Dynamic photovoltaic sunshade shutter control method

Abstract

The invention belongs to the technical field of sunshade shutter control, and provides a dynamic photovoltaic sunshade shutter control method. Comprising the steps of photovoltaic shutter sunshade device construction, dynamic mathematical model construction, optimization target definition, original controller design, controller iterative optimization, control rule definition, weight strategy acquisition, shutter adjustment strategy generation and photovoltaic shutter sunshade device adjustment. According to the invention, through fusion of the model prediction control method and the rule-based control method, adaptive adjustment of the photovoltaic shutter sunshade device according to the optimization target and the custom rule is realized, and the flexibility and the universality of the device are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sunshade louver control, and in particular to a dynamic photovoltaic sunshade louver control method. Background Art

[0002] Currently, louver shading is widely used in various office, school, and residential buildings. However, due to the conflict between glare and the need to utilize natural light and maintain a good field of view, current manual louver shading and passive motorized louver shading systems struggle to balance these multiple objectives and optimize the indoor lighting environment. This results in a significant amount of unnecessary artificial lighting loss.

[0003] Although patent CN 110805391 A provides a smart photovoltaic power generation and environmentally friendly home blinds, the real-time tracking of light sources by the blinds is not suitable for situations where the temperature difference between indoors and outdoors is too large. At the same time, the technology does not use a machine learning algorithm, resulting in a poor operating strategy. In addition, the technology does not introduce a human feedback function, resulting in a poor user experience. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a dynamic photovoltaic sunshade blind control method, which solves the problems of single operation strategy and poor user experience of traditional blind devices.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A dynamic photovoltaic sunshade blind control method, comprising:

[0007] Construct photovoltaic louver shading devices;

[0008] Constructing a dynamic mathematical model based on the photovoltaic louver sunshade device;

[0009] defining an optimization objective of the dynamic mathematical model;

[0010] designing an original controller using a model predictive control algorithm according to the optimization objective;

[0011] Based on the dynamic mathematical model and typical meteorological year data, a temporary control strategy is generated using the original controller, the optimization target is calculated according to the temporary control strategy to obtain strategy execution effect data, and the original controller is iteratively optimized using a preset optimization algorithm according to the strategy execution effect data to obtain an optimized blind state controller;

[0012] Define control rules;

[0013] Monitor and collect external environmental data, perform weight matching on the control rules according to the external environmental data, and obtain a weight strategy;

[0014] Inputting the weight strategy and the external environment data into the louver state controller to generate a solution to obtain a louver adjustment strategy;

[0015] The photovoltaic blinds sunshade device is adjusted according to the blinds adjustment strategy.

[0016] Preferably, the dynamic mathematical model is constructed based on physical properties, light properties and temperature properties.

[0017] Preferably, the optimization objectives include: maximizing power generation, maximizing space lighting comfort, minimizing glare probability, and maximizing indoor vision.

[0018] Preferably, the optimization algorithm is a particle swarm optimization algorithm.

[0019] Preferably, the control rules include: the shape of the blinds, the degree of opening, the solar incident angle and the direct radiation intensity under different preset environments.

[0020] Preferably, the typical meteorological year data includes: solar altitude angle, solar azimuth angle, direct radiation vector data and ambient temperature data.

[0021] Preferably, the photovoltaic material used in the photovoltaic blind sunshade device is copper indium gallium selenide photovoltaic film.

[0022] Preferably, the external environment data includes: external light intensity and external temperature.

[0023] The present invention discloses the following technical effects:

[0024] The present invention provides a dynamic photovoltaic sunshade louver control method. By integrating a model prediction control method and a rule-based control method, it solves the problems of a single operating strategy and poor user experience of traditional louver devices, and realizes adaptive adjustment of the photovoltaic louver shading device according to optimization objectives and custom rules. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 A schematic diagram of the dynamic photovoltaic sunshade blind control process provided by an embodiment of the present invention;

[0027] Figure 2 A diagram of the movement pattern of photovoltaic sunshade louvers provided in an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of a typical form provided by an embodiment of the present invention;

[0029] Figure 4 A schematic diagram of an upper and lower folding configuration provided by an embodiment of the present invention;

[0030] Figure 5 A flow chart for obtaining a control strategy provided by an embodiment of the present invention;

[0031] Figure 6 This is a sunshade control process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments 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 are within the scope of protection of the present invention.

