Method and device for determining building envelope of substation and computer equipment

Abstract

The application discloses a kind of transformer substation enclosure determination method, device and computer equipment.Therein, the method comprises: obtaining the multiple historical load rates of transformer in target transformer substation, the operation data of target transformer substation and the building data of target transformer substation;Based on multiple historical load rates and operation data, construct internal disturbance prediction model;Based on internal disturbance prediction model and building data, construct energy consumption prediction model;Based on energy consumption prediction model, construct multi-objective optimization model using genetic algorithm, wherein the multi-objective optimization model minimizes energy consumption, carbon emissions and cost as optimization target;Based on multi-objective optimization model, determine the target design parameters of enclosure, wherein the target design parameters include insulation material type, insulation layer thickness and outer window type.The application solves the technical problem that mechanical equipment heat dissipation dynamic change is ignored in conventional method.

Description

Technical Field

[0001] The present invention relates to the field of electric power engineering, and in particular to a method, device and computer equipment for determining a transformer substation enclosure structure. Background Art

[0002] As a key node between the power system and energy-consuming industries, improving substation building performance, especially in terms of reducing energy consumption and minimizing environmental impact, is crucial for optimizing power system performance. The building envelope, a crucial component of substation buildings, not only isolates the internal and external environments but also directly impacts the building's energy consumption and carbon emissions. Therefore, precise optimization of the building envelope during the design and planning phase is crucial for improving substation building performance, reducing energy consumption and carbon emissions, while maintaining good economic efficiency and environmental sustainability.

[0003] While multi-objective optimization design methods for building envelopes exist for residential and office buildings, these methods are not fully applicable to the specific industrial building type of substations. The thermal environment within a substation differs significantly from that of traditional buildings. In particular, the dynamic heat dissipation generated by power equipment such as transformers during operation directly impacts both the calculation of building energy consumption and the design of the building envelope. Existing optimization design methods often overlook this critical factor, failing to accurately guide the optimal design of substation envelopes, significantly reducing their potential for energy conservation and carbon reduction.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The embodiments of the present invention provide a method, apparatus, and computer equipment for determining a substation enclosure structure, so as to at least solve the technical problem of ignoring the dynamic changes in heat dissipation of mechanical equipment in traditional methods.

[0006] According to one aspect of an embodiment of the present invention, a method for determining a substation enclosure structure is provided, comprising: obtaining multiple historical load rates of transformers in a target substation, operating data of the target substation, and building data of the target substation, wherein the operating data includes personnel density, lighting power, and historical equipment operating data, and the building data includes geometric structure data, temperature data, and temperature control mode; constructing an internal disturbance prediction model based on the multiple historical load rates and operating data, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer within a preset future time period; constructing an energy consumption prediction model based on the internal disturbance prediction model and the building data, wherein the independent variables of the energy consumption prediction model include the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient, and exterior window solar heat gain coefficient of the enclosure structure of the target substation, and the dependent variable is the total energy consumption for air conditioning and ventilation of the target substation; constructing a multi-objective optimization model based on the energy consumption prediction model using a genetic algorithm, wherein the multi-objective optimization model takes minimizing energy consumption, carbon emissions, and costs as optimization objectives; and determining target design parameters of the enclosure structure based on the multi-objective optimization model, wherein the target design parameters include the type of insulation material, the thickness of the insulation layer, and the type of exterior windows.

[0007] Optionally, an internal disturbance prediction model is constructed based on multiple historical load rates and operating data, including: based on multiple historical load rates, using a neural network to predict the load rate of the transformer at multiple preset future moments; based on the load rates and operating data at multiple preset future moments, determining the heat dissipation of the transformer at multiple preset future moments; based on the heat dissipation at multiple preset future moments, constructing an internal disturbance prediction model.

[0008] Optionally, an energy consumption prediction model is constructed based on the internal disturbance prediction model and building data, including: constructing a physical model of the target substation based on the building data; constructing an energy consumption simulation model based on the physical model and the internal disturbance prediction model, wherein the energy consumption simulation model is used to simulate the operating status of the target substation; determining multiple value combinations of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient based on multiple values ​​corresponding to each of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient; based on the energy consumption simulation model, simulating the operating status of the target substation corresponding to multiple value combinations, and obtaining multiple air conditioning and ventilation total energy consumption data; constructing an energy consumption prediction model through multivariate nonlinear regression fitting based on multiple value combinations and the corresponding total air conditioning and ventilation energy consumption data.

[0009] Optionally, based on the energy consumption prediction model, a multi-objective optimization model is constructed using a genetic algorithm, including: obtaining the heat transfer coefficient, carbon emission factor and cost corresponding to each of multiple insulation material types; based on the heat transfer coefficient, carbon emission factor and cost corresponding to each of multiple insulation material types, a carbon emission calculation model and a cost calculation model are constructed, wherein the independent variables of the carbon emission calculation model and the cost calculation model are the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient, the dependent variable of the carbon emission calculation model is the carbon emissions of the target substation, and the dependent variable of the cost calculation model is the construction cost of the enclosure structure; based on the energy consumption prediction model, the carbon emission calculation model and the cost calculation model, a multi-objective optimization model is constructed using a non-dominated sorting multi-objective genetic algorithm.

[0010] Optionally, based on a multi-objective optimization model, target design parameters of the envelope structure are determined, including: calculating a solution set of the multi-objective optimization model, wherein the solution set includes multiple solutions, one solution representing a set of values ​​of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient, and the exterior window solar heat gain coefficient; based on the ideal point method, determining a target solution in the solution set; and determining target design parameters based on the target solution.

[0011] Optionally, based on the ideal point method, the target solution in the solution set is determined, including: based on the single-objective genetic algorithm, respectively determining the optimal solutions corresponding to multiple single-objective optimization models in the multi-objective optimization model; combining the optimal solutions corresponding to multiple single-objective optimization models in the multi-objective optimization model to determine the corresponding ideal points in the preset multi-dimensional space; determining multiple points corresponding to multiple solutions in the solution set in the preset multi-dimensional space; respectively calculating the Euclidean distances from the multiple points to the ideal point; determining the point with the smallest Euclidean distance to the ideal point among the multiple points as the target point; and determining the solution corresponding to the target point as the target solution.

