporate data about the surrounding environment, such as nearby buildings or landscape features that could affect solar exposure or wind patterns. This comprehensive model may allow for more accurate simulations of environmental factors that could impact the performance and durability of the solar installation.
[0075] By combining GIS integration, drone surveys, and 3D modelling, the site data acquisition process may provide a rich and detailed set of information about the installation site. This data may serve as the foundation for subsequent steps in the automated planning and analysis method, enabling more accurate and optimized designs for solar module mounting structures.
[0076] The system for automatically configuring a solar module mounting structure may incorporate various structural and solar simulations to optimize the design and performance of solar installations. These simulations may include load simulation, solar irradiance simulation, and energy yield forecasting.
[0077] In some cases, the system may perform load simulations to ensure the structural integrity of the proposed configuration. These load simulations may include wind load analysis, snow load calculations, and weight distribution assessments. The wind load analysis may simulate the effects of wind forces on the mounting structure and solar modules, taking into account factors such as local wind patterns, building height, and surrounding terrain. Snow load calculations may estimate the additional weight that may accumulate on the solar array during winter months, ensuring that the mounting structure can support this added burden. Weight distribution assessments may analyse how the weight of the solar modules and mounting components is distributed across the installation site, helping to identify potential stress points or areas requiring additional support.
[0078] The system may also employ solar irradiance simulations to calculate the optimal inclination of rafters and orientation of modules for maximum energy generation. These simulations may take into account factors such as the installation site's latitude, local weather patterns, and potential shading from nearby structures or vegetation. In some cases, the solar irradiance simulations may generate heat maps showing the expected solar exposure across different areas of the installation site, allowing for more informed decisions about module placement and orientation.
[0079] Energy yield forecasting may be another component of the simulation process. The system may use historical weather data, solar irradiance patterns, and the proposed system configuration to estimate annual energy production. In some cases, this forecasting may involve simulating energy production under various weather scenarios, such as cloudy days or seasonal variations in sunlight. The energy yield forecasts may help in determining the economic viability of the installation and in setting realistic expectations for energy production.
[0080] The results of these simulations may contribute to the design process in various ways. For example, the load simulation results may inform the selection and placement of structural components such as columns, rafters, and purlins. If the simulations indicate that certain areas of the installation site are subject to higher wind loads, the system may recommend stronger or more closely spaced support structures in those areas.
[0081] Solar irradiance simulation results may guide the overall layout of the solar array. In some cases, these simulations may reveal that certain areas of the installation site receive significantly more sunlight than others. The system may use this information to prioritize the placement of solar modules in high-irradiance areas, potentially increasing the overall energy yield of the installation.
[0082] Energy yield forecasts may influence decisions about the type and number of solar modules to be used. For instance, if the forecasts indicate that the proposed configuration may not meet the desired energy production targets, the system may suggest alterations such as using higher-efficiency modules or increasing the number of modules.
[0083] In some cases, the system may perform iterative simulations, adjusting various parameters to find an optimal balance between structural integrity, energy production, and cost-effectiveness. This iterative process may involve running multiple scenarios with different combinations of mounting structures, module types, and array configurations to identify the most suitable design for the specific installation site.
[0084] By integrating these various simulations into the design process, the automatically configuring a solar module mounting structure system may enhance the accuracy and efficiency of solar installation planning, potentially leading to more robust, productive, and cost-effective solar energy systems.
[0085] The system for automatically configuring a solar module mounting structure may incorporate machine learning algorithms to enhance its performance and decision-making capabilities over time. These algorithms may be applied for pattern recognition and adaptive learning, allowing the system to continuously improve its recommendations and optimizations.
[0086] In some cases, machine learning algorithms may be used for pattern recognition to analyse historical data from previous solar installations. This analysis may include examining factors such as component selections, installation configurations, and performance metrics. By recognizing patterns in this data, the system may identify correlations between certain design choices and successful outcomes.
[0087] For example, the machine learning algorithms may analyse data from multiple installations in a particular geographic region. The algorithms may recognize patterns indicating that certain types of mounting components or specific array configurations tend to perform better in that region's climate conditions. This pattern recognition may allow the system to make more informed recommendations for future installations in similar areas.
[0088] The system may also employ adaptive learning techniques to refine its recommendations over time. As new data is collected from each installation, the machine learning algorithms may update their models and adjust their decision-making processes. This adaptive learning approach may enable the system to continuously improve its accuracy and effectiveness.
[0089] The machine learning algorithms may be trained using a combination of supervised and unsupervised learning techniques. In some cases, the training data may include information from previous successful solar module installations, simulated design scenarios, and expert-labelled datasets. The network may learn to recognize patterns in site characteristics, structural requirements, and environmental factors that lead to optimal mounting system designs.
[0090] As new installations are completed and performance data is collected, the machine learning system may continuously update and refine its recommendations. This adaptive learning process may allow the system to improve its accuracy and efficiency over time, taking into account factors such as regional variations, emerging technologies, and changing best practices in the solar industry.
