Photovoltaic integration scheme generation method and system based on big data prediction

Abstract

The present invention relates to the field of photovoltaic power generation technology, and discloses a photovoltaic integration solution generation method and system based on big data prediction, the method comprising: S1: obtaining user's location information, roof information and power consumption information; S2: calculating the user's local lighting information based on the location information; S3: calculating the user's local optimal installation inclination angle and annual effective use of light time based on the user's local light information; S4: calculating the number of required photovoltaic modules based on the user's power consumption information and the annual effective use of light time; S5: obtaining the arrangement plan of photovoltaic modules on the user's roof based on the number of required photovoltaic modules and the optimal installation inclination angle and the roof information. The photovoltaic integration solution generation method based on big data prediction provided by the present invention generates the photovoltaic integration arrangement plan that best suits the local situation through big data analysis to achieve the best power generation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a method and system for generating a photovoltaic integration solution based on big data prediction. Background Art

[0002] With the gradual exhaustion of non-renewable energy sources such as oil and coal and the increasing environmental pollution, photovoltaic power generation has become a major development trend in the power industry.

[0003] In the design of photovoltaic power generation systems, the arrangement of photovoltaic arrays and the arrangement scheme are crucial for accurately evaluating the installation capacity and power generation of photovoltaic systems. A reasonable arrangement scheme can increase the installation capacity of the system and reduce the power generation loss caused by shadow shading.

[0004] The key factors that need to be considered in the photovoltaic array layout plan include local solar radiation, installation space, photovoltaic module orientation, inclination, installation spacing, etc. Usually, the photovoltaic array layout design is mainly completed by designers based on experience or with the help of auxiliary software. The key factors such as the layout mode, inclination, orientation, spacing, etc. of photovoltaic modules are mostly empirical parameters, which cannot achieve the layout result that maximizes the system installation capacity or the system annual power generation; in addition, the existing optimization layout technology solutions consider a relatively single scenario and cannot handle complex situations such as irregular installation areas, multiple installation areas, multiple obstacles and shadows. Summary of the invention

[0005] The present invention aims to provide a photovoltaic integration scheme generation method based on big data prediction. Through big data analysis, combined with local sunlight, installation space and electricity consumption, a photovoltaic integration layout scheme that best suits local conditions is generated to achieve the best power generation efficiency, eliminating the need for designers to conduct on-site surveys and avoiding the problem that designers' experience-based judgment makes it difficult to achieve the optimal solution.

[0006] The technical solution provided by the present invention is: a photovoltaic integration solution generation method based on big data prediction, comprising:

[0007] S1: Obtain the user's location information, roof information and power consumption information;

[0008] S2: Calculate the user's local lighting information based on the location information;

[0009] S3: Calculate the optimal installation angle and annual effective sunlight time of the user's local area according to the user's local sunlight information;

[0010] S4: Calculate the number of photovoltaic modules required based on the user's electricity consumption information and the annual effective use of sunlight time;

[0011] S5: Based on the required number of PV modules and the optimal installation angle, combined with the roof information, the layout plan of the PV modules on the user's roof is obtained.

[0012] The working principle and advantages of the present invention are: firstly, the geographical location information of the user is obtained, and the roof information of the installation space and the user's recent power consumption information are provided. According to the user's location information, combined with big data analysis, the local lighting information of the user is obtained. According to the lighting situation, the optimal installation inclination angle and the annual effective use of light time of the local photovoltaic components of the user are calculated. On the basis of the annual effective use of light time, the number of required photovoltaic components is predicted and calculated so that the power generation can match the user's power consumption. Finally, according to the number of required photovoltaic components and the optimal installation inclination angle, combined with the situation of roof information, the array position of the photovoltaic components is intelligently arranged to obtain the arrangement scheme. The method of the present invention only needs to obtain the user's location information, roof information and power consumption information, which are three data that can be conveniently provided by the user. On this basis, through the analysis and calculation of big data, according to the user's needs, the optimal installation inclination angle and the number of required photovoltaic components when the user installs photovoltaics locally are obtained, and finally according to the user's roof information, a specific arrangement scheme is generated according to local conditions. Through big data analysis and prediction, it is possible to take into account the problems that designers cannot consider based on experience, and to achieve a highly efficient arrangement scheme to the greatest extent. At the same time, this method only requires users to provide relevant basic information, and does not require designers to conduct on-site surveys, which improves the efficiency of solution generation and reduces the cost of manpower and material resources.

