Management method based on photovoltaic cell panel energy efficiency analysis

Abstract

The invention relates to the technical field of photovoltaic cell management, and discloses a photovoltaic cell panel energy efficiency analysis-based management method, which comprises the steps of establishing a weather sample set based on acquired historical weather data of a current region; obtaining cell panel energy efficiency data corresponding to the current weather sample set; combining the weather sample set and the battery panel energy efficiency data corresponding to the weather sample set to construct a battery panel energy efficiency prediction model; and generating the running state of the current photovoltaic cell panel in combination with the obtained weather data of the current monitoring time node and the cell panel energy efficiency prediction model, and generating a control instruction according to the running state of the current photovoltaic cell panel. Through the steps of setting the energy efficiency compatibility interval, optimizing and generating the first-level weather data, obtaining the operation state, generating the operation state sample set and the like, the cell panel energy efficiency prediction model is constructed to process interaction among various environmental factors, and the accuracy of energy efficiency prediction and operation state judgment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic cell management, and particularly to a management method based on the energy efficiency analysis of photovoltaic panels. Background Art

[0002] In the context of the growing global demand for clean energy, photovoltaic energy, as a renewable and pollution-free energy source, has an expanding application scale. However, the energy efficiency of photovoltaic panels is affected by various factors, such as weather conditions and environmental factors. In order to better utilize photovoltaic energy, improve its power generation efficiency and ensure stable operation, an effective energy efficiency analysis and management method is needed.

[0003] The efficiency of photovoltaic panels is directly related to the overall performance and economy of photovoltaic power generation systems. By deeply analyzing the energy efficiency of the panels, the system design, installation, and operation can be optimized, costs can be reduced, and the competitiveness of photovoltaic power generation in the energy market can be improved. For example, accurately predicting the energy efficiency of the panels under different weather conditions helps to reasonably arrange the capacity of the energy storage system and improve the utilization rate of energy. Summary of the Invention

[0004] The object of the present invention is to: classify based on historical weather data, quantify environmental data to generate reference values, construct a weather sample set by combining a sub-dataset and the reference values, and construct a panel energy efficiency prediction model through steps such as setting an energy efficiency compatibility interval, optimizing to generate primary weather data, obtaining the operating state, and generating an operating state sample set.

[0005] To achieve the above object, the present invention provides a management method based on the energy efficiency analysis of photovoltaic panels, including: Establishing a weather sample set based on the obtained historical weather data of the current region; Obtaining the panel energy efficiency data corresponding to the current weather sample set; Constructing a panel energy efficiency prediction model by combining the weather sample set and the panel energy efficiency data corresponding to the weather sample set; Generating the operating state of the current photovoltaic panel by combining the obtained weather data at the current monitoring time node and the panel energy efficiency prediction model, and generating a control instruction according to the operating state of the current photovoltaic panel.

[0006] In some embodiments of the present invention, when establishing the weather sample set, it includes: Classifying the historical weather data based on weather types to generate multiple sub-historical weather data sets; Quantifying each type of environmental data based on the current sub-historical weather data to generate a reference value for each type of environmental data; Obtain the reference values of all types of environmental data in the current sub-historical weather dataset to generate an environmental reference value set R, R = {R1, R2…R i …R n}; Among them, R i represents the reference value of the i-th type of environmental data in the current sub-historical weather dataset, and n represents the total number of environmental data types in the current sub-historical weather dataset; Combine all sub-historical weather datasets and the corresponding environmental reference value set R to generate the weather sample set of the current region.

[0007] In some embodiments of the present invention, when constructing the photovoltaic panel energy efficiency prediction model, it includes: Obtain the photovoltaic panel energy efficiency data corresponding to the current weather sample set; Combine the energy efficiency data and the environmental reference value set R to set the energy efficiency compatibility interval; Optimize the current sub-historical weather dataset according to the energy efficiency compatibility interval to generate the first-level weather data; Obtain various operating states of the photovoltaic panel based on the historical operating data of the photovoltaic panel; Obtain the first-level weather data corresponding to each operating state of the photovoltaic panel to generate the operating state sample set; Construct the photovoltaic panel energy efficiency prediction model based on the operating state sample set.