[0091] The outputs produced by the machine learning system may be used to enhance various aspects of the design process. In some cases, these outputs may inform the selection of optimal column heights, rafter angles, and purlin spacing. The system may also provide recommendations for component materials based on site-specific environmental conditions and load requirements.
[0092] By leveraging machine learning algorithms, the system for automatically configuring a solar module mounting structure may reduce the reliance on manual engineering decisions and improve the overall efficiency and reliability of solar module installations. The neural network architecture may enable the system to process complex, multidimensional data and generate optimized design solutions that may be difficult or time-consuming for human engineers to derive manually.
[0093] In some cases, the adaptive learning process may involve comparing predicted performance metrics with actual outcomes. For instance, if the system predicts a certain energy yield for a particular installation configuration, and the actual yield differs significantly, the algorithms may analyse the discrepancy to identify potential areas for improvement in future predictions.
[0094] The integration of machine learning may improve the system's performance in various ways. For component recommendations, the algorithms may learn to suggest optimal sizes, materials, or configurations based on patterns observed in successful installations with similar site characteristics. This may lead to more efficient use of materials and improved structural integrity.
[0095] In terms of layout optimization, machine learning algorithms may refine their understanding of how various factors interact to affect energy production. For example, the algorithms may learn to better account for complex shading patterns or local microclimate effects that impact solar module performance.
[0096] The system's ability to adapt and improve over time may also enhance its reliability and cost-effectiveness. As the machine learning algorithms accumulate more data and refine their models, their recommendations may become increasingly accurate and tailored to specific installation scenarios. This may result in reduced errors, improved energy yields, and more efficient use of resources.
[0097] In some cases, the machine learning integration may allow the system to identify trends or opportunities that might not be apparent through traditional analysis methods. For instance, the algorithms may recognize subtle correlations between certain component combinations and long-term durability, leading to recommendations that optimize not just initial performance but also system longevity.
[0098] By leveraging machine learning for pattern recognition and adaptive learning, the system for automatically configuring a solar module mounting structure may continuously enhance its capabilities, potentially leading to more efficient, reliable, and cost-effective solar installations over time.
[0099] The system for automatically configuring a solar module mounting structure may combine various components and processes to provide a comprehensive solution for designing and installing optimized solar module mounting structures. This integration may allow the system to streamline the entire process from initial site assessment to final installation.
[0100] In some cases, the system may begin by gathering site data through geographic information systems (GIS) integration, drone surveys, and 3D modelling. This data may be used to create a detailed digital representation of the installation site, including factors such as roof geometry, load-bearing capacity, and local environmental conditions.
[0101] The system may then utilize this site data in conjunction with structural simulations and solar irradiance analysis to determine the optimal configuration for the mounting structure. This process may involve calculating the ideal placement and specifications for columns, rafters, and purlins, as well as determining the most effective orientation and tilt for solar modules.
[0102] Machine learning algorithms may be employed to refine these calculations and recommendations based on historical data from previous installations. These algorithms may analyse patterns in successful installations to improve component selection and layout optimization for new projects.
[0103] Once the optimal configuration has been determined, the system may generate detailed component lists, assembly instructions, and layout schematics. These outputs may provide comprehensive guidance for the installation process, potentially reducing the likelihood of errors and improving efficiency.
[0104] In some cases, the component lists may include precise specifications for each element of the mounting structure, such as the dimensions and materials for columns, rafters, and purlins. The assembly instructions may provide step-by-step guidance for constructing the mounting structure and attaching solar modules, potentially including details on proper fastening techniques and cable management.
[0105] The layout schematics generated by the system may offer a visual representation of the final installation, showing the placement of each component and solar module. These schematics may be used by installation teams to ensure accurate implementation of the design.
[0106] The integrated and automated system may feature a user-friendly graphical interface designed for use by non-experts. This interface may allow users to input minimal data about their installation site and requirements. In some cases, users may be able to enter basic information such as the address of the installation site, desired energy output, and any specific constraints or preferences.
[0107] The system may then process this input data in conjunction with its integrated and automated databases and analytical tools to generate optimized solutions. These solutions may be presented to the user in an easily understandable format, potentially including visual representations of the proposed installation and key performance metrics.
[0108] In some cases, the user interface may allow for interactive exploration of different design options. Users may be able to adjust certain parameters and see in real-time how these changes affect the overall system design and projected performance.
[0109] By integrating these various components and processes, the system may provide a comprehensive solution for designing and installing optimized solar module mounting structures. This integration may potentially reduce the complexity of the planning process, improve the accuracy of designs, and enhance the overall efficiency of solar installations.
[0110] In some cases, the system for automatically configuring a solar module mounting structure may be utilized for a residential rooftop solar installation. The process may begin with site data acquisition. A drone equipped with high-resolution cameras and LiDAR sensors may conduct an aerial survey of the residential property. The drone may capture detailed imagery of the roof structure, including dimensions, pitch, and any obstructions such as chimneys or vents. The pitch may correspond to gaps between the rows and of solar modules being installed.
