Building energy intelligent control method and system

Abstract

The application provides a building energy intelligent control method and system, the method comprises the following steps: obtaining a three-dimensional model of a building surface, dividing the building surface into grid units, identifying a set of building surface units that meet the light condition by analyzing the light coverage index of each grid unit throughout the year, calculating the hourly predicted irradiance, and predicting the annual power generation potential per unit area of each deployable unit based on the hourly predicted irradiance, obtaining preset design parameters, jointly analyzing the annual power generation potential per unit area of each deployable unit based on the hourly predicted irradiance based on the design parameters, and optimizing the photovoltaic deployment capacity of each unit to obtain a set of units with non-zero deployment capacity, and finally deploying the set of units with non-zero deployment capacity to the building information model in the form of parameterized components. The application greatly improves the design accuracy, operation stability and engineering implementation efficiency of the building photovoltaic system.

Description

Technical Field

[0001] This invention belongs to the field of intelligent building energy control, and particularly relates to an intelligent building energy control method and system. Background Technology

[0002] As building energy systems accelerate their shift towards higher proportions of renewable energy, building-integrated photovoltaics (BIPV) systems are gradually becoming an important pathway to achieving building-specific energy supply and carbon emission reduction. In practical engineering, the geometric, shading, and accessibility differences of building exterior surfaces, especially roofs and facades, make the selection of photovoltaic module deployment areas a critical task in the design phase. However, current common photovoltaic deployment methods are still mainly based on two-dimensional drawings or simple component orientation analysis, only roughly treating the roof and generally resulting in low utilization rates of facades, failing to meet the demand for higher precision utilization of building exterior surface resources in high-density urban environments. Furthermore, existing illumination analysis methods often use empirical solar altitude angle models or fixed shading coefficients, lacking the ability to model annual temporal illumination variations and dynamic shading behavior, leading to significant deviations in facade illumination prediction and making it difficult to meet the refined design requirements for deployment at the unit grid level. At the operational level of photovoltaic (PV) systems, although energy storage systems have gradually become an important component of building energy systems, they are currently mostly configured after PV capacity design. There is a lack of technical approaches to incorporate factors such as the charging and discharging power limitations, dynamic response time, and rapid power change handling capacity of energy storage systems into calculations during the deployment phase. This often leads to a mismatch between PV power generation curves and energy storage regulation capabilities, resulting in wasted power generation or scheduling pressure, and reducing overall system efficiency. Meanwhile, the PV deployment functions in existing BIM platforms remain at the level of geometric component placement, lacking parameterized expressions that are interconnected with power generation prediction models, capacity optimization logic, and scheduling strategies. This results in high costs for cross-platform and multi-software collaboration, significant information loss, and difficulty in achieving a complete process from identifying PV deployment areas to predicting power generation potential, then to energy storage coupling optimization and BIM deployment.

[0003] In summary, there is an urgent need for a building energy intelligent control method and system that can simultaneously solve problems such as insufficient spatial recognition accuracy, lack of dynamic lighting modeling, disconnect between capacity configuration and energy storage regulation, and lack of unified parameter expression in BIM, so as to achieve truly feasible, high-precision, and controllable integrated photovoltaic and energy storage design in complex building environments. Summary of the Invention

[0004] The purpose of this invention is to propose a smart building energy control method and system to solve the above-mentioned problems.

[0005] To achieve the above objectives, a first aspect of the present invention provides a building energy intelligent control method, the method comprising the following steps: S1. Obtain a three-dimensional model of the building surface, divide the building surface into grid cells, and identify a set of deployable building surface cells that meet the lighting conditions by analyzing the annual light coverage index of each grid cell; wherein, each grid cell has the attributes of center coordinates, local fitting normal vector and area; S2. For several deployable units in the set of deployable building surface units, calculate the hourly predicted irradiance by combining their spatial orientation, light coverage index and time-series meteorological data of their location, and predict the annual power generation potential per unit area of ​​each deployable unit based on the hourly predicted irradiance; the time-series meteorological data includes direct irradiance, diffuse irradiance and cloud cover coefficient. S3. Obtain the preset design parameters, and based on the design parameters, perform a joint analysis on the annual power generation potential per unit area of ​​each deployable unit predicted by the hourly irradiance, optimize and determine the photovoltaic deployment capacity of each unit, and finally obtain a set of units with non-zero deployment capacity, so that the volatility of photovoltaic power generation matches the regulation capability of the energy storage system. S4. Deploy the set of units with non-zero deployment capacity into the building information model in the form of parametric components.

