A Dynamic Algorithm for Building Skin Energy Density Distribution Based on Multi-Objective Optimization

Abstract

This invention discloses a dynamic distribution algorithm for building skin energy density based on multi-objective optimization, comprising the following steps: S1: Constructing a near-zero energy building model and obtaining the parameters of the building model; and establishing a multi-objective optimization problem for the building model; S2: Determining the photovoltaic panel parameter model, the annual average monthly irradiance time model of the city where the design site is located, and the target building energy consumption model, including the photovoltaic panel's power generation efficiency, peak power, cost per kilowatt-hour, size data, monthly irradiance time, and energy consumption data, and constructing a dynamic energy matrix for the building skin; S3: Using the NSGA-II algorithm to solve the multi-objective optimization problem, including setting the population size, maximum number of iterations, and crossover and mutation probabilities, and using a genetic algorithm to execute the optimization process to obtain the Pareto optimal solution set; S4: Using the maximum power generation solution and the minimum cost solution obtained by the NSGA-II algorithm, combining the photovoltaic panel parameter model, the annual average monthly irradiance time model, and the target building energy consumption model, to obtain the annual monthly energy consumption curve.

Description

Technical Field

[0001] This invention belongs to the field of building energy synergistic optimization technology, specifically involving a dynamic distribution algorithm for building skin energy density that integrates building information modeling (BIM), meteorological big data, and the characteristics of multiple energy devices. It is applicable to the integrated energy system design of near-zero energy buildings. Background Technology

[0002] Nearly zero-energy buildings (NZEB) represent an important development direction in the field of building energy conservation, aiming to significantly reduce building operating energy consumption through efficient energy-saving technologies and the use of renewable energy.

[0003] The core technologies of near-zero energy buildings mainly include ultra-efficient building envelopes, renewable energy integration, intelligent energy management, airtightness, and heat recovery. Currently, near-zero energy buildings face technical challenges such as initial investment being 10%-30% higher than ordinary buildings, difficulties in multi-system collaborative design, and insufficient regional adaptability.

[0004] Therefore, to address the technical challenge of building skins based on single systems being unable to coordinate with multiple systems in design, this paper proposes a dynamic energy density distribution algorithm for building skins that integrates Building Information Modeling (BIM), meteorological big data, and the characteristics of multiple energy devices. This algorithm is highly compatible with the development trend of green buildings and represents the future trend of urban and building energy development. Summary of the Invention

[0005] The technical problem this invention aims to solve is to provide a dynamic energy density distribution algorithm for building facades based on multi-objective optimization, addressing the current technical issue that building facades based on a single system cannot coordinate the design of multiple systems. This invention, based on multi-system collaborative design, considers the requirements of building facade photovoltaic installation, cost, and near-zero energy buildings, making the simulation algorithm fit the design conditions of actual engineering projects. This allows for the acquisition of design parameters for building photovoltaic installation schemes with minimum cost and optimal energy supply capacity.

[0006] The technical solution adopted by this invention to solve the above-mentioned technical problems is: a dynamic distribution algorithm for building skin energy density based on multi-objective optimization, comprising the following steps:

[0007] Step S1: Build a near-zero energy building model and obtain the geometric parameters of the building model; establish a multi-objective optimization problem for the model. The optimization objectives of the multi-objective optimization problem are photovoltaic power generation and photovoltaic installation cost. The variables to be optimized in the multi-objective optimization problem are the material selection and design parameters of photovoltaic on the building facade and roof. The constraints of the multi-objective optimization problem are the design range of the variables to be optimized, including the area size of each skin of the building in different orientations and the upper limit of the near-zero energy skin installation cost. Based on this, establish a four-dimensional parametric skin model.

[0008] ;

[0009] in: This refers to the number of photovoltaic panels on each of the building's facades. This refers to the number of photovoltaic panels in the width direction; This refers to the number of photovoltaic panels in the height direction; The width of each skin of the building; The height of each skin of the building; Width of the photovoltaic panel; The height of the photovoltaic panel; This refers to the installation gap between the two photovoltaic panels.

[0010] Step S2: Determine the initial selection of various photovoltaic panel parameter models, the annual monthly average irradiance time model of the city where the design site is located, and the target building energy consumption model, including the photovoltaic panel's power generation efficiency, peak power, cost per kilowatt-hour, size data, monthly irradiance time, energy consumption data, etc., and construct the building skin energy dynamic matrix;

[0011] Step S3: Use the NSGA-II algorithm to solve the multi-objective optimization problem, including at least the population size, maximum number of iterations, and crossover and mutation probability. Use a genetic algorithm to perform the optimization process and obtain the Pareto optimal solution set.