[0111] Concurrently, the system may access geographic information system (GIS) data for the property location. This GIS data may include local climate information, historical weather patterns, and solar irradiance data specific to the area. The system may also incorporate local building codes and regulations relevant to residential solar installations.
[0112] Following data acquisition, the system may perform a series of simulations and analyses. A 3D model of the roof may be generated from the drone survey data. This model may be used to calculate the available roof area for solar module placement and to identify optimal module orientations.
[0113] The simulation engine may then conduct structural load simulations. These simulations may take into account the weight of the proposed solar modules and mounting structure, as well as potential wind and snow loads specific to the location. The system may use this information to determine the required strength and spacing of the mounting structure components. The system may determine the strength of each component of the mounting structure based on the simulations. The strength may be one of light, medium, and heavy. In an embodiment, the system may access the inventory of components such that the system may be able to select the strength of each component based on the inventory.
[0114] Solar irradiance simulations may be performed next. These simulations may utilize the 3D roof model and local climate data to predict solar exposure throughout the year. The system may use this information to optimize the tilt angle of the solar modules and their arrangement on the roof to maximize energy production.
[0115] Based on the simulation results, the designing engine may generate a list of required components for the solar module mounting structure. This list may include the number and specifications of columns, rafters, and purlins needed for the installation. The system may select components that are optimized for the specific load requirements and roof geometry of the residential property.
[0116] The validation engine may then verify that the proposed design meets all local building codes and structural requirements. In some cases, the system may make adjustments to the design to ensure compliance with regulations while maintaining optimal energy production.
[0117] Once the design is validated, the recommendation engine may generate a comprehensive installation plan. This plan may include detailed schematics showing the exact placement of each mounting structure component and solar module. The plan may also provide step-by-step assembly instructions tailored to the specific residential installation.
[0118] The system may present the final design and installation plan through a user-friendly interface. This interface may allow the homeowner or installer to visualize the proposed solar array on a 3D model of their roof. The interface may also provide estimated energy production figures and potential cost savings based on local electricity rates.
[0119] Throughout this process, machine learning algorithms may refine and optimize the system's recommendations. These algorithms may draw upon data from previous residential installations to improve component selection, layout optimization, and energy production estimates.
[0120] In some cases, the system may also generate a list of required permits and documentation for the specific municipality, streamlining the approval process for the homeowner. This permit and documentation generation process may involve accessing a database of local regulations and requirements for solar installations. The system may cross-reference the proposed installation design with these regulations to identify necessary permits and generate the required documentation.
[0121] The generated documentation may include information crucial for residential rooftop solar installations, such as the allowed limits of wattage for the household and the maximum permitted height for the solar panel mounting structure above the rooftop. These parameters are important because they directly impact the design and configuration of the solar installation. For instance, wattage limits may determine the number and type of solar panels that can be installed, while height restrictions may influence the tilt angle of the panels and the overall mounting structure design.
[0122] By incorporating this permit and documentation information into its decision-making process, the system can select or determine the optimal configuration more effectively. It can ensure that the proposed installation not only maximizes energy production and structural integrity but also complies with local regulations. For example, if a municipality has a strict height limit for rooftop structures, the system may adjust its design to use a lower profile mounting system or alter the tilt angle of the panels to stay within the allowed height while still optimizing for energy production.
[0123] This integrated and automated approach, which considers both technical optimization and regulatory compliance, may result in a highly optimized, code-compliant solar installation design. Such a design can maximize energy production for the specific residential property while minimizing installation time, potential errors, and the risk of regulatory issues or rejected permit applications. This comprehensive consideration of both technical and regulatory factors may significantly streamline the entire solar installation process for homeowners and housing societies.
[0124] The mounting structure components of the configured solar module mounting structure may comprise specialized columns, rafters, and purlins designed to optimize structural integrity, ease of installation, and overall system performance.
[0125] In some cases, the columns may feature a tapered design. This tapered configuration may allow for reduced material usage while maintaining the required strength to support the solar module array. The tapering may be calculated based on load simulations and structural analysis performed by the system, ensuring that each column is optimized for its specific position and load requirements within the mounting structure.
[0126] The columns may incorporate integrated and automated flanges or tracks. These features may facilitate quick and secure connections with the rafters. In some cases, the integrated and automated flanges or tracks may allow for adjustable attachment points, providing flexibility in the final assembly to accommodate site-specific variations or installation requirements.
[0127] Rafters may be designed with standardized slots and fasteners for secure column attachment and easy adjustment. These standardized connection points may enable efficient assembly and may allow for fine-tuning of the rafter position during installation. In some cases, the rafters may be angled based on solar simulation results to optimize energy capture for the specific installation site.