[0012] According to another aspect of an embodiment of the present invention, a device for determining a transformer substation enclosure structure is provided, comprising: an acquisition module for acquiring multiple historical load rates of transformers in a target transformer substation, operating data of the target transformer substation, and building data of the target transformer substation, wherein the operating data includes personnel density, lighting power, and historical equipment operating data, and the building data includes geometric structure data, temperature data, and temperature control mode; a first construction module for constructing an internal disturbance prediction model based on multiple historical load rates and operating data, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer in a preset future time period; a second construction module for constructing an internal disturbance prediction model based on the internal disturbance prediction model An energy consumption prediction model is constructed based on the model and building data, wherein the independent variables of the energy consumption prediction model include the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient and exterior window solar heat gain coefficient of the enclosure structure of the target substation, and the dependent variable is the total energy consumption of air conditioning and ventilation of the target substation; a third construction module is used to construct a multi-objective optimization model based on the energy consumption prediction model using a genetic algorithm, wherein the multi-objective optimization model takes minimization of energy consumption, carbon emissions and cost as optimization objectives; a determination module is used to determine the target design parameters of the enclosure structure based on the multi-objective optimization model, wherein the target design parameters include the type of insulation material, the thickness of the insulation layer and the type of exterior window.

[0013] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, which includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any one of the above-mentioned methods for determining the substation enclosure structure.

[0014] According to another aspect of an embodiment of the present invention, a computer device is provided. The computer device includes a processor, and the processor is used to run a program. When the program is run, any one of the above-mentioned methods for determining a substation enclosure structure is executed.

[0015] According to another aspect of the embodiments of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, any one of the above-mentioned methods for determining a substation enclosure structure is implemented.

[0016] In an embodiment of the present invention, a method for determining a transformer substation enclosure structure is adopted, by obtaining multiple historical load rates of transformers in a target transformer substation, operating data of the target transformer substation, and building data of the target transformer substation, wherein the operating data includes personnel density, lighting power, and historical equipment operating data, and the building data includes geometric structure data, temperature data, and temperature control mode; based on multiple historical load rates and operating data, an internal disturbance prediction model is constructed, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer in a preset future time period; based on the internal disturbance prediction model and building data, an energy consumption prediction model is constructed, wherein the independent variables of the energy consumption prediction model include the external wall transmission data of the enclosure structure of the target transformer substation, ... Thermal coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient and exterior window solar heat gain coefficient, and the dependent variable is the total energy consumption of air conditioning and ventilation of the target substation; based on the energy consumption prediction model, a multi-objective optimization model is constructed using genetic algorithm, wherein the multi-objective optimization model takes minimization of energy consumption, carbon emissions and cost as optimization objectives; based on the multi-objective optimization model, the target design parameters of the envelope structure are determined, wherein the target design parameters include insulation material type, insulation layer thickness and exterior window type, thereby achieving the purpose of scientifically determining the design parameters of the envelope structure, thereby achieving the technical effect of improving the accuracy of the envelope structure design, and thus solving the technical problem of ignoring the dynamic changes of heat dissipation of mechanical equipment in the traditional method. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for determining a substation enclosure structure is shown;

[0019] Figure 2 is a flow chart of a method for determining a substation enclosure structure according to an embodiment of the present invention;

[0020] Figure 3 is a schematic diagram of a physical model of an indoor substation provided according to an optional embodiment of the present invention;

[0021] Figure 4 1 is a flow chart of a multi-objective optimization method for a substation building envelope structure taking into account dynamic changes in transformer load rate according to an optional embodiment of the present invention;

[0022] Figure 5 4 is a structural block diagram of a device for determining a substation enclosure structure provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] According to an embodiment of the present invention, an embodiment of a method for determining a substation enclosure structure is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing a method for determining a substation enclosure structure is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (illustrated as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0027] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be fully or partially integrated into any of the other components of the computer terminal 10. As discussed in the embodiments of the present application, the data processing circuitry functions as a processor control (e.g., the selection of a variable resistor terminal path connected to an interface).

[0028] The memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the method for determining the substation enclosure structure in the embodiment of the present invention. The processor executes the software programs and modules stored in the memory 104 to execute various functional applications and data processing, thereby implementing the method for determining the substation enclosure structure of the application. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0029] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0030] Figure 2 FIG. 1 is a flow chart of a method for determining a substation enclosure structure according to an embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0031] Step S201: Acquire multiple historical load rates of transformers in a target substation, operating data of the target substation, and building data of the target substation. The operating data includes personnel density, lighting power, and historical equipment operating data, and the building data includes geometric structure data, temperature data, and temperature control mode.

[0032] In this step, historical load factors serve as the basis for assessing transformer operating conditions and heat dissipation patterns. By collecting load factor data for transformers in target substations at different time points (for example, at different hours of the day, in different seasons, and under different weather conditions), a predictive model for the dynamic changes in transformer load factors can be constructed. This model is crucial for accurately predicting the heat generated by transformer operation within the substation and forms the basis for predicting substation energy consumption and carbon emissions.

[0033] Operational data includes personnel density and lighting power density within the substation, as well as historical equipment operating data. These data provide information on the heat generated within the substation due to personnel activity and lighting equipment usage, which is crucial for developing a building internal disturbance prediction model. Historical equipment operating data covers not only transformers but also other electrical equipment that may affect the building's internal thermal environment, such as switchgear, capacitors, and reactors. By analyzing the operating data of these devices, we can estimate their heat dissipation and refine the building internal disturbance prediction model to ensure that it fully reflects the heat generation within the substation.