[0006] Furthermore, S1 specifically includes: S101. Obtain the BIM model and point cloud data of the target building, and perform unified processing to extract the component surfaces; the component surfaces are represented as a set of points, an attached normal vector, and the area of ​​the component surface. S102. Divide each of the component surfaces into several grid cells of equal area, each of the grid cells having the attributes of center coordinates, local fitting normal vector and area; S103. Obtain the unit vector of the solar incidence direction at each moment, and calculate the solar incidence angle by combining it with the local fitting normal vector of the current grid cell; select the solar incidence angle less than... Furthermore, the grid cells with no obstruction under the unit vector of the solar incident direction at each time moment are used to calculate the corresponding light coverage index; S104. Select grid cells whose light coverage index is greater than a set threshold to construct a set of deployable building surface cells.

[0007] Furthermore, the type of each component surface indicates either a roof or a facade.

[0008] Furthermore, the calculation of hourly predicted irradiance specifically involves: Obtain hourly meteorological data sequences for the location of the target building; Based on the cosine value of the maximum incident angle calculated from the local fitting normal vector and the solar incident direction vector, combined with the direct irradiance and the diffuse irradiance at the current moment, and with the introduction of a structural enhancement term, the hourly predicted irradiance is calculated. The annual power generation potential per unit area is calculated as follows: Based on the hourly predicted irradiance for 8760 hours throughout the year, the annual power generation potential per unit area of ​​each deployable unit is accumulated.

[0009] Furthermore, the structural enhancement term is calculated based on the light coverage index and cloud cover coefficient, and is used in special scenarios where scattered radiation may dominate illumination during periods of severe shading and high cloud cover, thereby improving the model's response to low-light environments.

[0010] Furthermore, the preset design parameters include total energy storage capacity, maximum charging power, dynamic response delay time, and dynamic adjustment capability coefficient.

[0011] Furthermore, S3 specifically refers to: Hourly predicted irradiance based on time series is obtained, and combined with the light coverage index, the mean absolute difference of the light change in the current unit is obtained, along with the value for enhancing shading. When the light coverage index is low, the value for enhancing shading based on the mean absolute difference of the light change in the current unit is amplified to reflect the objective fact of its drastic light fluctuations. Conversely, when the light coverage index is high, the operation of enhancing shading based on the mean absolute difference of the light change in the current unit is naturally weakened to avoid excessive penalty for high-quality surfaces. The deployment capacity allocated to each unit is calculated by combining the annual power generation potential per unit area, the average absolute difference of the current unit's illumination change and the value after enhancing the shading, and the dynamic response delay time. When the average absolute difference of the unit's illumination change and the value after enhancing the shading are large, or the dynamic response delay time is long, the deployment capacity will automatically decrease to avoid the energy storage load exceeding the limit due to high fluctuation units. A total capacity constraint is imposed on the allocation of deployment capacity for all units, ensuring that the total capacity of the allocated deployment capacity of units does not exceed the upper limit of the maximum photovoltaic capacity planned for the project, and that the allocated deployment capacity of each unit does not exceed the structural limit corresponding to its area, resulting in a set of units with non-zero deployment capacity.

[0012] Furthermore, S4 specifically includes: The deployable unit is mapped to a BIM component entity, and a predefined photovoltaic module shape is selected in the component family; all photovoltaic module shapes are bound to component attributes; and the component attributes are connected to the system.

[0013] Furthermore, the component attributes include the component's deployment capacity, the level of annual power generation potential per unit area, the level of solar irradiance index, the strategy type, the strategy response priority, and the number of the energy storage / inverter system connected to the component.