[0128] The system may select rafter specifications, such as material type, dimensions, and strength characteristics, based on the automated analysis and simulations. Factors such as expected snow loads, wind forces, and the weight of the solar modules may be considered in determining the appropriate rafter design for each installation.
[0129] Purlins may be pre-configured with features to enhance installation efficiency and system performance. In some cases, purlins may include pre-drilled holes for cable management and easy assembly. These pre-drilled holes may be strategically placed based on the system's analysis of optimal cable routing and attachment point locations for the specific solar module configuration.
[0130] The selection and configuration of purlins may be determined by the automated planning system based on factors such as the type and size of solar modules to be installed, the calculated structural loads, and the optimal spacing for maximum energy production. In some cases, the system may recommend different purlin specifications for various sections of the mounting structure to address site-specific requirements or variations in load distribution.
[0131] The integrated and automated design of these mounting structure components may allow for a modular and adaptable system. In some cases, the components may be easily combined or adjusted to accommodate different roof geometries, ground conditions, or solar module configurations. This modularity may enable the system to generate optimised plans for residential rooftops with varying structures considering the inventory.
[0132] By leveraging the automated analysis and simulation capabilities of the system, the selection and configuration of these mounting structure components may be tailored to the specific requirements of each installation site. This approach may result in mounting structures that are optimized for structural integrity, cost-effectiveness, and energy production efficiency.
[0133] In some cases, the system for automatically configuring a solar module mounting structure may include a user-friendly graphical interface. This interface may allow non-expert users to input minimal data and receive optimized solutions for solar module installations. The interface may present a series of simple questions or data entry fields to gather essential information about the installation site and project requirements.
[0134] The system may generate various automated outputs based on the input data and subsequent analyses. In some cases, these outputs may include detailed installation plans, comprehensive component lists, and step-by-step assembly instructions. The installation plans may provide visual representations of the proposed solar module layout, including precise positioning of mounting structures and individual modules. These plans may be presented as 2D schematics or interactive 3D models, allowing users to visualize the final installation from different angles.
[0135] Component lists generated by the system may include detailed specifications for all required mounting hardware, solar modules, and electrical components. In some cases, these lists may be customized based on local availability and project-specific requirements. The system may also provide estimated quantities and potential alternatives for each component to facilitate procurement and inventory management.
[0136] Assembly instructions produced by the system may offer detailed guidance for installers. These instructions may include illustrated step-by-step procedures, highlighting critical assembly points and potential areas of concern. In some cases, the system may generate customized assembly sequences optimized for the specific installation site and chosen components.
[0137] The integrated and automated system may incorporate automated checks to ensure chosen components meet regulatory requirements and structural constraints. In some cases, these checks may compare the proposed design against local building codes, zoning regulations, and industry standards. The system may flag potential compliance issues and suggest modifications to meet necessary requirements.
[0138] For structural constraints, the automated checks may verify that the selected components can withstand expected loads and environmental conditions specific to the installation site. In some cases, the system may perform iterative analyses, adjusting component selections or configurations until all structural requirements are satisfied.
[0139] The user interface may present the results of these automated checks in a clear and understandable format. In some cases, this may include visual indicators such as green checkmarks for compliant elements and red flags for areas requiring attention. The interface may also provide explanations for any identified issues and suggest potential solutions or alternatives.
[0140] By combining a user-friendly interface with comprehensive automated outputs and rigorous compliance checks, the integrated and automated system may streamline the solar module installation planning process. This approach may reduce the potential for errors, ensure regulatory compliance, and provide non-expert users with professional-grade planning capabilities.
[0141] The system for automatically configuring a solar module mounting structure may offer several key advantages over traditional methods of solar module installation planning and design. In some cases, the system may significantly reduce the time required for site assessment and design generation. By automating many of the processes that traditionally require manual input, the system may minimize human error and increase overall efficiency.
[0142] In some cases, the integrated and automated approach may lead to more optimized designs. By considering multiple factors simultaneously, such as structural requirements, solar exposure, and local regulations, the system may generate solutions that balance various competing objectives more effectively than manual methods.
[0143] The system's ability to leverage machine learning algorithms may result in continual improvement of designs and recommendations over time. As the system accumulates data from completed installations, the accuracy and efficiency of its output may increase, potentially leading to better performance and cost-effectiveness of solar installations.
[0144] In some cases, the automated nature of the system may make solar module installation planning more accessible to a wider range of users. The user-friendly interface and automated outputs may allow individuals with less technical expertise to engage in the planning process, potentially expanding the adoption of solar energy solutions.
[0145] Advanced weather prediction models may be incorporated into future versions of the system. These models may provide more accurate long-term forecasts of solar irradiance and weather patterns, potentially improving the accuracy of energy yield predictions and informing more resilient mounting structure designs.
[0146] In some cases, future developments may include the integration of augmented reality (AR) technologies. AR may be used to provide installers with real-time, on-site guidance during the installation process, potentially reducing errors and improving installation efficiency.