[0034] Building data includes the target substation's geometric structure data, temperature data, and temperature control methods. Geometric data, such as the building's size, shape, and enclosure type, is crucial for building physical models and energy consumption prediction models, helping to simulate the flow and consumption of energy within the building. Temperature data includes temperature records for each room within the substation, which helps understand temperature variations at different times and seasons, providing a practical basis for energy consumption prediction. Temperature control methods involve how the substation maintains indoor temperature through systems such as air conditioning and ventilation. This information is crucial for considering the energy consumption of air conditioning and ventilation systems when building energy consumption prediction models.

[0035] Step S202 : constructing an internal disturbance prediction model based on a plurality of historical load rates and operation data, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer in a preset future period.

[0036] In this step, historical data of the transformer load rate of the main transformer room of similar substation buildings can be collected as a sample set, and the sample set can be divided into a training set and a test set according to the holdout method. The target substation building transformer load rate prediction model based on the deep learning neural network is trained with the training set, and the transformer load rate prediction model is tested with the test set.

[0037] Based on the transformer load rate prediction model for the target substation building obtained in the above steps, combined with the mathematical formula for transformer equipment heat dissipation and information such as lighting power density during the operation phase of similar substation buildings obtained from research (i.e., operating data), a prediction model for internal disturbances in the target substation building based on the dynamic changes in the transformer load rate can be constructed. The mathematical formula for transformer equipment heat dissipation is shown below:

[0038]

[0039] in, is the total loss in W; is the no-load loss, in W; is the load factor; is the load loss in W.

[0040] In step S203, an energy consumption prediction model is constructed based on the internal disturbance prediction model and the building data, wherein the independent variables of the energy consumption prediction model include the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient, and exterior window solar heat gain coefficient of the enclosure structure of the target substation, and the dependent variable is the total energy consumption of air conditioning and ventilation of the target substation.

[0041] In this step, the energy consumption prediction model can be constructed based on the output of the internal disturbance prediction model and the physical characteristics of the building, with the focus on analyzing the impact of the thermal performance of the envelope on the energy consumption of the air conditioning and ventilation system. The thermal performance of the envelope, especially the heat transfer coefficient of the exterior wall ( ), roof heat transfer coefficient ( )、Exterior window heat transfer coefficient( ) and the exterior window solar heat gain coefficient (SHGC) are key independent variables in the model. These parameters reflect the thermal insulation capacity of the building envelope and its response to external solar energy, directly affecting the thermal environment and energy demand within the substation.

[0042] Step S204 , based on the energy consumption prediction model, a multi-objective optimization model is constructed using a genetic algorithm, wherein the multi-objective optimization model takes minimization of energy consumption, carbon emissions and costs as optimization objectives.

[0043] In this step, you can first select different types of building insulation materials and exterior window types, and count their corresponding thermal parameters, carbon emission factors in the production stage, and costs; use the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient, and exterior window solar heat gain coefficient as independent variables, and construct mathematical calculation models with full life cycle carbon emissions and building construction costs as dependent variables, respectively, and combine the energy consumption prediction model as the objective function of the multi-objective optimization problem; finally, you can call the genetic algorithm to establish a multi-objective optimization model of the substation building envelope structure based on the dynamic change of the transformer load rate, and use this model to obtain the optimal performance plan for the optimization design of the substation building envelope structure that meets the decision-making objectives, and output the optimization target design parameters of each envelope structure.

[0044] Step S205 : determining target design parameters of the enclosure structure based on the multi-objective optimization model, wherein the target design parameters include the type of insulation material, the thickness of the insulation layer, and the type of exterior windows.

[0045] In this step, the goal of the multi-objective optimization model is to find a design solution for the enclosure structure that can simultaneously minimize energy consumption, carbon emissions, and costs. This means that when designing the algorithm, these three objectives should be set as optimization targets, and through the iterative process of the genetic algorithm, solutions that achieve a balance between multiple objectives are found. The multi-objective optimization model can not only provide scientific enclosure structure design recommendations, but also ensure that the design solution achieves an optimal balance between energy consumption, carbon emissions, and costs, thereby achieving sustainable design of substation buildings. This method embodies the advanced concept of comprehensively considering environmental impacts and economic benefits during the building design stage, which will help promote energy conservation, emission reduction, and green transformation in the power industry.

[0046] Through the above steps, the purpose of scientifically determining the design parameters of the enclosure structure is achieved, thereby achieving the technical effect of improving the accuracy of the enclosure structure design, and further solving the technical problem of ignoring the dynamic changes in the heat dissipation of mechanical equipment in the traditional method.

[0047] As an optional embodiment, an internal disturbance prediction model is constructed based on multiple historical load rates and operating data, including: based on multiple historical load rates, using a neural network to predict the load rate of the transformer at multiple preset future moments; based on the load rates and operating data at multiple preset future moments, determining the heat dissipation of the transformer at multiple preset future moments; based on the heat dissipation at multiple preset future moments, constructing an internal disturbance prediction model.

[0048] Optionally, historical operating data of the load rate of the transformer between the main transformers of a substation building similar to the target substation can be collected as a sample set. For example, 80% of the sample set data can be used as a training set and 20% as a test set. The training set is used to train a target substation building main transformer load rate prediction model based on a CNN deep learning neural network, and the test set is used to test the transformer load rate prediction model. The above model can predict the load rate at multiple preset future moments. Then, combined with the mathematical formula for the heat dissipation of the transformer equipment and information such as the lighting power density during the operation phase of similar substation buildings obtained from the survey, an internal disturbance prediction model based on the dynamic change of the transformer load rate of the target substation building can be constructed. Table 1 is a schematic table of transformer parameters provided according to an optional embodiment of the present invention. As shown in Table 1, the building internal disturbance factors considered by the above model mainly include the loss of the main transformer between the main transformers considering the dynamic change of the transformer load rate during the building operation phase.