[0014] A second aspect of the present invention provides a building energy intelligent control system, the system comprising: The region identification module is used to acquire a three-dimensional model of the building surface, divide the building surface into grid cells, and identify a set of deployable building surface cells that meet the lighting conditions by analyzing the annual light coverage index of each grid cell; wherein, each grid cell has the attributes of center coordinates, local fitting normal vector and area; The power generation potential prediction module is used to calculate the hourly predicted irradiance for several deployable units of the set of deployable building surface units, in combination with their spatial orientation, light coverage index and time-series meteorological data of their location, and to predict the annual power generation potential per unit area of ​​each deployable unit based on the hourly predicted irradiance; the time-series meteorological data includes direct irradiance, diffuse irradiance and cloud cover coefficient. The deployment optimization module is used to obtain the preset design parameters, and based on the design parameters, to jointly analyze the annual power generation potential per unit area of ​​each deployable unit predicted by the hourly predicted irradiance, optimize and determine the photovoltaic deployment capacity of each unit, and finally obtain a set of units with non-zero deployment capacity, so that the volatility of photovoltaic power generation matches the regulation capability of the energy storage system. The BIM deployment module is used to deploy the set of units with non-zero deployment capacity into the building information model in the form of parametric components.

[0015] The beneficial technical effects of the present invention are at least as follows: This invention addresses key issues in existing building photovoltaic (PV) design processes, such as coarse spatial identification, insufficient accuracy in predicting power generation potential, separate design of PV and energy storage systems, and fragmented BIM deployment. It proposes an integrated intelligent control method and system for building energy systems. First, based on a 3D building model, the method identifies refined grid units on the roof and facades facing different orientations that meet installation requirements through component-level surface partitioning, mesh modeling, and visibility analysis based on solar paths. It also constructs accessibility indicators that reflect shading behavior. Building upon this, and combining hourly meteorological data of the building's location, a time-series irradiance prediction model is constructed using the interaction between surface orientation, shading ratio, and irradiance. Enhancements suitable for complex shading environments are introduced to achieve unit-level annual power generation potential calculation. Furthermore, this invention incorporates the charging and discharging limitations, dynamic response time, and capacity to withstand rapid power changes of the energy storage system into the deployment phase. By constructing a capacity allocation model based on matching power generation volatility with energy storage responsiveness, the PV deployment results and energy storage regulation capabilities are designed in a consistent manner, avoiding over-deployment and scheduling bottlenecks. Ultimately, this invention embeds the aforementioned prediction and optimization results into the BIM system as component-level parameters. Through component spatial transformation, parameter encapsulation, and system mapping mechanisms, the deployment location, capacity, power generation potential level, shading level, control strategy mode, and priority of each grid unit are fully bound to the BIM model. This achieves an integrated data link from identifying photovoltaic deployment areas to modeling power generation potential, from optimizing deployment capacity to generating energy storage regulation strategies, and from algorithm results to engineering deployment. This invention effectively solves the problems of insufficient utilization of facade resources in complex building environments, large deviations in time-series illumination prediction, weak synergy between energy storage and photovoltaic systems, and fragmented BIM deployment, significantly improving the design accuracy, operational stability, and engineering implementation efficiency of building photovoltaic systems. Attached Figure Description

[0016] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0017] Figure 1 This is a flowchart of a building energy intelligent control method according to the present invention. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0019] like Figure 1As shown in the figure, an embodiment of the present invention provides a building energy intelligent control method, the method comprising: S1. Obtain a three-dimensional model of the building surface, divide the building surface into grid units, and identify a set of deployable building surface units that meet the lighting conditions by analyzing the annual light coverage index of each grid unit; wherein, each grid unit has the attributes of center coordinates, local fitting normal vector and area.

[0020] Specifically, this step aims to identify building surface areas suitable for photovoltaic module installation from the building's 3D model, paying particular attention to components with shading characteristics and complex geometries, such as roofs and facades. Traditional deployment strategies typically rely on designers' subjective judgment of deployment areas or selection based solely on roof orientation and slope. In this invention, point cloud spatial data or BIM geometric component data is used. Through component partitioning, meshing, and shading angle analysis, a light accessibility index for each candidate installation unit is constructed, and this index serves as the basis for outputting the deployable areas.