[0012] Step S4: Using the maximum power generation solution and minimum cost solution obtained from the NSGA-II algorithm, and combining the photovoltaic panel parameter model, the annual average monthly irradiance time model, and the target building energy consumption model, the monthly energy consumption curve for the whole year is obtained. If the photovoltaic power generation in a given month does not meet the building energy consumption for that month, the monthly energy consumption curve for the whole year can provide a design basis for the subsequent configuration of the energy storage system.

[0013] Furthermore, in step S2, the construction function of the building skin energy dynamic matrix is ​​expressed as:

[0014] ;

[0015] in: Performance indicators for the target location (spatial coordinates) (such as energy consumption, light environment, thermal comfort, etc.); For the first k Weighting coefficients for each data source; Indicates the first k Data sources at discrete grid locations The data values ​​need to be mapped to spatial points. .

[0016] Furthermore, in step S3, the NSGA-II algorithm in the gamultiobj function of MATLAB is used to solve the multi-objective optimization problem and obtain the Pareto optimal solution. The stochastic optimization is reflected in the multi-process simulation N times for the area of ​​each photovoltaic power generation form with different orientations on the building skin during the solution process, and the power generation value of N photovoltaic power generation forms and the cost value of N building skin photovoltaic installation forms are calculated. The magnitude of the power generation value and the cost value are used as the indicators for the NSGA-II algorithm to judge the quality of individual photovoltaic installations. The value of N is greater than or equal to 30, and in this embodiment, the value of N is 100.

[0017] Furthermore, in step S3, the minimum laying cost and the maximum power generation are used as the optimization objectives of NSGA-II, and the objective function is expressed as follows: ;

[0018] in, The area of ​​the building's skin in all directions; The power generation of photovoltaic systems on the building's facade in all directions; The cost per kilowatt-hour for different types of photovoltaic panel materials, The power generation efficiency of different types of photovoltaic panels. The target building's total annual energy consumption; all parameters above are derived from the building skin energy dynamic matrix. Computational extension;

[0019] Furthermore, in step S1, the near-zero energy building model is built using Autodesk Revit software, and Autodesk Revit software, combined with the DYNAMO plugin, outputs the building geometric model parameters required by MATLAB to construct the thinking.

[0020] In step S2, the photovoltaic panel parameter model can be input by the user in the MATLAB software or called by the commonly used photovoltaic panel parameter model contained in the program and stored in the MATLAB matrix structure;

[0021] In step S2, the city's annual monthly average radiation time model is established by reading the TMY3 meteorological data file using MATLAB software, or by the user inputting it into MATLAB software, or by calling the typical city's annual monthly average radiation time model contained in the program, and storing it in the MATLAB matrix structure.

[0022] In step S2, the target energy consumption model can be input by the user in the MATLAB software or called by the typical building energy consumption model contained in the program and stored in the MATLAB matrix structure;

[0023] In step S4, the monthly energy consumption curve for the whole year is calculated, with the aim of visualizing the energy allocation and storage relationship and solving the energy storage problem;

[0024] The technical innovations of this invention are as follows:

[0025] 1) Modular treatment of building skin, compatible with common photovoltaic modules on the market; building geometry-driven area calculation model to solve the problem of adapting photovoltaic configuration to building geometry; facade-roof zoning material selection strategy, which conforms to actual engineering operation;

[0026] 2) NSGA-II Multi-Objective Optimization: Simultaneously optimizes energy, facade design, and cost, providing a Pareto frontier for quantitative decision support;

[0027] 3) Three-dimensional visualization output enables visual verification of the solution and aids in understanding the results.

[0028] The beneficial effects of this invention are as follows: Based on multi-system collaborative design, this invention takes into account the requirements of building skin photovoltaic installation, cost and near-zero energy consumption buildings, so that the simulation algorithm fits the design of actual engineering projects, thereby outputting the design parameters of building photovoltaic installation scheme with optimal cost and optimal production capacity, and can also visualize and generate monthly energy consumption curves throughout the year, providing a design basis for the subsequent configuration of electric energy storage systems. Attached Figure Description

[0029] Figure 1 This is a diagram of the overall architecture of the algorithm of this invention;

[0030] Figure 2 This is a schematic diagram of a building photovoltaic installation scheme according to an embodiment of the present invention;