[0049] Table 1 Transformer parameter diagram

[0050]

[0051] As an optional embodiment, an energy consumption prediction model is constructed based on an internal disturbance prediction model and building data, including: constructing a physical model of the target substation based on the building data; constructing an energy consumption simulation model based on the physical model and the internal disturbance prediction model, wherein the energy consumption simulation model is used to simulate the operating status of the target substation; based on multiple values ​​corresponding to the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient, multiple value combinations of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient are determined; based on the energy consumption simulation model, the operating status of the target substation corresponding to the multiple value combinations is simulated respectively to obtain multiple air conditioning and ventilation total energy consumption data; based on the multiple value combinations and the corresponding air conditioning and ventilation total energy consumption data, an energy consumption prediction model is constructed through multivariate nonlinear regression fitting.

[0052] Optionally, the geometric structure information (i.e., building data) of the target substation building can be obtained through the construction drawings of the target substation building. Combined with the internal disturbance prediction model and external meteorological parameter information, an energy consumption simulation model of the target substation building based on the dynamic changes of the transformer load rate can be constructed; based on the target substation building energy consumption simulation model obtained in the above steps, the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient, and exterior window solar heat gain coefficient are used as independent variables, and the total energy consumption of air conditioning and ventilation of the building throughout the year is used as the dependent variable. Batch simulations are performed on different parameter combination conditions to obtain the total energy consumption results of air conditioning and ventilation of the building throughout the year under different working conditions, and a database of the impact of thermal performance optimization of the target substation building envelope structure on energy consumption is established; based on the above database, the exterior wall heat transfer coefficient can be constructed through the multivariate nonlinear regression fitting method. , roof heat transfer coefficient , heat transfer coefficient of external window The energy consumption prediction model of the target substation building based on the dynamic change of transformer load rate β is constructed with the solar heat gain coefficient SHGC of the exterior windows as the independent variable and the total energy consumption of air conditioning and ventilation E of the building throughout the year as the dependent variable. The corresponding mathematical prediction model is as follows:

[0053]

[0054] For example, Table 2 is an information table of room temperature control provided according to an optional embodiment of the present invention. As shown in Table 2, the physical model of the target substation can be established using Sketch Up and Open Studio software based on the collected geometric structure information of the target substation building and the room temperature control information. Figure 3 is a schematic diagram of a physical model of an indoor substation provided according to an optional embodiment of the present invention, such as Figure 3 As shown, the building specifications in the CAD drawings can be referenced to include information such as building area, shape, orientation, height, and the material and thickness of the building envelope to obtain building data and model the structure. Combining the internal disturbance prediction model obtained in the previous steps with external meteorological parameter information, Energy Plus software is used to create an energy consumption simulation model for the target substation building based on the dynamic changes in transformer load factor.

[0055] Table 2 Information table of room temperature control

[0056]

[0057] Then, the Energy Plus energy consumption simulation software and its auxiliary software jEPlus can be used in combination to perform batch simulations of multiple operating conditions based on the target substation building energy consumption simulation model in the above steps. Different value combinations of the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient, and exterior window solar heat gain coefficient can be sampled using the LHS sequence method. Using the aforementioned parameters as independent variables and the building's total annual air conditioning and ventilation energy consumption as the dependent variable, batch simulations are performed on different parameter combination conditions to obtain the building's total annual air conditioning and ventilation energy consumption results under different operating conditions, and to establish a database on the impact of the target substation building envelope structure thermal performance optimization on energy consumption.

[0058] Finally, based on the database obtained in the above steps, a multivariate nonlinear regression fitting method can be used to construct an energy consumption prediction model for the target substation building based on the dynamic changes in the transformer load rate β and the relationship between the total energy consumption of air conditioning and ventilation throughout the year and the optimized design elements of each enclosure structure. For the above energy consumption prediction model, the following evaluation indicators can be calculated: Determination coefficient ( ), which is used to measure the strength of the linear relationship between the model prediction value and the actual observation value, The closer the value is to 1, the better the model's prediction performance. The root mean square error (RMSE) measures the standard deviation between the predicted value and the actual observed value. A smaller RMSE indicates a higher prediction accuracy. The mean absolute error (MAE) measures the average absolute error between the predicted value and the actual observed value. A smaller MAE indicates a smaller prediction error. If all these evaluation indicators meet the preset conditions, that is, are less than a certain set value, the model's prediction performance is considered acceptable and can be used to predict the target building's total annual air conditioning and ventilation energy consumption.

[0059] As an optional embodiment, based on the energy consumption prediction model, a multi-objective optimization model is constructed using a genetic algorithm, including: obtaining the heat transfer coefficient, carbon emission factor and cost corresponding to each of multiple insulation material types; based on the heat transfer coefficient, carbon emission factor and cost corresponding to each of the multiple insulation material types, a carbon emission calculation model and a cost calculation model are constructed, wherein the independent variables of the carbon emission calculation model and the cost calculation model are the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient, the dependent variable of the carbon emission calculation model is the carbon emissions of the target substation, and the dependent variable of the cost calculation model is the construction cost of the enclosure structure; based on the energy consumption prediction model, the carbon emission calculation model and the cost calculation model, a non-dominated sorting multi-objective genetic algorithm is used to construct a multi-objective optimization model.

[0060] Optionally, first collect performance parameters for different types of insulation materials (such as EPS boards, rock wool boards, and rigid polyurethane foam boards). Table 3 is a schematic table of parameters for different types of building insulation materials, provided according to an optional embodiment of the present invention. As shown in Table 3, these parameters may include heat transfer coefficients, carbon emission factors, and costs. This data is typically obtained from material manufacturer specifications, relevant literature, or experimental testing.

[0061] Table 3 Parameters of different types of building insulation materials

[0062]

[0063] By varying the type of building envelope insulation material, insulation layer thickness, and exterior window type, the heat transfer coefficients of the exterior walls, roof, and exterior windows, as well as the solar heat gain coefficient of the exterior windows, can be altered. Using these four parameters as independent variables, mathematical calculation models were constructed with lifecycle carbon emissions and building construction costs as dependent variables. Finally, the energy consumption prediction model, carbon emission calculation model, and cost calculation model were integrated to establish a multi-objective optimization model using the non-dominated sorting multi-objective genetic algorithm (NSGA-II).