[0147] The system may evolve to incorporate more advanced materials science considerations. In some cases, this may involve recommending novel mounting materials or coatings that enhance durability or improve energy efficiency based on specific environmental conditions.
[0148] Future versions of the system may include more sophisticated financial modelling capabilities. These capabilities may allow users to generate detailed cost-benefit analyses, return on investment projections, and financing options tailored to specific installation projects.
[0149] The continuous development and refinement of the system for automatically configuring a solar module mounting structure may contribute to the broader goal of increasing renewable energy adoption and improving the efficiency of solar installations across various scales and applications.
[0150] Referring to FIG. 2, process flow 200 for designing a solar module mounting structure is illustrated. This process may involve several steps to ensure an optimized and efficient design for solar module installations.
[0151] In step 202, site data may be received. This data may include various types of information relevant to the installation site. In some cases, the site data may comprise geographic coordinates, climate data, and solar irradiance patterns. The data may be obtained through various means, such as geographic information systems (GIS), satellite imagery, or on-site surveys. In some implementations, drone surveys may be utilized to capture precise building dimensions, roof obstructions, and shading elements.
[0152] Step 204 involves performing simulations for analysis. These simulations may utilize the site data received in step 202 to conduct various types of analysis. In some cases, the simulations may include structural load analysis, which may account for factors such as wind load, snow load, and weight distribution. Solar irradiance simulations may also be performed to calculate the optimal inclination of rafters and orientation of modules for maximum energy generation. In some implementations, energy yield forecasting may be conducted to estimate annual energy production and guide placement decisions.
[0153] In step 206, a list of components required for a solar module mounting structure may be generated. This list may be based on the results of the simulations performed in step 204. The components may include items such as columns, rafters, purlins, and fasteners. In some cases, the list may specify the quantity, dimensions, and materials for each component. The component selection may be optimized based on factors such as structural requirements, cost-effectiveness, and availability.
[0154] Step 208 involves determining the strength of the components from the list generated in step 206, based on the simulations performed in step 204. This step may ensure that the selected components meet the structural requirements determined by the simulations. In some implementations, finite element analysis (FEA) may be used to verify the structural integrity of key components under various load conditions. The strength determination may also consider factors such as material properties, component geometry, and safety factors.
[0155] In the final step 210, a design for the solar module mounting structure may be generated. This design may incorporate all the information and analysis from the previous steps. The design may include detailed specifications for the placement and configuration of all components. In some cases, the design output may comprise 3D models, assembly instructions, and layout schematics. The generated design may aim to optimize factors such as structural integrity, energy production, and ease of installation.
[0156] Throughout this process, machine learning algorithms may be employed to refine and optimize the design based on historical data and performance metrics from previous installations. This may lead to continual improvements in the efficiency and reliability of the solar module mounting structures designed using this process.
[0157] Referring to FIG. 3, a system architecture 102 for the system for automatically configuring a solar module mounting structure is illustrated. The system architecture 102 includes a processor, I/O interfaces, and memory. The memory houses five distinct software engines that work together to facilitate the design and planning process for solar module installations.
[0158] The Data Acquisition Engine may be responsible for gathering and processing relevant site information. In some cases, this engine collects data from various sources such as geographic information systems (GIS), drone surveys, and user inputs. The Data Acquisition Engine may process and format this data for use by other components of the system.
[0159] The Simulation Engine may perform various analyses and simulations based on the acquired data. In some cases, this engine conducts structural load simulations, solar irradiance calculations, and energy yield forecasting. The Simulation Engine may generate results that inform the design process and component selection.
[0160] The Designing Engine may utilize outputs from the Data Acquisition and Simulation Engines to create optimal designs for solar module mounting structures. In some cases, this engine determines the placement and configuration of columns, rafters, and purlins based on site-specific factors and simulation results.
[0161] The Validation Engine may verify that the proposed designs meet all necessary structural, regulatory, and performance requirements. In some cases, this engine checks the designs against local building codes, ensures adequate load-bearing capacity, and confirms compatibility with chosen solar modules.
[0162] The Recommendation Engine may analyse data from all other engines and historical installations to provide optimized suggestions for component selection, layout, and installation procedures. In some cases, this engine employs machine learning algorithms to refine its recommendations over time.
[0163] These engines may work together in a coordinated manner throughout the design process. For example, the Data Acquisition Engine may provide site data to the Simulation Engine, which then generates load analyses. The Designing Engine may use these analyses to create a preliminary design, which the Validation Engine checks for compliance. Finally, the Recommendation Engine may suggest optimizations based on the validated design and historical data.
[0164] In some cases, the system architecture allows for iterative refinement of designs. The Recommendation Engine may provide feedback to the Designing Engine, which then creates updated designs for further validation and optimization. This iterative process may continue until an optimal solution is achieved.
[0165] The modular nature of the system architecture may allow for flexibility and scalability. Additional engines or functionalities may be integrated and automated into the system as needed to address specific requirements or incorporate new technologies.