[0021] The input data comes from structured 3D data sources of the building, supporting two types of sources. The first type is BIM models, in formats such as IFC or RVT, which contain the geometry, spatial coordinates, normal attributes, and component type labels of the components. These can be exported as a collection of structured components through interfaces such as Revit API. The second category consists of point cloud data generated by 3D laser scanning or photogrammetry, which requires extracting component surfaces with geometric boundaries through region clustering and plane fitting. After unification, each component... Represented as a point set Attached normal vector ,area Component type marking , where 1 represents the roof and 2 represents the facade.

[0022] Furthermore, after the components are extracted, the surface of each component is divided into equal-area mesh units. Each unit has a center coordinate. Local fitting normal vector ,area Light assessment uses a set of typical sunshine hours. The unit vector corresponding to the direction of solar incidence at each moment This can be obtained based on geographic coordinates, date, and time using a solar position calculation model (such as SPA). To quantify the year-round sunlight reception capacity of a unit, a sunlight coverage index is defined. :

[0023] in, This is an indicator function that takes the value 1 when the condition is true. The visibility function represents the direction. Lower grid cell Whether there is any obstruction. This function is implemented using a ray tracing method based on accelerated structures (such as BVH) to detect whether light rays emitted from the direction of the sun are blocked by other structures. When When, it means the angle of incidence of the sun is less than It has the potential for irradiation.

[0024] Taking an office building in a certain city as an example, the south-facing curtain wall is divided into approximately 4,800 sections. The study found that approximately 21% of the units had insufficient unobstructed sunlight exposure at any time of year. This indicates that building shading has a significant impact on the selection of deployment areas. To eliminate units that lack light value, a shading threshold is set. Only retain those that meet the requirements. The cells are selected as the final candidate deployment areas:

[0025] The final output is a set of grid cells. Each cell contains the coordinates of its center point. Normal vector ,area and the component number This set will serve as input for photovoltaic potential modeling in the next step, used for surface-level temporal irradiance prediction. In engineering implementation, this method does not rely on high-computing-power models; instead, it combines geometric properties and time path analysis to construct a high-precision, distributed, and deployable deployment area identification mechanism, making it particularly suitable for urban building structures with facade shading or complex boundaries.

[0026] S2. For several deployable units in the set of deployable building surface units, calculate the hourly predicted irradiance by combining their spatial orientation, light coverage index and time-series meteorological data of their location, and predict the annual power generation potential per unit area of ​​each deployable unit based on the hourly predicted irradiance; the time-series meteorological data includes direct irradiance, diffuse irradiance and cloud cover coefficient.

[0027] Specifically, this step uses the set of deployable building surface units output from step one. Using this as input, a unit-level annual power generation potential prediction model is constructed to provide a high-resolution, structurally adaptable input foundation for subsequent photovoltaic capacity configuration and energy storage regulation design. In the urban building environment addressed by this invention, building facades and roofs are not only complex in orientation but also affected by factors such as long-term shading, enhanced reflection, and climate fluctuations. Simply relying on surface orientation and empirical illumination models cannot accurately estimate the unit's power generation potential. Therefore, this step adopts a lightweight time-series modeling approach that combines geometric factors and dynamic meteorological characteristics, proposing an illumination modeling formula that incorporates an enhanced shading term, and performing integrated calculations of annual power generation capacity.

[0028] The input data includes: the center coordinates of each deployment unit. Local normal vector Unit area and occlusion ratio index (Calculated from step one), and hourly meteorological data series for the building's location. ,in , representing direct irradiance, diffuse irradiance, and cloud cover coefficient, respectively. Solar incident direction vector. The result is calculated using a solar position algorithm based on latitude, longitude, and time, and is expressed as a three-dimensional unit vector.

[0029] Furthermore, based on this, the present invention proposes the following prediction formula for modeling hourly cell irradiance:

[0030] in, Indicates the first Hour, Unit Predicted irradiance; This represents the direct irradiance at that moment, and the meteorological input per unit area. The cosine of the incident angle of the light is the angle between the ray and the surface normal. This refers to the diffuse irradiance. For the scattering-to-reception ratio, a recommended value is [value to be filled in]. ; The occlusion reachability of this cell, ranging from... This is calculated from step one using the ratio of available hours throughout the year; This is the cloud cover coefficient at that moment, representing the degree of sky obstruction; The adjustment coefficient for the occlusion enhancement is set to [value]. .