[0008] In some embodiments of the present invention, when combining the energy efficiency data and the environmental reference value set R to set the energy efficiency compatibility interval, it includes: Obtain the photovoltaic panel energy efficiency data corresponding to the current weather sample set; Combine all the photovoltaic panel energy efficiency data to generate the corresponding energy efficiency reference values, and sort the energy efficiency reference values to generate the energy efficiency reference value set P, P = {P1, P2…P j …P m}; Among them, P1 represents the minimum value of the energy efficiency reference value, P m represents the maximum value of the energy efficiency reference value; Pj represents the energy efficiency reference value of the j-th sequence, and m represents the total number of energy efficiency reference values; Set the compatibility interval g based on the energy efficiency reference value set P;

[0009] Among them, is the average value of the energy efficiency reference value set P; Combine the compatibility interval g and the energy efficiency reference value set P to generate the number b of energy efficiency compatibility intervals;

[0010] Divide the energy efficiency reference value set P by combining the compatible interval g and the number b of energy efficiency compatible intervals to generate the energy efficiency compatible interval set PH, PH = {PH1, PH2... PH k …PH b}; Among them, b < m, PH k represents the k-th energy efficiency compatible interval of the energy efficiency reference value set P; b represents the total number of energy efficiency compatible intervals; PH k = [Pa, Pb]; Pa = P1 + (k - 1) * g; Pb = P1 + k * g; Pa is the left endpoint of the energy efficiency compatible interval, and Pb is the right endpoint of the energy efficiency compatible interval.

[0011] In some embodiments of the present invention, when generating the first-level weather data, it includes: Generate the first-level weather data based on one or more weather sample sets corresponding to the energy efficiency compatible intervals; The first-level weather data is a set of environmental reference value intervals; Obtain the environmental reference value set R in the current first-level weather data, and calculate the correlation between each environmental reference value in the environmental reference value set R and the energy efficiency reference value to obtain the first correlation reference value; Compare the first correlation reference value with the preset correlation reference value, and eliminate the environmental types corresponding to the environmental reference values lower than the preset correlation reference value to generate the second environmental reference value set R2 of the current first-level weather data; Take the union of the environmental reference values of the environmental types in the second environmental reference value set R2 corresponding to the first-level weather data to generate the environmental reference value interval of the current environmental type; Combine the environmental reference value intervals of all environmental types to generate the environmental reference value interval set.

[0012] In some embodiments of the present invention, when generating the operating state sample set, it includes: Obtain the first-level weather data and the energy efficiency compatible intervals corresponding to the current operating state of the photovoltaic panel; Combine the current operating state of the photovoltaic panel, the first-level weather data, and the energy efficiency compatible intervals to generate an operating state sample; Obtain the operating state samples of all operating state types to generate the operating state sample set.

[0013] In some embodiments of the present invention, when generating the operating state of the current photovoltaic panel, it includes: Obtain the weather data at the current monitoring time node; Based on the weather data, obtain the environmental reference value set R at the current monitoring time node; Determine the corresponding primary weather data in combination with the environmental reference value set R and the environmental reference value interval set; Obtain the energy efficiency data of the solar panel at the current monitoring time node and generate a real-time energy efficiency reference value Ps; Determine the operating state of the photovoltaic panel at the current monitoring time node by comparing the real-time energy efficiency reference value and the primary weather data with the operating state sample set.

[0014] In some embodiments of the present invention, when generating a control instruction according to the operating state of the current photovoltaic panel, it includes: Set an occurrence degree evaluation model for each operating state based on historical data; Calculate the occurrence degree evaluation value mq of the current operating state based on the environmental reference value set R of the photovoltaic panel at the current monitoring time node;

[0015] Wherein, d1 represents the first fixed coefficient, d2 represents the second fixed coefficient, v represents the number of environmental types in the second environmental reference value set R2, represents the xth type of environmental reference value, c x represents the weight of the xth type of environmental reference value, represents the real-time energy efficiency reference value; Generate a multi-level control instruction by comparing the occurrence degree evaluation value mq of the current operating state with the preset evaluation value.

[0016] In some embodiments of the present invention, when generating a multi-level control instruction, it includes: Calculate the occurrence degree evaluation values of different stages of the current operating state in combination with the current sub-historical weather data and the occurrence degree evaluation model; And set corresponding preset evaluation values according to the occurrence degree evaluation values of different stages, including: the first preset evaluation value e1, the second preset evaluation value e2; If mq < e1, generate a first-level control instruction; If e1 ≤ mq < e2, generate a second-level control instruction; If e2 ≤ mq, generate a third-level control instruction.