[0166] Referring to FIG. 4, FIG. 4 illustrates an example configuration of a solar module mounting structure that may be generated by the system for automatically configuring a solar module mounting structure. The structure depicted in FIG. 4 represents an optimized design for a residential rooftop installation, showcasing how the system may utilize site-specific data and simulation results to create an efficient and structurally sound configuration.
[0167] The mounting structure may comprise vertical support columns (402), horizontal rafters (404), and solar panels (406) arranged in an angled configuration. The system may generate a list of components required for the solar module mounting structure based on the results of structural load simulations and solar irradiance simulations. This list may include columns (402), rafters (404), and purlins, forming a modular mounting framework.
[0168] In determining the strength of the components, the system may select components of varying strengths based on the structural load simulations. This is evident in FIG. 4, where the vertical columns (402) are shown as robust, elongated members extending upward from a base. These columns may be designed with a tapered profile to reduce material usage while maintaining the required strength. The columns may also feature integrated flanges or tracks for quick connection with rafters.
[0169] The system may generate a configuration with columns (402) of different heights to create an optimal angle for the solar panels (406). In some cases, the structure may include front columns with a first height, middle columns with a second height, and back columns with a third height. This arrangement may allow the rafters (404) to form bridges between the columns at an inclined angle.
[0170] The horizontal components may consist of uniform rafters (404) that span between the columns (402). These rafters may be sloped at an angle determined by the system's solar irradiance simulations to maximize energy capture for the specific installation site. The rafters (404) shown in FIG. 4 may have a medium strength rating, as determined by the system's structural analysis and optimization algorithms.
[0171] To enhance lateral stability, the system may incorporate interconnecting braces between the columns (402) and rafters (404). These braces, while not explicitly shown in FIG. 4, may be included in the generated configuration to provide additional structural support.
[0172] The configuration also includes a purlin assembly (412), which may be connected laterally across multiple rafters (404), forming a grid-like structure. This grid-like structure may allow for flexibility in supporting solar modules in both portrait and landscape orientations. The arrangement of the purlin assembly (412) may be changed to change the orientation of the solar modues.
[0173] Solar panels (406) are shown mounted on top of the purlin assembly (412) in FIG. 4. The arrangement of the panels demonstrates how the mounting structure may be designed to support the weight and distribution of the solar array effectively. The angle of the panels may be optimized by the system's solar irradiance simulations to maximize energy production for the specific installation site.
[0174] In some cases, the system may include a centralized junction box mounted on one of the columns (402) for aggregating electrical connections from multiple solar panels. While not explicitly shown in FIG. 4, this junction box may be incorporated into the design to streamline the electrical integration of the solar array.
[0175] Connection points (408) are visible where the rafters (404) meet the columns (402), showcasing the structural interface between vertical and horizontal elements. These connections may be designed to transfer loads effectively throughout the structure.
[0176] Base plates (410) are shown at the bottom of each column (402), illustrating how the structure may be anchored to the mounting surface. These base plates may distribute the load of the entire structure and solar array across a wider area of the roof.
[0177] Referring to FIG. 5, neural network architecture 500 that may be employed as part of the machine learning system is illustrated.
[0178] Figure 5 illustrates an example artificial neural network (“ANN”) 500 of the machine learning algorithms and models described above. In particular embodiments, an ANN may refer to a computational model comprising one or more nodes. Example ANN 500 may comprise an input layer 510, hidden layers 520, 530, 540, and an output layer 550. The input layer 510 may receive various data inputs related to solar module installations, such as site characteristics, environmental conditions, structural requirements, and historical performance data. The output layer 550 may produce recommendations and optimizations for the solar module mounting system design. These outputs may include suggested component sizes, materials, layouts, and installation parameters. Each layer of the ANN 500 may comprise one or more nodes, such as a node 505 or a node 515. In particular embodiments, each node of an ANN may be connected to another node of the ANN. As an example, and not by way of limitation, each node of the input layer 510 may be connected to one of more nodes of the hidden layer 520. In particular embodiments, one or more nodes may be a bias node (e.g., a node in a layer that is not connected to and does not receive input from any node in a previous layer). In particular embodiments, each node in each layer may be connected to one or more nodes of a previous or subsequent layer. Although Figure 5 depicts a particular ANN with a particular number of layers, a particular number of nodes, and particular connections between nodes, this disclosure contemplates any suitable ANN with any suitable number of layers, any suitable number of nodes, and any suitable connections between nodes. As an example, and not by way of limitation, although Figure 5 depicts a connection between each node of the input layer 510 and each node of the hidden layer 520, one or more nodes of the input layer 510 may not be connected to one or more nodes of the hidden layer 520.