[0064] As an optional embodiment, based on a multi-objective optimization model, the target design parameters of the envelope structure are determined, including: calculating a solution set of the multi-objective optimization model, wherein the solution set includes multiple solutions, and one solution represents a set of values ​​of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient, and the exterior window solar heat gain coefficient; based on the ideal point method, determining the target solution in the solution set; and determining the target design parameters based on the target solution.

[0065] Alternatively, a multi-objective genetic algorithm (NSGA-II) can be used to simultaneously minimize multiple objectives, including the substation building's annual total energy consumption for air conditioning and ventilation, its lifecycle carbon emissions, and its initial construction cost. This multi-objective optimization design model for the substation building envelope, which considers the dynamics of transformer load factors, can be developed on a Python platform. The model's key functions include predicting objective variable values, controlling the range of optimization variable values, filtering outliers in the predicted objective variable values, and setting genetic algorithm control parameters. This ultimately yields a Pareto solution set for the multi-objective optimization problem. It's important to note that, unlike single-objective optimization problems, the results of multi-objective optimization typically contain several Pareto solutions, each of which is optimal for the overall objective function. Therefore, a decision-making method is often required to screen these solutions, ultimately determining a final solution based on the decision maker's preferences and presenting it as the optimal solution.

[0066] As an optional embodiment, based on the ideal point method, the target solution in the solution set is determined, including: based on the single-objective genetic algorithm, respectively determining the optimal solutions corresponding to multiple single-objective optimization models in the multi-objective optimization model; combining the optimal solutions corresponding to multiple single-objective optimization models in the multi-objective optimization model to determine the corresponding ideal points in a preset multi-dimensional space; determining multiple points corresponding to multiple solutions in the solution set in the preset multi-dimensional space; respectively calculating the Euclidean distances from the multiple points to the ideal point; determining the point with the smallest Euclidean distance to the ideal point among the multiple points as the target point; and determining the solution corresponding to the target point as the target solution.

[0067] Optionally, in order to determine the target solution, that is, the optimal solution, the optimal solution can be determined for each single objective function, that is, the single objective optimal solution corresponding to the minimum building energy consumption, the minimum carbon emissions, and the minimum initial cost can be determined respectively, and these single objective optimal solutions can be combined to form an ideal point; then the Euclidean distances from the points corresponding to each solution in the Pareto solution set obtained above to the ideal point are calculated respectively, and the point closest to the ideal point is determined as the target point, and the Pareto solution corresponding to the target point is determined as the target solution, that is, the optimal solution; finally, the enclosure structure optimization scheme of the substation building considering the dynamic changes of the transformer load rate is determined according to the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient and exterior window solar heat gain coefficient corresponding to the target solution, and the optimization design parameters of each enclosure structure are output.

[0068] As an optional embodiment, a multi-objective optimization design method for substation enclosure structures considering the dynamic change of transformer load rate is also provided. Figure 4 is a flow chart of a multi-objective optimization method for a substation building envelope structure considering dynamic changes in transformer load rate according to an optional embodiment of the present invention. Figure 4 As shown in this paper, by combining the impact of outdoor environmental changes, building envelope type, and dynamic changes in transformer operating conditions on building loads, and targeting substation building energy consumption, carbon emissions, and economic efficiency, a multi-objective optimization design model for substation building envelopes based on the dynamic changes in transformer load factor is constructed. This model further guides the selection of substation building envelopes. This method comprehensively considers the combined impact of dynamic changes in transformer load factor, outdoor environmental parameters, and the thermal performance of the building envelope on building loads. By scientifically constructing a substation building energy consumption prediction model, the accuracy of building envelope design is improved, effectively addressing the issue of traditional methods that ignore the dynamic changes in mechanical equipment heat dissipation. The analysis results of this multi-objective optimization design model enable comprehensive selection of substation building envelopes, reducing building operating energy consumption, carbon emissions, and construction costs. This promotes the low-carbon development of substation buildings and improves their sustainable development and economic efficiency.

[0069] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0070] Through the above description of the embodiments, those skilled in the art will clearly understand that the method for determining the substation enclosure structure according to the above embodiments can be implemented using software and a necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is the more preferred implementation method. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0071] According to an embodiment of the present invention, a device for implementing the above method is also provided. Figure 5 FIG. 1 is a structural block diagram of a device for determining a substation enclosure structure according to an embodiment of the present invention. Figure 5 As shown, the device includes: an acquisition module 51, a first construction module 52, a second construction module 53, a third construction module 54 and a determination module 55. The device is described below.

[0072] The acquisition module 51 is used to obtain multiple historical load rates of the transformers in the target substation, the operating data of the target substation, and the building data of the target substation, wherein the operating data includes personnel density, lighting power, and historical equipment operating data, and the building data includes geometric structure data, temperature data, and temperature control method.

[0073] The first construction module 52 is connected to the acquisition module 51 and is used to construct an internal disturbance prediction model based on multiple historical load rates and operation data, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer in a preset future time period.

[0074] The second construction module 53 is connected to the first construction module 52 and is used to construct an energy consumption prediction model based on the internal disturbance prediction model and building data, wherein the independent variables of the energy consumption prediction model include the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient and exterior window solar heat gain coefficient of the enclosure structure of the target substation, and the dependent variable is the total energy consumption of air conditioning and ventilation of the target substation.

[0075] The third construction module 54 is connected to the second construction module 53 and is used to construct a multi-objective optimization model based on the energy consumption prediction model using a genetic algorithm, wherein the multi-objective optimization model takes minimization of energy consumption, carbon emissions and costs as optimization objectives.

[0076] The determination module 55 is connected to the third construction module 54 and is used to determine the target design parameters of the enclosure structure based on the multi-objective optimization model, wherein the target design parameters include the type of insulation material, the thickness of the insulation layer and the type of the exterior window.