[0031] Figure 3 This is a Pareto front plot for photovoltaic design optimization in this invention;

[0032] Figure 4 This is a graph comparing monthly energy consumption and monthly photovoltaic power generation under the minimum cost solution of this invention. Detailed Implementation

[0033] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0034] like Figure 1 As shown, this invention provides a dynamic distribution algorithm for building skin energy density based on multi-objective optimization, the specific steps of which are as follows;

[0035] The following example demonstrates a near-zero energy office building in Hangzhou, Zhejiang Province. Figure 2 As shown:

[0036] Export the REVIT of a near-zero energy office building as a point cloud file using the DYNAMO plugin, or convert it into a simple geometric shape and save it as a MATLAB-readable file.

[0037] 1. Read data

[0038] BIM model: Roof and curtain wall, building area 3000m³

[0039] Meteorological data: Hangzhou TMY3 file

[0040] Equipment parameters: Cadmium telluride thin-film photovoltaic louvers, type 1 (40% transmittance, η=9%), size 600mm×1200mm, unit price 13.3 yuan / W; type 2 (30% transmittance, η=12%), size 600mm×1200mm, unit price 15 yuan / W; type 3 (10% transmittance, η=15%), size 600mm×1200mm, unit price 17 yuan / W; monocrystalline silicon rooftop photovoltaic, roof area 580m², size 2465mm×1134mm, unit price 4 yuan / W;

[0041] 2. Establish a four-dimensional parametric skin model and construct a dynamic influence matrix.

[0042] ;

[0043] in: Performance indicators for the target location (spatial coordinates) (such as energy consumption, light environment, thermal comfort, etc.); For the first k Weight coefficients for each data source; Indicates the first k Data sources at discrete grid locations The data values ​​need to be mapped to spatial points. .

[0044] Specifically, in this implementation plan, the BIM geometric topology data read from REVIT is: The material photoelectric property matrix read is Read the monthly sunshine duration and horizontal solar radiation intensity from TMY3 and store them as... Obtain the real-time state matrix of equipment from the given building functions ;

[0045] 3. Use the NSGA-II algorithm to solve a multi-objective optimization problem, including at least the population size, maximum number of iterations, and crossover / mutation probability. Utilize a genetic algorithm to execute the optimization process and obtain the Pareto optimal solution set, as shown below. Figure 3 As shown; specifically in this implementation scheme, the population size is set to 80, the maximum number of iterations is 100, and the crossover mutation probability is 0.8;

[0046] Minimize installation cost and maximize power generation as the optimization objectives of NSGA-II. The objective function is expressed as follows:

[0047] ;

[0048] in, The area of ​​the building's skin in all directions; The power generation of photovoltaic systems on the building's facade in all directions; The cost per kilowatt-hour for different types of photovoltaic panel materials, The power generation efficiency of different types of photovoltaic panels. The target building's total annual energy consumption; all parameters above are derived from the building skin energy dynamic matrix. Computational extension; specifically, in this implementation scheme, the skin area to be solved is... Building skin area in all directions for , , The cost per kilowatt-hour of cadmium telluride thin-film photovoltaic louvers type 1 is... , Type 2 is , Type 3 is , The cost per kilowatt-hour of monocrystalline silicon photovoltaic power is , ; for Isotropic sum, numerically ;

[0049] To determine whether this scheme meets the requirements for near-zero energy buildings, when the total power generation of all types of photovoltaic power is ≥ When the energy consumption is 0.7 times that of near-zero energy buildings (as defined in GB / T 51350-2019 "Technical Standard for Near-Zero Energy Buildings"), the requirements for near-zero energy buildings are met.

[0050] 4. The maximum power generation solution and the minimum cost solution obtained through the NSGA-II algorithm; The minimum cost solution (total lifecycle cost of 1285 yuan / m²) has a total lifecycle energy consumption of 76.34 kWh / (m²·a): facade material selection type 2, roof material selection type 1; the photovoltaic panel areas on each side are as follows: East: 0.0m², South: 0.0m², West: 0.0m², North: 0.0m², Roof: 534.5m²; The maximum power generation solution (total lifecycle power generation of 29.87 kWh / (m²·a)) has a total lifecycle cost of 1442.00 yuan / m²: facade material selection type 2, roof material selection type 1; the photovoltaic panel areas on each side are as follows: East: 175.5m², South: 221.0m², West: 215.2m², North: 149.4m². Roof area: 599.9 m², if Figure 4 As shown, by combining the photovoltaic panel parameter model, the annual average monthly irradiance time model, and the target energy consumption model, the monthly energy consumption curves for the whole year are obtained.