[0031] This formula introduces This term describes a special scenario where scattered radiation may dominate illumination during periods of severe shading and high cloud cover, thereby improving the model's response to low-light environments. It is a precision correction mechanism specifically designed for situations where facade building shading is significant in this invention.

[0032] Based on the hourly irradiance predicted for 8760 hours throughout the year Furthermore, the annual power generation potential per unit area of ​​this unit can be calculated:

[0033] in, For unit Annual power generation capacity per unit area; For the overall conversion efficiency of the photovoltaic system, a value of [value to be specified] is recommended. Factors include component conversion losses, inverter efficiency, and line transmission. The time step is in hours, and is set to... ; This represents the number of hours throughout the year. The calculation process is as follows: For each... Extract from step one , , And calculate the whole year in combination with the building location. With meteorological input Substitute into the formula and calculate hourly. Finally, the sum is obtained The results will serve as input for subsequent steps, including selecting photovoltaic deployment capacity and assessing energy storage regulation capabilities. This modeling process highlights the interplay between shading characteristics and time-varying meteorological information, and introduces structural enhancement terms rarely used in solar illumination forecasting. In urban architectural environments with strong shading and rapid changes, it can significantly improve the accuracy of assessments and achieve a logical closed loop with the geometric analysis method in the previous step, thus possessing clear engineering applicability.

[0034] S3. Obtain the preset design parameters, and based on the design parameters, perform a joint analysis on the annual power generation potential per unit area of ​​each deployable unit predicted by the hourly irradiance, optimize and determine the photovoltaic deployment capacity of each unit, and finally obtain a set of units with non-zero deployment capacity, so that the volatility of photovoltaic power generation matches the regulation capability of the energy storage system.

[0035] Specifically, this step involves the unit-level annual power generation potential already obtained in step two. Hourly irradiance prediction And the spatial entity parameters output in step one (including center coordinates) Local fitting normal vector Occlusion ratio and area Building upon existing methods, this step introduces the dynamic response characteristics of energy storage systems to jointly optimize the deployment area and capacity of building-integrated photovoltaic (BIPV) systems. The rationale behind this step is that the power generation behavior of BIPV systems exhibits significant temporal instability, especially in urban building environments with high shading, complex facades, and frequent local environmental changes. Hourly irradiance variations in BIPV units can then exhibit significant transient fluctuations. Energy storage systems, on the other hand, have limited power regulation capabilities and non-negligible response lag. If the deployment phase focuses solely on power generation potential, the energy storage system may be unable to absorb rapidly increasing power output, leading to energy waste or equipment overload. Therefore, this step incorporates the dynamic constraints of the energy storage system into the deployment planning stage, ensuring that the final deployment scheme matches the actual controllability of the energy storage system.

[0036] The input includes all valid units output from the previous stage. Power generation potential Hourly irradiance prediction Geometric features , Obstruction index and area Furthermore, the design parameters for the energy storage system are provided by the project designers, including: Total energy storage capacity (e.g., 100kWh); Maximum charging power (e.g., 30kW); Maximum discharge power (e.g., 30kW); Dynamic response delay time (e.g., 0.5h) represents the delay between the energy storage system receiving a charge / discharge command and the actual power change.

[0037] Dynamic adjustment capability coefficient (0 to 1) is used to represent the system's ability to withstand rapid power changes.

[0038] Furthermore, to reflect the local photovoltaic fluctuation characteristics in complex building-shaded environments, this step introduces a "shading-sensitive fluctuation factor" into the deployment model. By controlling the proportion of cells with unstable lighting behavior in the deployment capacity, the deployment result can achieve greater stability. First, we construct the cells. "Normalized dynamic illumination fluctuations" in the time series:

[0039] in, Represents the mean absolute difference of unit illumination changes and enhances occlusion processing; when A lower value (indicating frequent shading of the unit) will be amplified to reflect the objective fact of drastic fluctuations in illumination; while when... If the value is high (indicating no obstruction), this criterion will naturally be reduced to avoid over-penalizing high-quality surfaces.