[0033] The purpose of the present invention is to provide a dynamic photovoltaic sunshade blind control method to solve the problems of single operation strategy and poor user experience of traditional blind devices.

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Figure 1 A schematic diagram of the dynamic photovoltaic sunshade louver control process provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a dynamic photovoltaic sunshade shutter control method, comprising:

[0036] Step 100: constructing a photovoltaic louver sunshade device;

[0037] Step 200: constructing a dynamic mathematical model based on the photovoltaic louver sunshade device;

[0038] Step 300: defining the optimization target of the dynamic mathematical model;

[0039] Step 400: Designing an original controller using a model predictive control algorithm according to the optimization objective;

[0040] Step 500: Based on the dynamic mathematical model and typical meteorological year data, a temporary control strategy is generated using the original controller; the optimization target is calculated according to the temporary control strategy to obtain strategy execution effect data; and based on the strategy execution effect data, the original controller is iteratively optimized using a preset optimization algorithm to obtain an optimized louver state controller;

[0041] Step 600: defining control rules;

[0042] Step 700: Monitor and collect external environment data, perform weight matching on the control rules according to the external environment data, and obtain a weight strategy;

[0043] Step 800: Inputting the weight strategy and the external environment data into the louver state controller to generate a solution and obtain a louver adjustment strategy;

[0044] Step 900: Adjust the photovoltaic blinds sunshade device according to the blinds adjustment strategy.

[0045] Preferably, the dynamic mathematical model is constructed based on physical properties, light properties and temperature properties.

[0046] Specifically, the optimization objectives include: maximizing power generation, maximizing space lighting comfort, minimizing glare probability, and maximizing indoor vision.

[0047] Optionally, the optimization algorithm is a particle swarm optimization algorithm.

[0048] Preferably, the control rules include: the shape of the blinds, the degree of opening, the solar incident angle and the direct radiation intensity under different preset environments.

[0049] Specifically, the typical meteorological year data includes: solar altitude angle, solar azimuth angle, direct radiation vector data and ambient temperature data.

[0050] Preferably, the photovoltaic material used in the photovoltaic blind sunshade device is copper indium gallium selenide photovoltaic film.

[0051] Optionally, the external environment data includes: external light intensity and external temperature.

[0052] Furthermore, this embodiment uses building performance simulation (BPS) and machine learning (ML) technology to obtain the mapping relationship between blinds parameters and external environmental conditions and thereby realize automatic control of blinds sunshade. While optimizing the indoor light environment, it can adaptively adjust to the user's needs and external environmental changes in real time, reduce the energy consumption of artificial lighting, and use the photovoltaic system to achieve self-power supply and provide a small amount of electricity for small distributed components such as sensors.

[0053] Specifically, the main components of photovoltaic blinds sunshade include external frame, slide rails, pulleys, rotating shafts, aluminum rotating rods, aluminum blinds support structure, circuit control system, power transmission system and energy storage system.

[0054] Furthermore, the material selection for photovoltaic louver shading includes the selection of supporting structure materials and photovoltaic materials. Aluminum alloy is selected as the supporting structure. When combining louver materials with photovoltaics, multiple factors need to be considered, including light transmittance, weather resistance, weight, structural strength, and cost. Aluminum alloy has the characteristics of being lightweight and high-strength and has good thermal conductivity, which helps dissipate heat from the photovoltaic system. Therefore, aluminum alloy is selected as the supporting structure of the photovoltaic shading system. Copper indium gallium selenide photovoltaic film is selected as the photovoltaic material and placed on the outer surface of the aluminum alloy frame. The reason is that its lower weight and customizable size can be applied to more possible photovoltaic shading forms, so it does not have to stick to the module restrictions of photovoltaic panels. Although cadmium telluride film can also achieve similar effects, it is easily decomposed at high temperatures to produce toxic and flammable compounds. Therefore, it is not suitable for indoor shading devices.

[0055] Optionally, the specifications of common copper indium gallium selenide photovoltaic films are shown in Table 1.

[0056] Table 1

[0057] name CIGS photovoltaic thin film Working current <![CDATA[4160 m A]]> size 1600*370*17mm power 75W weight 1700g Conversion rate 16.5％ Operating voltage 18V

[0058] Furthermore, the photovoltaic blinds sunshade system mainly consists of structural components, photovoltaic modules, inverters, drive devices, energy storage devices, and control systems. The specific functional description is shown in Table 2.