[0077] It should be noted that the acquisition module 51, first construction module 52, second construction module 53, third construction module 54, and determination module 55 correspond to steps S201 to S205 in the embodiment. The examples and application scenarios implemented by these modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in the embodiment.

[0078] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0079] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for determining the substation enclosure structure in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method for determining the substation enclosure structure. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0080] The processor can call information and applications stored in the memory through the transmission device to perform the following steps: obtaining multiple historical load rates of the transformer in the target substation, operating data of the target substation, and building data of the target substation, wherein the operating data includes personnel density, lighting power, and historical equipment operating data, and the building data includes geometric structure data, temperature data, and temperature control method; constructing an internal disturbance prediction model based on the multiple historical load rates and operating data, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer in a preset future time period; constructing an energy consumption prediction model based on the internal disturbance prediction model and the building data, wherein the independent variables of the energy consumption prediction model include the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient, and exterior window solar heat gain coefficient of the enclosure structure of the target substation, and the dependent variable is the total energy consumption of air conditioning and ventilation of the target substation; constructing a multi-objective optimization model based on the energy consumption prediction model using a genetic algorithm, wherein the multi-objective optimization model takes minimizing energy consumption, carbon emissions, and cost as optimization objectives; and determining target design parameters of the enclosure structure based on the multi-objective optimization model, wherein the target design parameters include the type of insulation material, the thickness of the insulation layer, and the type of exterior windows.

[0081] Optionally, the processor may also execute the program code of the following steps: constructing an internal disturbance prediction model based on multiple historical load rates and operating data, including: predicting the load rate of the transformer at multiple preset future moments using a neural network based on multiple historical load rates; determining the heat dissipation of the transformer at multiple preset future moments based on the load rates and operating data at multiple preset future moments; and constructing an internal disturbance prediction model based on the heat dissipation at multiple preset future moments.

[0082] Optionally, the processor may also execute the program code of the following steps: constructing an energy consumption prediction model based on the internal disturbance prediction model and building data, including: constructing a physical model of the target substation based on the building data; constructing an energy consumption simulation model based on the physical model and the internal disturbance prediction model, wherein the energy consumption simulation model is used to simulate the operating status of the target substation; determining multiple value combinations of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient based on multiple values ​​corresponding to each of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient; simulating the operating status of the target substation corresponding to multiple value combinations based on the energy consumption simulation model to obtain multiple air conditioning and ventilation total energy consumption data; constructing an energy consumption prediction model based on multiple value combinations and the corresponding air conditioning and ventilation total energy consumption data through multivariate nonlinear regression fitting.

[0083] Optionally, the processor may also execute the program code of the following steps: based on the energy consumption prediction model, a multi-objective optimization model is constructed using a genetic algorithm, including: obtaining the heat transfer coefficient, carbon emission factor and cost corresponding to each of multiple insulation material types; based on the heat transfer coefficient, carbon emission factor and cost corresponding to each of multiple insulation material types, a carbon emission calculation model and a cost calculation model are constructed, wherein the independent variables of the carbon emission calculation model and the cost calculation model are the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient, the dependent variable of the carbon emission calculation model is the carbon emissions of the target substation, and the dependent variable of the cost calculation model is the construction cost of the enclosure structure; based on the energy consumption prediction model, the carbon emission calculation model and the cost calculation model, a multi-objective optimization model is constructed using a non-dominated sorting multi-objective genetic algorithm.

[0084] Optionally, the processor may also execute the program code of the following steps: determining the target design parameters of the enclosing structure based on a multi-objective optimization model, including: calculating the solution set of the multi-objective optimization model, wherein the solution set includes multiple solutions, and one solution represents a set of values ​​of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient, and the exterior window solar heat gain coefficient; determining the target solution in the solution set based on the ideal point method; and determining the target design parameters based on the target solution.

[0085] Optionally, the processor may also execute the program code of the following steps: determining the target solution in the solution set based on the ideal point method, including: determining the optimal solutions corresponding to each of the multiple single-objective optimization models in the multi-objective optimization model based on the single-objective genetic algorithm; combining the optimal solutions corresponding to each of the multiple single-objective optimization models in the multi-objective optimization model to determine the corresponding ideal points in the preset multi-dimensional space; determining the multiple points corresponding to each of the multiple solutions in the solution set in the preset multi-dimensional space; calculating the Euclidean distances from the multiple points to the ideal point; determining the point with the smallest Euclidean distance to the ideal point among the multiple points as the target point; and determining the solution corresponding to the target point as the target solution.

[0086] According to an embodiment of the present invention, a method for determining the enclosure structure of a substation is provided. By obtaining multiple historical load rates of the transformer in the target substation, the operating data of the target substation and the building data of the target substation, wherein the operating data includes personnel density, lighting power and historical equipment operating data, and the building data includes geometric structure data, temperature data and temperature control mode; based on multiple historical load rates and operating data, an internal disturbance prediction model is constructed, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer in a preset future period; based on the internal disturbance prediction model and the building data, an energy consumption prediction model is constructed, wherein the independent variables of the energy consumption prediction model include the heat transfer coefficient of the exterior wall, the heat transfer coefficient of the roof, the exterior The window heat transfer coefficient and the external window solar heat gain coefficient, and the dependent variable is the total energy consumption of air conditioning and ventilation of the target substation; based on the energy consumption prediction model, a multi-objective optimization model is constructed using a genetic algorithm, wherein the multi-objective optimization model takes minimizing energy consumption, carbon emissions and costs as optimization objectives; based on the multi-objective optimization model, the target design parameters of the envelope structure are determined, wherein the target design parameters include the type of insulation material, the thickness of the insulation layer and the type of external windows, thereby achieving the purpose of scientifically determining the design parameters of the envelope structure, thereby achieving the technical effect of improving the accuracy of the envelope structure design, and thus solving the technical problem of ignoring the dynamic changes of heat dissipation of mechanical equipment in the traditional method.