[0013] Further, the illumination information includes illumination angle and annual average illumination time on the horizontal plane;

[0014] The S2 includes:

[0015] S2-1: Obtain the user's local latitude and longitude and weather data based on the user's location information;

[0016] S2-2: Calculate the local lighting angle by longitude and latitude;

[0017] S2-3: Conduct statistical analysis on local meteorological data to obtain the annual average sunshine time on the horizontal surface.

[0018] In the process of photovoltaic layout design, the location of the photovoltaic array is determined by taking into account the local lighting conditions, mainly including the lighting angle and the average annual lighting time on the horizontal plane. The local longitude and latitude and meteorological data are obtained based on the location information. The angle range of local lighting can be calculated through the longitude and latitude. The local historical meteorological data is statistically collated and analyzed to obtain the local average annual lighting time on the horizontal plane.

[0019] Further, the S3 includes:

[0020] S3-1: Calculate the best installation angle based on the illumination angle;

[0021] S3-2: Based on the average illumination time on the horizontal plane and the optimal installation inclination angle, calculate the average annual illumination time at the optimal inclination angle;

[0022] S3-3: Calculate the annual effective sunlight time based on the average annual sunlight time at the optimal inclination angle and system losses.

[0023] According to the angle of light, the inclination angle at which the photovoltaic module can receive the most light is calculated. The original data is the average light time on the horizontal plane. Based on the optimal installation inclination, the annual average light time at the optimal inclination is calculated. Because there is an inevitable conversion system loss in the process of photovoltaic modules converting light energy into electrical energy, the annual effective use of light time is calculated based on the system loss, which serves as the basis for the next step of calculating the number of photovoltaic modules.

[0024] Further, the S4 includes:

[0025] S4-1: Analyze the user's recent power consumption and user type based on power consumption information to determine the user's future power consumption;

[0026] S4-2: Calculate the number of PV panels required based on the user’s future electricity consumption.

[0027] In addition to meeting the current electricity demand, it is also necessary to further consider the user's future electricity consumption to avoid secondary renovation and expansion at that time. Based on the electricity consumption information, this method first determines whether the user is a residential user or an enterprise user. The future electricity consumption of residential users is generally small, and the future electricity consumption of enterprise users may further increase. Then, the increase or decrease of the user's recent electricity consumption is analyzed, and the total number of photovoltaic components required by the user to meet both the current and future needs is calculated based on the growth rate.

[0028] Further, the S4 includes:

[0029] S4-1: Calculate the number of PV panels required based on the user's electricity consumption information and the annual effective use of sunlight time;

[0030] S4-2: Obtain the local photovoltaic power generation data of the user and analyze the average power generation of a single photovoltaic module;

[0031] S4-3: Based on the average power generation of a single photovoltaic module and the user's power consumption information, the number of predicted photovoltaic modules is calculated;

[0032] S4-4: Compare the predicted number of photovoltaic modules with the required number of photovoltaic modules, and adjust the required number of photovoltaic modules.

[0033] This method also collects and obtains the local photovoltaic power generation data of the user for analysis, and obtains the average power generation of a single photovoltaic module in the local area based on the big data analysis results. Based on this, the number of photovoltaic modules is predicted again, and the number of required photovoltaic modules calculated previously is used as a reference and adjusted based on this prediction result. This can further improve the authenticity of the calculation results.

[0034] Furthermore, the arrangement scheme includes array positions and spacing distances.

[0035] The layout plan mainly includes the array position of photovoltaic modules on the roof and the spacing distance between photovoltaic modules.

[0036] Furthermore, the arrangement scheme also includes an inspection channel.

[0037] In addition to the array position and spacing distance of PV modules, this method also takes into account the inspection channels that need to be reserved when staff conduct inspections, so as to facilitate the operation and maintenance of PV modules after installation.

[0038] Further, the roof information includes a roof plan layout and roof image information;

[0039] The S5 includes:

[0040] S5-1: Identify obstructions based on roof image information;

[0041] S5-2: Analyze the obstruction area of ​​the obstruction object and cross out the obstruction area on the roof plan;

[0042] S5-3: Generate a layout plan of photovoltaic modules on the user's roof in the remaining part of the roof plan.