[0179] In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANN with no cycles or loops where communication between nodes flows in one direction beginning with the input layer and proceeding to successive layers). As an example, and not by way of limitation, the input to each node of the hidden layer 520 may comprise the output of one or more nodes of the input layer 510. As another example and not by way of limitation, the input to each node of the output layer 550 may comprise the output of one or more nodes of the hidden layer 540. In particular embodiments, an ANN may be a deep neural network (e.g., a neural network comprising at least two hidden layers). In particular embodiments, an ANN may be a deep residual network. A deep residual network may be a feedforward ANN comprising hidden layers organized into residual blocks. The input into each residual block after the first residual block may be a function of the output of the previous residual block and the input of the previous residual block. As an example, and not by way of limitation, the input into residual block N may be F(x)+x, where F(x) may be the output of residual block N−1, x may be the input into residual block N−1. Although this disclosure describes a particular ANN, this disclosure contemplates any suitable ANN.
[0180] In particular embodiments, an activation function may correspond to each node of an ANN. An activation function of a node may define the output of a node for a given input. In particular embodiments, an input to a node may comprise a set of inputs. As an example, and not by way of limitation, an activation function may be an identity function, a binary step function, a logistic function, or any other suitable function.
[0181] In particular embodiments, the input of an activation function corresponding to a node may be weighted. Each node may generate output using a corresponding activation function based on weighted inputs. In particular embodiments, each connection between nodes may be associated with a weight. As an example, and not by way of limitation, a connection 525 between the node 505 and the node 515 may have a weighting coefficient of 0.4, which may indicate that 0.4 multiplied by the output of the node 505 is used as an input to the node 515. In particular embodiments, the input to nodes of the input layer may be based on a vector representing an object. Although this disclosure describes particular inputs to and outputs of nodes, this disclosure contemplates any suitable inputs to and outputs of nodes. Moreover, although this disclosure may describe particular connections and weights between nodes, this disclosure contemplates any suitable connections and weights between nodes.
[0182] In particular embodiments, the ANN may be trained using training data. As an example, and not by way of limitation, training data may comprise inputs to the ANN 500 and an expected output. As another example and not by way of limitation, training data may comprise vectors, each representing a training object and an expected label for each training object. In particular embodiments, training the ANN may comprise modifying the weights associated with the connections between nodes of the ANN by optimizing an objective function. As an example, and not by way of limitation, a training method may be used (e.g., the conjugate gradient method, the gradient descent method, the stochastic gradient descent) to backpropagate the sum-of-squares error measured as a distance between each vector representing a training object (e.g., using a cost function that minimizes the sum-of-squares error). In particular embodiments, the ANN may be trained using a dropout technique. As an example, and not by way of limitation, one or more nodes may be temporarily omitted (e.g., receive no input and generate no output) while training. For each training object, one or more nodes of the ANN may have some probability of being omitted. The nodes that are omitted for a particular training object may be different than the nodes omitted for other training objects (e.g., the nodes may be temporarily omitted on an object-by-object basis). Although this disclosure describes training the ANN in a particular manner, this disclosure contemplates training the ANN in any suitable manner.
[0183] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims.

, Claims:
Claims:
1. A system for automatically configuring a solar module mounting structure, comprising:
a memory 112; and
a processor 108 coupled to the memory, wherein the processor is configured to execute instructions stored in the memory for:
receiving site data for a solar module installation site;
performing simulations based on the site data, wherein the simulations include structural load simulations and solar irradiance simulations;
generating a configuration for a solar module mounting structure based on results of the simulations, wherein generating the configuration comprises generating a list of components required for the solar module mounting structure based on results of the simulations and determining strength of the components from the list of components;
validating structural integrity of the configuration based on site data and simulation results; and
generating instructions for installation of the solar module mounting structure based on the configuration.
2. The system of claim 1, wherein receiving site data comprises acquiring geographic information systems (GIS) data, drone survey data, and 3D modelling data of the solar module installation site.
3. The system of claim 1, wherein the structural load simulations include wind load analysis, snow load calculations, and weight distribution assessments.
4. The system of claim 1, wherein the system may determine an ideal placement of the solar panel mounting system based on solar irradiance simulations that identify a reduction in energy generated by the solar panel due to shadows, and wherein the system is further configured to calculate optimal inclination angles for rafters and orientation of solar modules to maximize energy generation by avoiding shadows.
5. The system of claim 1, wherein generating the configuration further comprises applying machine learning algorithms to recognize patterns and adaptively learn from historical installation data.
6. The system of claim 1, wherein the list of components includes columns, rafters, and purlins for a modular mounting framework.
7. The system of claim 6, wherein determining strength of the components comprises selecting components of varying strengths based on the structural load simulations.
8. The system of claim 1, wherein validating structural integrity comprises performing finite element analysis (FEA) on the configuration.
9. The system of claim 1, wherein the processor is further configured to execute instructions for generating a 3D visualization of the solar module mounting structure based on the configuration.
10. The system of claim 1, wherein the installation instructions include detailed component lists, assembly instructions, and layout schematics.