[0087] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0088] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the method for determining the substation enclosure structure provided in the above embodiment.

[0089] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0090] Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for executing the following steps: obtaining multiple historical load rates of the transformer in the target substation, operating data of the target substation, and building data of the target substation, wherein the operating data includes personnel density, lighting power, and historical equipment operating data, and the building data includes geometric structure data, temperature data, and temperature control mode; constructing an internal disturbance prediction model based on multiple historical load rates and operating data, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer in a preset future time period; and constructing an internal disturbance prediction model based on the internal disturbance prediction model and the established internal disturbance prediction model. Based on the construction data, an energy consumption prediction model is constructed, where the independent variables of the energy consumption prediction model include the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient and exterior window solar heat gain coefficient of the target substation's enclosure structure, and the dependent variable is the total energy consumption of air conditioning and ventilation of the target substation; based on the energy consumption prediction model, a multi-objective optimization model is constructed using a genetic algorithm, where the multi-objective optimization model takes minimizing energy consumption, carbon emissions and costs as optimization objectives; based on the multi-objective optimization model, the target design parameters of the enclosure structure are determined, where the target design parameters include the type of insulation material, the thickness of the insulation layer and the type of exterior windows.

[0091] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: constructing an internal interference prediction model based on multiple historical load rates and operating data, including: predicting the load rate of the transformer at multiple preset future moments using a neural network based on multiple historical load rates; determining the heat dissipation of the transformer at multiple preset future moments based on the load rates and operating data at multiple preset future moments; and constructing an internal interference prediction model based on the heat dissipation at multiple preset future moments.

[0092] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: constructing an energy consumption prediction model based on the internal disturbance prediction model and building data, including: constructing a physical model of the target substation based on the building data; constructing an energy consumption simulation model based on the physical model and the internal disturbance prediction model, wherein the energy consumption simulation model is used to simulate the operating state of the target substation; determining multiple value combinations of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient based on multiple values ​​corresponding to each of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient; simulating the operating states of the target substation corresponding to multiple value combinations based on the energy consumption simulation model to obtain multiple air conditioning and ventilation total energy consumption data; constructing an energy consumption prediction model based on multiple value combinations and the corresponding air conditioning and ventilation total energy consumption data through multivariate nonlinear regression fitting.

[0093] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: based on the energy consumption prediction model, a multi-objective optimization model is constructed using a genetic algorithm, including: obtaining the heat transfer coefficient, carbon emission factor and cost corresponding to each of multiple insulation material types; based on the heat transfer coefficient, carbon emission factor and cost corresponding to each of multiple insulation material types, a carbon emission calculation model and a cost calculation model are constructed, wherein the independent variables of the carbon emission calculation model and the cost calculation model are the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient, the dependent variable of the carbon emission calculation model is the carbon emissions of the target substation, and the dependent variable of the cost calculation model is the construction cost of the enclosure structure; based on the energy consumption prediction model, the carbon emission calculation model and the cost calculation model, a multi-objective optimization model is constructed using a non-dominated sorting multi-objective genetic algorithm.

[0094] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: determining target design parameters of the enclosing structure based on a multi-objective optimization model, including: calculating a solution set of the multi-objective optimization model, wherein the solution set includes multiple solutions, one solution representing a set of values ​​of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient, and the exterior window solar heat gain coefficient; determining a target solution in the solution set based on the ideal point method; and determining target design parameters based on the target solution.

[0095] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: determining a target solution in a solution set based on an ideal point method, including: determining the optimal solutions corresponding to each of multiple single-objective optimization models in a multi-objective optimization model based on a single-objective genetic algorithm; combining the optimal solutions corresponding to each of multiple single-objective optimization models in the multi-objective optimization model to determine the corresponding ideal points in a preset multi-dimensional space; determining multiple points corresponding to each of multiple solutions in the solution set in a preset multi-dimensional space; calculating the Euclidean distances from the multiple points to the ideal point; determining the point with the smallest Euclidean distance to the ideal point among the multiple points as the target point; and determining the solution corresponding to the target point as the target solution.

[0096] An embodiment of the present invention further provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can achieve the following: obtaining multiple historical load rates of transformers in a target substation, operating data of the target substation, and building data of the target substation, wherein the operating data includes personnel density, lighting power, and historical equipment operating data, and the building data includes geometric structure data, temperature data, and temperature control mode; constructing an internal disturbance prediction model based on multiple historical load rates and operating data, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer in a preset future time period; based on Based on the internal disturbance prediction model and building data, an energy consumption prediction model is constructed, wherein the independent variables of the energy consumption prediction model include the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient and exterior window solar heat gain coefficient of the envelope structure of the target substation, and the dependent variable is the total energy consumption of air conditioning and ventilation of the target substation; based on the energy consumption prediction model, a multi-objective optimization model is constructed using a genetic algorithm, wherein the multi-objective optimization model takes minimizing energy consumption, carbon emissions and costs as optimization objectives; based on the multi-objective optimization model, the target design parameters of the envelope structure are determined, wherein the target design parameters include the type of insulation material, the thickness of the insulation layer and the type of exterior windows.

[0097] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0098] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0100] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0101] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part 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 for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), a mobile hard drive, a magnetic disk, or an optical disk.