[0059] Table 2

[0060]

[0061] refer to Figure 2 The movement of photovoltaic blinds follows the constrained movement of traditional blinds, including single-axis rotation and up and down sliding. n is the number of panels (the top panel is 1 when moving upward, and the bottom panel is 1 when moving downward); m is the unit of movement, meter. At the same time, the blinds rely on the shaft controlled by the servo motor to achieve single-axis rotation, and the rotation angle A ranges from [0°, 90°] to ensure that the sunshade device can completely block the window and provide privacy protection for the room. The sunshade blinds can change the sunshade effect and the direction of light entry by adjusting the angle between the panels and the blades. When it is necessary to block the sunlight, the blades are rotated to a larger angle so that the sunlight cannot directly shine into the room, thereby reducing the light intensity and temperature in the room; when it is necessary to increase the light in the room, the blades can be rotated to a smaller angle to allow more sunlight to enter the room.

[0062] refer to Figure 3 , the louver blades slide up and down by means of the slide rails and pulleys on both sides. The sliding distance L of each louver is in the range of [-0.14×(i-1)-0.14×(ni-1)] (where n is the number of louver blades; i is the number of the louver blades) to achieve the goal of completely folding the louver blades to the upper or lower part of the window. The sunshade louvers adjust their shading effect and ventilation by sliding up and down on the slide rails. By sliding up and down, the overall position of the sunshade louvers can be changed. When it is necessary to block the sunlight, the sunshade louvers are slid to a state where they block less of each other, increasing their shading of the windows and preventing direct sunlight from entering the room. When it is necessary to increase ventilation and light entry, the sunshade louvers can be slid to one side to reduce the shading of the windows, provide more vision and allow more natural light and air to flow into the room.

[0063] Specifically, Model Predictive Control (MPC) simulates and optimizes the climatic conditions of a standard meteorological year and the building's location, including the climate zone. It then determines the optimal configuration for different demand weights and generates a schedule to control the shading configuration. MPC uses a dynamic mathematical model of the system to calculate the optimal control strategy at each moment based on the current state and future forecasts.

[0064] refer to Figure 4 The louver blades can achieve high-degree-of-freedom configuration through single-axis rotation and up-and-down folding, and obtain the corresponding optimal solution for specific usage needs. At the same time, they can also quickly switch to certain preset configurations, such as fully closed, fully open, and evenly distributed.

[0065] refer to Figure 5 , Model Predictive Control (MPC) can achieve more accurate and efficient automatic control of photovoltaic shading devices. The implementation steps include the following steps:

[0066] 1) Establish a dynamic model: First, establish a dynamic mathematical model of the photovoltaic shading device. This model includes the physical characteristics of the device, environmental conditions, and the influence of external factors such as light and temperature.

[0067] 2) Define control objectives: Determine control objectives, including maximizing power generation, maximizing spatial lighting comfort (sUDIMax), minimizing glare probability (sDGPMin), and maximizing indoor field of view. These objectives can be weighted differently based on the needs of the application scenario;

[0068] 3) Controller Design: Design a controller using a model predictive control algorithm. MPC is a model-based control method that calculates the optimal control strategy at each moment based on the current system state and a forecast model for a future period. The control strategy is represented as a sequence of control inputs at future moments, which is the optimal set of configurations optimized using typical meteorological year (TMY) data.

[0069] 4) Optimization problem solving: For each moment, an optimization algorithm (such as a genetic algorithm) is used to solve the optimization problem in controller design to determine the optimal control input sequence so that the system achieves the optimal control target within the prediction period;

[0070] 5) Real-time execution and adjustment: Adjust the shading configuration based on the optimal solution obtained at each moment. Controller parameters can be adjusted and optimized based on actual execution results, such as opening and closing the shading in inclement weather.

[0071] 6) Performance evaluation and improvement: Collect system data and regularly evaluate system performance, and improve the controller based on feedback information to further improve the control accuracy and efficiency of the system.

[0072] Furthermore, Rule-Based Control (RBC) uses a machine learning model to model the shading forms optimized for different demand preferences under specific environmental conditions. This model then obtains the relationship between environmental variables, shading forms, and user preferences. Combined with RBC, this model implements intelligent control of shading blinds. The process is as follows:

[0073] 1) Define control rules: First, define a series of control rules that describe the appropriate shading configurations for different environmental conditions. These rules are derived from a machine learning model trained on a dataset. For example, when the sun is high and the user wants a better indoor light environment and minimizes glare, the appropriate shading angle or opening level should be used.