[0040] Furthermore, to ensure that the deployed capacity matches the dynamic adjustment capability of energy storage, this invention proposes to jointly model power generation potential and volatility, and allocate the deployed capacity to each unit using the following capacity optimization formula. :

[0041] in, To allocate to the unit For photovoltaic capacity (e.g., kW level), the denominator in this formula reflects dynamic stability constraints: when Larger, or dynamic response delay time Over a longer period, the deployed capacity will automatically decrease to avoid energy storage load exceeding limits due to high fluctuations in the unit. This indicates the system's sensitivity to dynamic fluctuations; the higher the value, the stricter the capacity limitations for the fluctuation range. This formula has a clear engineering implication: the more severe the fluctuations in power generation from a single unit and the slower the energy storage response, the smaller the deployable capacity of that unit, thus ensuring the overall stability of the system.

[0042] After calculation using the above formula, for all Implement total quantity constraints to make Not exceeding the maximum photovoltaic capacity limit planned for the project, for example kW, and ensure that each unit The capacity must not exceed the structural limits corresponding to its area, for example, by limiting it to a maximum of 0.2kW per square meter according to engineering standards. This results in a set of units with non-zero capacity.

[0043] The output includes the deployment capacity of each unit. This includes the unit's spatial location, normal vector, and component number. These outputs will serve as core parameters required for deploying the photovoltaic component in the BIM model in the next step.

[0044] S4. Deploy the set of units with non-zero deployment capacity into the building information model in the form of parametric components.

[0045] Specifically, this step is based on the deployment unit set output in the previous stage. and their corresponding deployment capacity This paper proposes a deployment mechanism that integrates photovoltaic (PV) modules and energy storage devices into a Building Information Modeling (BIM) system. The goal is not only to mark the geometric locations of the modules, but also to endow the deployment results with engineering attributes such as "can be called," "can be recognized," and "can be scheduled" through parameter binding and component encapsulation. Currently, most BIM systems support PV systems at the level of simple component placement, lacking the ability to bind energy efficiency simulation with structural parameters. This paper proposes a deployment mechanism that combines spatial positioning, component parameter encapsulation, and system configuration mapping to ensure that power generation forecasting, capacity configuration, and scheduling logic can be directly reproduced in the BIM system.

[0046] Input data includes each deployment unit spatial coordinates Normal vector Layout area Allocation capacity and the annual power generation potential per unit area obtained from step two. With occlusion ratio In addition, it also includes the scheduling strategy type determined in step three. and response priority These control parameters need to be synchronously written into the BIM component properties.

[0047] The specific deployment process includes the following key stages: First, component geometry generation and spatial placement. Each layout unit... Mapped to a BIM component entity Select a predefined photovoltaic module shape from the component family. Its spatial position and orientation are determined by its center coordinates. and normal vector To ensure the installation angle aligns with the surface orientation, a local transformation matrix needs to be constructed for spatial alignment. This step defines the transformation matrix for component placement as follows:

[0048] in, Number the components The local coordinate system transformation matrix of the building surface; To ensure that the photovoltaic module faces the incident direction, a rotation matrix is ​​generated based on the unit normal vector. This is a scaling matrix used to adjust the size of components so that they visually match the actual layout area and capacity.

[0049] Second, deploy parameter encapsulation. Based on the generated component geometry, all energy efficiency simulation results need to be bound to component attributes. BIM systems such as Revit allow adding parameter fields (such as "SharedParameters" or "FamilyParameters") to components. This step writes the following parameter fields for each component: PV_Capacity: From , indicating the corresponding deployment capacity of the component (unit: kW); Irradiation_Class: will Divided into 5 levels (e.g., [low / low-medium / medium / high-medium / high]); Shading_Level: will It is divided into 3 levels (high occlusion, medium occlusion, no occlusion); Control_Mode: Policy type Examples include fixed power output, solar tracking, and peak shaving and valley filling. Priority_Index: Policy response priority The smaller the value, the faster the response; System_ID: The number of the energy storage / inverter system to which this component is connected, used for physical system association.

[0050] After deployment, these fields can be read and used by subsequent energy efficiency simulation modules, scheduling platforms, or construction units. For example, the Revit platform can automatically identify component parameters and extract control strategies in batches through IFC export or the Revit API interface; coordination software such as Navisworks can also be used to display the location of high-shading units, thereby assisting in the inspection and maintenance of photovoltaic systems.