[0103] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for determining a substation enclosure structure, characterized in that: include: Acquiring multiple historical load rates of transformers in a target substation, operating data of the target substation, and building data of the target substation, wherein the operating data includes personnel density, lighting power, and historical equipment operating data, and the building data includes geometric structure data, temperature data, and temperature control mode; Based on the multiple historical load rates and the operating data, constructing an internal disturbance prediction model, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer in a preset future time period; Based on the internal disturbance prediction model and the building data, an energy consumption prediction model is constructed, wherein the independent variables of the energy consumption prediction model include the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient, and exterior window solar heat gain coefficient of the enclosure structure of the target substation, and the dependent variable is the total energy consumption of air conditioning and ventilation of the target substation; Based on the energy consumption prediction model, a multi-objective optimization model is constructed using a genetic algorithm, wherein the multi-objective optimization model takes minimization of energy consumption, carbon emissions and costs as optimization objectives; Determining target design parameters of the enclosure structure based on the multi-objective optimization model, wherein the target design parameters include insulation material type, insulation layer thickness, and exterior window type; Among them, the energy consumption prediction model is constructed based on the internal disturbance prediction model and the building data, including: constructing a physical model of the target substation based on the building data; constructing an energy consumption simulation model based on the physical model and the internal disturbance prediction model, wherein the energy consumption simulation model is used to simulate the operating status of the target substation; based on the multiple values ​​corresponding to the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient, multiple value combinations of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient are determined; based on the energy consumption simulation model, the operating status of the target substation corresponding to the multiple value combinations is simulated respectively to obtain multiple air conditioning and ventilation total energy consumption data; based on the multiple value combinations and the corresponding air conditioning and ventilation total energy consumption data, the energy consumption prediction model is constructed through multivariate nonlinear regression fitting.

2. The method according to claim 1, characterized in that The constructing of an internal interference prediction model based on the multiple historical load rates and the operating data includes: Based on the multiple historical load rates, using a neural network to predict the load rate of the transformer at multiple preset future moments; determining the heat dissipation of the transformer at the plurality of preset future moments based on the load rates at the plurality of preset future moments and the operating data; The internal disturbance prediction model is constructed based on the heat dissipation at the multiple preset future moments.

3. The method according to claim 1, characterized in that The multi-objective optimization model is constructed based on the energy consumption prediction model using a genetic algorithm, including: Obtain the heat transfer coefficient, carbon emission factor, and cost for multiple insulation material types; Based on the heat transfer coefficients, carbon emission factors, and costs corresponding to the multiple insulation material types, a carbon emission calculation model and a cost calculation model are constructed, wherein the independent variables of the carbon emission calculation model and the cost calculation model are the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient, and the exterior window solar heat gain coefficient; the dependent variable of the carbon emission calculation model is the carbon emissions of the target substation; and the dependent variable of the cost calculation model is the construction cost of the enclosure structure; Based on the energy consumption prediction model, the carbon emission calculation model and the cost calculation model, the multi-objective optimization model is constructed using a non-dominated sorting multi-objective genetic algorithm.

4. The method according to any one of claims 1 to 3, characterized in that Determining target design parameters of the enclosure structure based on the multi-objective optimization model includes: Calculating a solution set of the multi-objective optimization model, wherein the solution set includes a plurality of solutions, and one solution represents a set of values ​​of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient, and the exterior window solar heat gain coefficient; Determining a target solution in the solution set based on an ideal point method; Based on the target solution, the target design parameters are determined.

5. The method according to claim 4, characterized in that Determining the target solution in the solution set based on the ideal point method includes: Based on a single-objective genetic algorithm, respectively determining the optimal solutions corresponding to the multiple single-objective optimization models in the multi-objective optimization model; Combining the optimal solutions corresponding to the multiple single-objective optimization models in the multi-objective optimization model to determine the corresponding ideal point in a preset multidimensional space; Determine a plurality of points in the preset multidimensional space corresponding to each of the plurality of solutions in the solution set; Calculating the Euclidean distances of the plurality of points to the ideal point respectively; Determine a point with the smallest Euclidean distance to the ideal point among the multiple points as a target point; The solution corresponding to the target point is determined as the target solution.

6. A device for determining a substation enclosure structure, characterized in that: include: an acquisition module, configured to acquire multiple historical load rates of transformers in a target substation, operating data of the target substation, and building data of the target substation, wherein the operating data includes personnel density, lighting power, and historical equipment operating data, and the building data includes geometric structure data, temperature data, and temperature control mode; A first building module is configured to build an internal disturbance prediction model based on the multiple historical load rates and the operating data, wherein the internal disturbance prediction model is used to predict the heat dissipation of the transformer in a preset future time period; a second construction module, configured to construct an energy consumption prediction model based on the internal disturbance prediction model and the building data, wherein the independent variables of the energy consumption prediction model include the exterior wall heat transfer coefficient, roof heat transfer coefficient, exterior window heat transfer coefficient, and exterior window solar heat gain coefficient of the enclosure structure of the target substation, and the dependent variable is the total energy consumption of air conditioning and ventilation of the target substation; A third construction module is configured to construct a multi-objective optimization model using a genetic algorithm based on the energy consumption prediction model, wherein the multi-objective optimization model takes minimization of energy consumption, carbon emissions, and costs as optimization objectives; a determination module, configured to determine target design parameters of the enclosure structure based on the multi-objective optimization model, wherein the target design parameters include insulation material type, insulation layer thickness, and exterior window type; Among them, the second construction module is also used to construct a physical model of the target substation based on the building data; construct an energy consumption simulation model based on the physical model and the internal disturbance prediction model, wherein the energy consumption simulation model is used to simulate the operating status of the target substation; based on the multiple values ​​corresponding to the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient, determine multiple value combinations of the exterior wall heat transfer coefficient, the roof heat transfer coefficient, the exterior window heat transfer coefficient and the exterior window solar heat gain coefficient; based on the energy consumption simulation model, simulate the operating status of the target substation corresponding to the multiple value combinations respectively to obtain multiple air conditioning and ventilation total energy consumption data; based on the multiple value combinations and the corresponding air conditioning and ventilation total energy consumption data, construct the energy consumption prediction model through multivariate nonlinear regression fitting.

7. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the method for determining the substation enclosure structure according to any one of claims 1 to 5.

8. A computer device, characterized in that: include: memory and processor, The memory stores a computer program; The processor is configured to execute a computer program stored in the memory, and when the computer program is run, the processor is enabled to execute the method for determining a substation enclosure structure according to any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for determining the substation enclosure structure according to any one of claims 1 to 5 is implemented.

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