[0074] 2) Write control rules: Based on the defined control rules, write the corresponding rule set. Each rule will generate a corresponding shading shape control output based on the current environmental conditions and system status.

[0075] 3) Real-time monitoring and adjustment: During system operation, environmental parameters are monitored in real time and the shading configuration is adjusted according to predefined control rules. System performance is regularly checked and adjustments are made based on user feedback or system operation status.

[0076] refer to Figure 6 , MPC specific process:

[0077] 1) Field of view dataset: obtain the openness index (field of view ratio) of all 2275 morphological combinations based on geometric calculations, and search and call them during the optimization process.

[0078] 2) Light environment prediction model: Using existing light environment simulation software, a machine learning model was trained based on five characteristic values: solar altitude angle, solar azimuth, horizontal direct radiation, shading angle, and shading position. This model was used to predict the glare ratio and effective illumination ratio of indoor spaces. The model was used for prediction during the optimization process. After model comparison and evaluation, the regression model achieved an R2 of over 0.95.

[0079] 3) Photovoltaic power generation prediction model: Based on the Perez sky model, the photovoltaic surface irradiation can be calculated according to parameters such as surface orientation, inclination, time, and geographical location. Through the equivalent single diode model, the power generation efficiency can be corrected according to the photovoltaic rated power, the actual photovoltaic surface irradiation and the outdoor temperature.

[0080] 4) Optimization model: The shading angle and shading position parameters are optimized through the particle swarm optimization algorithm. According to the predicted field of view, glare protection, illumination and power generation and the corresponding weight input, the optimal shading form under the current environment and demand weight is obtained.

[0081] The beneficial effects of the present invention are as follows:

[0082] The present invention realizes adaptive adjustment of the photovoltaic blind shading device according to optimization objectives and customized rules by integrating the model prediction control method and the rule-based control method, thereby improving the flexibility and versatility of the device.

[0083] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0084] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A dynamic photovoltaic sunshade louver control method, characterized in that: include: Construct photovoltaic louver shading devices; Constructing a dynamic mathematical model based on the photovoltaic louver sunshade device; defining an optimization objective of the dynamic mathematical model; designing an original controller using a model predictive control algorithm according to the optimization objective; Based on the dynamic mathematical model and typical meteorological year data, a temporary control strategy is generated using the original controller, the optimization target is calculated according to the temporary control strategy to obtain strategy execution effect data, and the original controller is iteratively optimized using a preset optimization algorithm according to the strategy execution effect data to obtain an optimized blind state controller; Define control rules; Monitor and collect external environmental data, perform weight matching on the control rules according to the external environmental data, and obtain a weight strategy; Inputting the weight strategy and the external environment data into the louver state controller to generate a solution to obtain a louver adjustment strategy; The photovoltaic blinds sunshade device is adjusted according to the blinds adjustment strategy.

2. A dynamic photovoltaic sunshade louver control method according to claim 1, characterized in that: The dynamic mathematical model is constructed based on physical characteristics, light characteristics and temperature characteristics.

3. A dynamic photovoltaic sunshade louver control method according to claim 1, characterized in that: The optimization objectives include: maximizing power generation, maximizing space lighting comfort, minimizing glare probability, and maximizing indoor vision.

4. The dynamic photovoltaic sunshade louver control method according to claim 1, characterized in that: The optimization algorithm is a particle swarm optimization algorithm.

5. The dynamic photovoltaic sunshade louver control method according to claim 1, characterized in that: The control rules include: the shape of the blinds, the degree of opening, the solar incident angle and the direct radiation intensity under different preset environments.

6. A dynamic photovoltaic sunshade louver control method according to claim 1, characterized in that: The typical meteorological year data includes: solar altitude angle, solar azimuth angle, direct radiation vector data and ambient temperature data.

7. The method for controlling dynamic photovoltaic sunshade blinds according to claim 1, characterized in that: The photovoltaic material used in the photovoltaic blind sunshade device is copper indium gallium selenide photovoltaic film.

8. The dynamic photovoltaic sunshade louver control method according to claim 1, characterized in that: The external environment data includes: external light intensity and external temperature.

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