[0051] If the monthly photovoltaic power generation is insufficient to meet the building's energy consumption for that month, the monthly energy consumption curve for the whole year can provide a design basis for the subsequent configuration of the energy storage system. Specifically, in this implementation case, the photovoltaic power generation under the minimum cost solution cannot meet the energy consumption needs of the near-zero energy building in February and June. The shortfall in February is about 1819 kWh, and the shortfall in June is about 1234.4 kWh. Energy storage equipment is needed to supplement the energy supply, and energy allocation is required in other surplus months. The scheme is selected by comparing the maximum power generation solution and the minimum cost solution.

[0052] Compared with traditional manual calculation methods in the same scenario, the traditional algorithm uses monocrystalline silicon photovoltaic panels on the roof and copper indium gallium selenide (CIGS) on the facade. Under the same cost, the DC power generation is 154004 kWh / a, while the DC power generation of this invention is 160950.99 kWh / a, which is an increase of nearly 4.5%. The traditional algorithm takes about 2-3 days, while the algorithm of this invention takes about 0.5 hours, which greatly improves the efficiency of providing decision-making solutions.

[0053] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the technical solutions of the present invention. Any technical solution that can be implemented based on the above embodiments without creative effort should be considered to fall within the scope of protection of the patent of the present invention.

Claims

1. A method for dynamic distribution of building skin energy density based on multi-objective optimization, characterized in that, Comprise the following steps: S1: build a near zero energy consumption building model, get the geometric parameters of the building model; and establish a multi-objective optimization problem of the building model; The optimization objective of the multi-objective optimization problem in the S1 includes photovoltaic power generation and photovoltaic laying cost; the variable optimized by the multi-objective optimization problem includes the material selection and design parameter of building facade roof photovoltaic; the constraint condition of the multi-objective optimization problem is the design range of the variable to be optimized, including the area size of different skin of building and the upper limit of skin laying cost of near zero energy consumption building geometric model, and a four-dimensional parameterized skin model is established; ； wherein: is the number of photovoltaic panels for each skin of the building; is the number of photovoltaic panels in the width direction; is the number of photovoltaic panels in the height direction; is the width of each skin of the building; is the height of each skin of the building; is the width of the photovoltaic panel; is the height of the photovoltaic panel; is the installation gap between two photovoltaic panels; S2: determine the photovoltaic panel parameter model, the annual monthly average irradiation time model of the city where the design site is located and the target building energy consumption model, including the power generation efficiency, peak power, degree of electricity cost, size data, monthly irradiation time, energy consumption data of photovoltaic panel, and construct the building skin energy dynamic matrix; The construction function of the building skin energy dynamic matrix in the S2 is represented as: ； Wherein: Performance indicators of the target position, including energy consumption, light environment, thermal comfort; The weight coefficient of the first k data source; The data value of the first k data source at the discrete grid position , which needs to be mapped to the spatial point ; S3: use NSGA-Ⅱ algorithm to solve the multi-objective optimization problem, including setting population size, maximum iteration number and crossover mutation probability, using genetic algorithm to execute the optimization process, and obtaining the Pareto optimal solution set; S4: through the maximum power generation solution and the minimum cost solution obtained by the NSGA-Ⅱ algorithm, comprehensively considering the photovoltaic panel parameter model, the annual monthly average irradiation time model and the target building energy consumption model, the annual monthly energy consumption curve is obtained.

2. A method for dynamic distribution of building skin energy density based on multi-objective optimization according to claim 1, characterized in that, In the S3, the genetic algorithm is used to execute the optimization process, each photovoltaic in different orientation skin arrangement form area is simulated for N times, and the power generation value of N photovoltaic power generation and the cost value of N building skin photovoltaic laying are calculated, and the size of the power generation value and the cost value is taken as the index for judging the degree of individual superiority and inferiority of NSGA-Ⅱ algorithm.

3. The method of claim 1, wherein, In the S3, the minimum laying cost and the maximum power generation are taken as the optimization objective of NSGA-Ⅱ, and the objective function is represented as ； wherein, is the building's each orientation skin area; is the building's each orientation skin photovoltaic power generation; is the different kinds of photovoltaic panel material's degree of electric cost, is the different kinds of photovoltaic panel power generation efficiency, is the target building's total annual energy consumption.

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