11. The system of claim 1, wherein the processor is further configured to execute instructions for optimizing the configuration to maximize energy yield based on the solar irradiance simulations.
12. The system of claim 1, wherein the processor is further configured to execute instructions for generating recommendations for future installations based on performance data collected from installed solar module mounting structures.
13. The system of claim 1, wherein the processor is further configured to execute instructions for adjusting the configuration in real-time based on user input through a graphical user interface.
14. The system of claim 1, wherein the processor is further configured to execute instructions for generating a cost estimate for the solar module mounting structure based on the configuration and current market prices for components.
15. The system of claim 1, wherein the processor is further configured to execute instructions for integrating weather forecast data to optimize the configuration for local climate conditions.
16. The system of claim 1, further comprising a solar irradiance simulation module configured to:
utilize geographic location data of the residential rooftop;
incorporate local weather pattern data;
account for the orientation and pitch of the residential rooftop;
consider shadows cast by objects on the roof and surrounding the residential rooftop;
calculate expected solar energy production at multiple points across the residential rooftop over different times of day and seasons; and
generate a heat map of solar irradiance levels across the residential rooftop.
17. The system of claim 16, wherein the solar irradiance simulation module is further configured to determine optimal placement locations for the solar modules on the residential rooftop to maximize solar energy capture based on the generated heat map.
18. The system of claim 16, wherein considering shadows cast by objects comprises:
creating a 3D model of the residential rooftop and surrounding area based on data from aerial surveys or LiDAR scans;
identifying objects in the 3D model that may cast shadows, including roof features, nearby buildings, and vegetation;
simulating sun positions throughout the day and year based on the geographic location;
calculating shadow projections from the identified objects onto the residential rooftop for different sun positions;
determining the duration and intensity of shading at various points on the residential rooftop; and
quantifying the impact of shadows on potential solar energy production by comparing shaded and unshaded irradiance levels at affected areas of the residential rooftop.
19. The system of claim 18, wherein the processor is further configured to execute instructions for generating the configuration for the solar module mounting structure based on the optimal placement locations determined by the solar irradiance simulation module.
20. A method for automatically configuring a solar module mounting structure, the method comprises:
receiving, by a processor, site data for a solar module installation site;
performing, by the processor, simulations based on the site data, wherein the simulations include structural load simulations and solar irradiance simulations;
generating, by the processor, a configuration for a solar module mounting structure based on results of the simulations, wherein generating the configuration comprises generating a list of components required for the solar module mounting structure based on results of the simulations and determining strength of the components from the list of components;
validating, by the processor, structural integrity of the configuration based on site data and simulation results; and
generating, by the processor, instructions for installation of the solar module mounting structure based on the configuration.
21. An apparatus for mounting solar modules, comprising:
a plurality of columns configured to be anchored to a surface, wherein the plurality of columns comprises at least one of front columns having a first height, middle columns having a second height, back columns having a third height, wherein the plurality of columns are tapered, and wherein a column of the plurality of columns is of a predefined strength;
a plurality of uniform rafters configured to be attached to the columns, wherein the rafters may be connected to the columns such that the rafters form a bridge between the at least one of the front, the middle, and the back columns, and wherein a rafter of the plurality of rafters is of a predefined strength;
a plurality of purlins configured to be attached to the rafters, wherein the plurality of purlins is connected laterally across multiple rafters, forming a grid-like structure, wherein the solar modules are mounted on the plurality of purlins, and wherein a purlin of the plurality of purlins is of a predefined strength;
a plurality of interconnecting braces between the columns and rafters to enhance lateral stability.

22. The apparatus of claim 21, wherein the plurality of columns comprises:
a corrosion-resistant outer coating;
vertical flanges with pre-formed mounting holes; and
an internal reinforcement structure to enhance load-bearing capacity.
23. The apparatus of claim 21, wherein the plurality of rafters comprises:
standardized slots and fasteners for secure attachment to the columns;
an internal hollow channel for routing electrical conduits; and
a lightweight, high-strength alloy composition to maximize structural integrity while minimizing weight.
24. The apparatus of claim 21, wherein the plurality of purlins comprises:
pre-drilled holes for cable management and assembly;
adjustable mounting brackets to accommodate various solar module sizes; and
integrated and automated grounding mechanisms compliant with electrical codes.
25. The apparatus of claim 21, wherein the thickness of the plurality of columns, the plurality of rafters, and the plurality of purlins varies based on the predefined strength, and wherein the predefined strength is one of light, medium, and heavy.
26. The apparatus of claim 21, further comprising adjustable feet attached to the base of each column to accommodate uneven surfaces and allow for fine-tuning of the structure's levelness post-installation.
27. The apparatus of claim 21, further comprising an integrated and automated weather monitoring system comprising sensors attached to select columns to provide real-time environmental data for system optimization.
28. The apparatus of claim 21, wherein the grid-like structure formed by the rafters and purlins is configurable to support solar modules in both portrait and landscape orientations.