[0051] Third, exporting the results and integrating them with the system. After component generation and attribute injection, this step will generate three output files for system integration: Component deployment model file: This refers to the parameterized component model (such as .rvt or .ifc format) saved by the BIM platform for use in design and construction. Photovoltaic System Statistics Table: Automatically summarizes all The capacity, area, coordinates, and control strategies of the components are exported as a CSV table for use in bidding and procurement. Control policy interface file: Export scheduling policy parameter list (including...) and This is for use by the building energy management system (EMS), and the format can be XML or JSON.

[0052] To ensure consistent data mapping, this invention establishes a parameter mapping dictionary, where each item... This indicates the binding between component field names and actual variables, such as "PV_Capacity" → This ensures that all platforms can identify data content based on unified parameter key values.

[0053] This invention also provides a building energy intelligent control system, the system comprising: The region identification module is used to acquire a three-dimensional model of the building surface, divide the building surface into grid cells, and identify a set of deployable building surface cells that meet the lighting conditions by analyzing the annual light coverage index of each grid cell; wherein, each grid cell has the attributes of center coordinates, local fitting normal vector and area; The power generation potential prediction module is used to calculate the hourly predicted irradiance for several deployable units of the set of deployable building surface units, in combination with their spatial orientation, light coverage index and time-series meteorological data of their location, and to predict the annual power generation potential per unit area of ​​each deployable unit based on the hourly predicted irradiance; the time-series meteorological data includes direct irradiance, diffuse irradiance and cloud cover coefficient. The deployment optimization module is used to obtain the preset design parameters, and based on the design parameters, to jointly analyze the annual power generation potential per unit area of ​​each deployable unit predicted by the hourly predicted irradiance, optimize and determine the photovoltaic deployment capacity of each unit, and finally obtain a set of units with non-zero deployment capacity, so that the volatility of photovoltaic power generation matches the regulation capability of the energy storage system. The BIM deployment module is used to deploy the set of units with non-zero deployment capacity into the building information model in the form of parametric components.

[0054] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0055] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or units may be electrical, mechanical, or other forms.

[0056] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0057] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for intelligent control of building energy, characterized in that, The method includes: S1. Obtain a three-dimensional model of the target building surface, divide the building surface into grid cells, and identify a set of deployable building surface cells that meet the lighting conditions by analyzing the annual light coverage index of each grid cell; wherein, each grid cell has the attributes of center coordinates, local fitting normal vector and area; S2. For several deployable units in the set of deployable building surface units, calculate the hourly predicted irradiance by combining their spatial orientation, light coverage index and time-series meteorological data of their location, and predict the annual power generation potential per unit area of ​​each deployable unit based on the hourly predicted irradiance; the time-series meteorological data includes direct irradiance, diffuse irradiance and cloud cover coefficient. S3. Obtain the preset design parameters, and based on the design parameters, perform a joint analysis on the annual power generation potential per unit area of ​​each deployable unit predicted by the hourly irradiance, optimize and determine the photovoltaic deployment capacity of each unit, and finally obtain a set of units with non-zero deployment capacity, so that the volatility of photovoltaic power generation matches the regulation capability of the energy storage system. S4. Deploy the set of units with non-zero deployment capacity into the building information model in the form of parametric components.

2. The intelligent building energy control method according to claim 1, characterized in that, S1 specifically includes: S101. Obtain the BIM model and point cloud data of the target building, and perform unified processing to extract the component surfaces; the component surfaces are represented as a set of points, an attached normal vector, and the area of ​​the component surface. S102. Divide each of the component surfaces into several grid cells of equal area, each of the grid cells having the attributes of center coordinates, local fitting normal vector and area; S103. Obtain the unit vector of the solar incidence direction at each moment, and calculate the solar incidence angle by combining it with the local fitting normal vector of the current grid cell; select the solar incidence angle less than... Furthermore, the grid cells with no obstruction under the unit vector of the solar incident direction at each time moment are used to calculate the corresponding light coverage index; S104. Select grid cells whose light coverage index is greater than a set threshold to construct a set of deployable building surface cells.

3. The intelligent building energy control method according to claim 2, characterized in that, The type of each component surface indicates either the roof or the facade.