[0043] The user provides the roof layout and image information. The system uses image recognition to determine whether there are any obstructions on the roof that affect the lighting effect. It then analyzes the area blocked by the obstructions and generates a layout plan on the layout diagram to avoid the blocked area. This step avoids the impact of obstructions on the lighting effect and further improves the efficiency of photovoltaic power generation.

[0044] Furthermore, it also includes S6: conducting simulation tests on the arrangement of photovoltaic modules on the user's roof in combination with local lighting information to analyze whether the power generation results meet expectations.

[0045] After the PV panel layout is generated, the system will also substitute the user's local lighting information and usage environment for simulation and testing, output the power generation results, and analyze and judge whether the power generation results meet expectations. If the simulation test results are not as expected, corresponding adjustments can be made before the plan is finalized.

[0046] The present invention also provides a photovoltaic integration scheme generation system based on big data prediction, which uses the above-mentioned photovoltaic integration scheme generation method based on big data prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a logic block diagram of Embodiment 1 of a photovoltaic integration scheme generation method based on big data prediction according to the present invention;

[0048] Figure 2 This is a logic block diagram of Embodiment 2 of the photovoltaic integration scheme generation method based on big data prediction of the present invention. DETAILED DESCRIPTION

[0049] Embodiment 1:

[0050] This embodiment discloses a photovoltaic integration solution generation method based on big data prediction, and the logical process is as follows: Figure 1 As shown, the method includes the following steps (the numbering of each step in this scheme is only used to distinguish the steps, and does not limit the specific execution order of each step, and each step can also be performed simultaneously):

[0051] S1: Obtain the user's local location information, the roof information where the user can install photovoltaic modules, and the user's recent electricity consumption information.

[0052] S2-1: According to the user's local location information, obtain the user's local longitude and latitude, and obtain the user's local historical meteorological data on the Internet platform.

[0053] S2-2: Calculate the angle range of light exposure in the local area based on the local longitude and latitude, and take the median of the angle range as the light angle.

[0054] S2-3: Collect and sort out local meteorological data, specifically the sunshine time for each day in recent years, and obtain the horizontal plane sunshine time for each of the past three years. Then calculate the average value to obtain the annual average horizontal plane sunshine time.

[0055] S3-1: Calculate the optimal installation angle of the photovoltaic module facing the sunlight based on the sunlight angle.

[0056] S3-2: Based on the average illumination time on the horizontal plane and the optimal installation inclination angle, calculate the average annual illumination time at the optimal inclination angle.

[0057] S3-3: Calculate the annual effective use of light time based on the average annual light time at the optimal tilt angle and the system loss. According to statistical data, the total system loss rate of the photovoltaic module in converting light energy into electrical energy in this embodiment is 20%. The annual effective use of light time is obtained by multiplying the average annual light time at the optimal tilt angle by 80%.

[0058] S4-1: Analyze the user's recent electricity consumption and user type based on the electricity consumption information to determine the user's future electricity consumption. First, determine whether the user is a residential user or an enterprise user based on the information on the user's electricity consumption payment list. In this embodiment, the user is determined to be an enterprise user. Then, the user's electricity consumption data for the past three years is obtained to calculate the electricity consumption growth rate. The user's future electricity consumption in the next three years is determined based on this growth rate.

[0059] S4-2: Calculate the number of photovoltaic modules required based on the user's future electricity consumption. Based on the user's future electricity consumption and the annual effective use of sunlight time, calculate the number of photovoltaic modules required by the user.

[0060] S5-1: Identify obstructions based on roof image information. In this embodiment, the user takes a photo of the entire roof at or above the roof to obtain roof image data, and after clearing the image, identifies obstructions in the roof image data using a machine recognition algorithm.

[0061] S5-2: Analyze the area blocked by the obstruction and remove the blocked area on the roof plan. Calculate the area that may be blocked by the obstruction from the image data at different angles and remove the corresponding blocked area on the roof plan.

[0062] S5-3: Generate the arrangement plan of photovoltaic modules on the user's roof in the remaining part of the roof plan. After obtaining the corresponding blocked part, plan the arrangement plan of photovoltaic modules in the remaining unblocked part of the roof, including the location of the photovoltaic module array on the roof, and the spacing between photovoltaic modules, and open a channel in the middle of the photovoltaic module array as a patrol and maintenance channel for staff.