4. The intelligent building energy control method according to claim 1, characterized in that, The calculation of hourly predicted irradiance is specifically as follows: Obtain hourly meteorological data sequences for the location of the target building; Based on the cosine value of the maximum incident angle calculated from the local fitting normal vector and the solar incident direction vector, combined with the direct irradiance and the diffuse irradiance at the current moment, and with the introduction of a structural enhancement term, the hourly predicted irradiance is calculated. The annual power generation potential per unit area is calculated as follows: Based on the hourly predicted irradiance for 8760 hours throughout the year, the annual power generation potential per unit area of ​​each deployable unit is accumulated.

5. The intelligent building energy control method according to claim 4, characterized in that, The structural enhancement terms are calculated based on the light coverage index and cloud cover coefficient. They are used in special scenarios where scattered radiation may dominate illumination during periods of severe shading and high cloud cover, thereby improving the model's response to low-light environments.

6. The intelligent building energy control method according to claim 1, characterized in that, The preset design parameters include total energy storage capacity, maximum charging power, dynamic response delay time, and dynamic adjustment capability coefficient.

7. The intelligent building energy control method according to claim 1, characterized in that, Specifically, S3 is: Hourly predicted irradiance based on time series is obtained, and combined with the light coverage index, the mean absolute difference of the light change in the current unit is obtained, along with the value for enhancing shading. When the light coverage index is low, the value for enhancing shading based on the mean absolute difference of the light change in the current unit is amplified to reflect the objective fact of its drastic light fluctuations. Conversely, when the light coverage index is high, the operation of enhancing shading based on the mean absolute difference of the light change in the current unit is naturally weakened to avoid excessive penalty for high-quality surfaces. The allocated capacity for each unit is calculated by combining the annual power generation potential per unit area, the average absolute difference of the current unit's illumination change, the value of enhanced shading processing, and the dynamic response delay time. When the average absolute difference of unit illumination variation and the value of enhanced shading processing are large, or the dynamic response delay time is long, the deployed capacity will automatically decrease to avoid energy storage load exceeding limits due to high fluctuation units. A total capacity constraint is imposed on the allocation of deployment capacity for all units, ensuring that the total capacity of the allocated deployment capacity of units does not exceed the upper limit of the maximum photovoltaic capacity planned for the project, and that the allocated deployment capacity of each unit does not exceed the structural limit corresponding to its area, resulting in a set of units with non-zero deployment capacity.

8. The intelligent building energy control method according to claim 1, characterized in that, Specifically, S4 is: The deployable unit is mapped to a BIM component entity, and a predefined photovoltaic module shape is selected in the component family; all photovoltaic module shapes are bound to component attributes; and the component attributes are connected to the system.

9. A building energy intelligent control method according to claim 8, characterized in that, The component attributes include the component's deployment capacity, the level of annual power generation potential per unit area, the level of solar irradiance index, the strategy type, the strategy response priority, and the number of the energy storage / inverter system connected to the component.

10. A building energy intelligent control system, characterized in that, The system includes: The region identification module is used to acquire a three-dimensional model of the building surface, divide the building surface into grid cells, and identify a set of deployable building surface cells that meet the lighting conditions by analyzing the annual light coverage index of each grid cell; wherein, each grid cell has the attributes of center coordinates, local fitting normal vector and area; The power generation potential prediction module is used to calculate the hourly predicted irradiance for several deployable units of the set of deployable building surface units, in combination with their spatial orientation, light coverage index and time-series meteorological data of their location, and to predict the annual power generation potential per unit area of ​​each deployable unit based on the hourly predicted irradiance; the time-series meteorological data includes direct irradiance, diffuse irradiance and cloud cover coefficient. The deployment optimization module is used to obtain the preset design parameters, and based on the design parameters, to jointly analyze the annual power generation potential per unit area of ​​each deployable unit predicted by the hourly predicted irradiance, optimize and determine the photovoltaic deployment capacity of each unit, and finally obtain a set of units with non-zero deployment capacity, so that the volatility of photovoltaic power generation matches the regulation capability of the energy storage system. The BIM deployment module is used to deploy the set of units with non-zero deployment capacity into the building information model in the form of parametric components.

Images