[0063] S6: Conduct simulation tests on the layout of photovoltaic modules on the user's roof in combination with local lighting information, analyze whether the power generation results meet expectations, and send the test results to designers.

[0064] Embodiment 2:

[0065] The logic flow of this embodiment is as follows Figure 2 As shown, the difference between the second embodiment and the first embodiment is that:

[0066] The S4 includes:

[0067] S4-1: Based on the user's electricity consumption information and the annual effective use of sunlight time, calculate the number of photovoltaic modules required.

[0068] S4-2: Obtain the local photovoltaic power generation data of the user, and analyze it to obtain the average power generation of a single photovoltaic module. Obtain the photovoltaic installed capacity data and annual power generation data of other users in the user's local area, perform data cleaning, filter out abnormal values ​​and missing values, and calculate the average power generation of a single photovoltaic module in the local area.

[0069] S4-3: Based on the average power generation of a single PV module and the user's power consumption information, the predicted number of PV modules is calculated.

[0070] S4-4: The predicted number of PV modules is compared with the required number of PV modules, and the required number of PV modules is adjusted. If the difference between the predicted number of PV modules and the required number of PV modules is too large, the average of the two is taken; if the difference between the two is small, the larger number is used.

[0071] The rest of this embodiment is the same as the first embodiment.

[0072] The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the relevant field know all the common technical knowledge in the technical field to which the invention belongs before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment obtained by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A photovoltaic integration scheme generation method based on big data prediction, characterized in that: include: S1: Obtain the user's location information, roof information and power consumption information; S2: Calculate the user's local lighting information based on the location information; S3: Calculate the optimal installation angle and annual effective sunlight time of the user's local area according to the user's local sunlight information; S4: Calculate the number of photovoltaic modules required based on the user's electricity consumption information and the annual effective use of sunlight time; S5: Based on the required number of photovoltaic modules and the optimal installation angle, combined with the roof information, the arrangement plan of photovoltaic modules on the user's roof is obtained; The illumination information includes illumination angle and annual average illumination time on the horizontal plane; The S2 includes: S2-1: Obtain the user's local latitude and longitude and weather data based on the user's location information; S2-2: Calculate the local lighting angle by longitude and latitude; S2-3: Statistical analysis of local meteorological data to obtain the annual average sunshine time on the horizontal surface; The S3 includes: S3-1: Calculate the best installation angle based on the illumination angle; S3-2: Based on the average illumination time on the horizontal plane and the optimal installation inclination angle, calculate the average annual illumination time at the optimal inclination angle; S3-3: Calculate the annual effective sunlight time based on the average annual sunlight time at the optimal inclination angle and the system loss; The S4 includes: S4-1: Based on the user's electricity consumption information and the annual effective use of sunlight time, the number of photovoltaic modules required is calculated. S4-2: Obtain the local photovoltaic power generation data of the user and analyze the average power generation of a single photovoltaic module; Obtain the photovoltaic installed capacity and annual power generation data of other users in the user's local area, perform data cleaning, filter out abnormal values ​​and missing values, and calculate the average power generation of a single photovoltaic module in the local area; S4-3: Based on the average power generation of a single photovoltaic module and the user's power consumption information, the number of predicted photovoltaic modules is calculated; S4-4: Compare the predicted number of photovoltaic modules with the required number of photovoltaic modules and adjust the required number of photovoltaic modules; The arrangement scheme includes array positions and spacing distances; The arrangement scheme also includes an inspection channel; The roof information includes a roof plan and roof image information; The S5 includes: S5-1: Identify obstructions based on roof image information; S5-2: Analyze the obstruction area of ​​the obstruction object and cross out the obstruction area on the roof plan; S5-3: Generate a layout plan of photovoltaic modules on the user's roof in the remaining part of the roof plan.

2. The photovoltaic integration scheme generation method based on big data prediction according to claim 1 is characterized in that it also includes S6: simulating the arrangement scheme of photovoltaic components on the user's roof in combination with local lighting information to analyze whether the power generation results meet expectations.

3. Photovoltaic integration scheme generation system based on big data prediction, characterized by: The system uses the photovoltaic integration scheme generation method based on big data prediction as described in any one of claims 1-2 above.

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