[0017] Compared with the prior art, the management method based on the energy efficiency analysis of photovoltaic panels provided by the embodiments of the present invention has the following beneficial effects: By establishing a weather sample set based on historical weather data and constructing a battery panel energy efficiency prediction model in combination with the corresponding battery panel energy efficiency data, the energy efficiency of the photovoltaic panel can be predicted more accurately. In the process of constructing the model, various environmental factors (through the construction and processing of the weather sample set) and the energy efficiency data of the battery panel itself are fully considered.

[0018] During the process of establishing the weather sample set and constructing the energy efficiency prediction model, the environmental data was carefully processed, which helps to effectively integrate complex environmental factors into energy efficiency analysis and operating status management, enabling the management method to adapt to different environmental conditions and improving the performance management ability of photovoltaic panels in various environments.

[0019] By calculating the correlation between the environmental reference value and the energy efficiency reference value, environmental types below the preset correlation reference value were excluded, thus optimizing the set of environmental factors closely related to the energy efficiency of the battery panels.

[0020] Multilevel control instructions are generated based on the comparison between the evaluation value of the occurrence degree of different operating states and the preset value, which makes the control instructions more reasonable and targeted. Brief Description of the Drawings

[0021] Figure 1 It is a flowchart of a management method based on energy efficiency analysis of photovoltaic panels provided by an embodiment of the present invention. Detailed Embodiments

[0022] The following will further describe in detail the specific embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0023] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0024] The terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "plurality" is two or more.

[0025] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] Embodiment 1: A management method based on the energy efficiency analysis of photovoltaic panels provided by an embodiment of the present invention, as Figure 1 shown, includes: Establishing a weather sample set based on the historical weather data of the current area; Obtaining the panel energy efficiency data corresponding to the current weather sample set; Constructing a panel energy efficiency prediction model by combining the weather sample set and the panel energy efficiency data corresponding to the weather sample set; Generating the operating state of the current photovoltaic panel by combining the weather data at the currently monitored time node and the panel energy efficiency prediction model, and generating a control instruction according to the operating state of the current photovoltaic panel.

[0027] In this embodiment, the weather types can be further subdivided. In addition to the common types such as sunny, cloudy, rainy, and snowy, sunny days can be further divided into slightly cloudy sunny days, mostly cloudy sunny days, etc. according to the amount of cloud cover; cloudy days can be divided into thin-cloudy days and thick-cloudy days according to the cloud layer thickness. For special weather such as thunderstorm weather, it can be further classified according to factors such as the intensity and duration of the thunderstorm.

[0028] During the classification process, refer to the standards and specifications of meteorology to ensure the scientificity and accuracy of the classification. At the same time, use data mining technology to quickly and accurately classify the historical weather data to improve the classification efficiency.

[0029] For each subdivided weather type, select the data belonging to this type from the massive historical weather data. These data include but are not limited to meteorological elements such as temperature, pressure, humidity, wind speed, and wind direction.

[0030] When screening the data, pay attention to the integrity and consistency of the data to avoid data loss or errors. Organize the screened data to form independent sub-historical weather data sets, providing basic data for subsequent analysis.

[0031] Embodiment 2: When establishing the weather sample set, it includes: Classifying the historical weather data based on the weather types to generate multiple sub-historical weather data sets; Quantify each type of environmental data based on the current sub-historical weather data to generate the reference value of each type of environmental data; Obtain the reference values of all types of environmental data in the current sub-historical weather dataset to generate the environmental reference value set R, R = {R1, R2…R i …R n}; Among them, R i represents the reference value of the i-th type of environmental data in the current sub-historical weather dataset, and n represents the total number of environmental data types in the current sub-historical weather dataset; Combine all sub-historical weather datasets and the corresponding environmental reference value set R to generate the weather sample set of the current region.

[0032] In this embodiment, the selection of environmental data should be comprehensive and representative. In addition to basic meteorological elements, factors such as different bands of solar radiation intensity (such as ultraviolet, visible light, infrared, etc.) and atmospheric transparency can also be considered.

[0033] For each type of environmental data, establish an accurate quantification standard. For example, for temperature, it can be quantified according to the international temperature scale; for humidity, it can be expressed as a percentage of relative humidity, and different quantification levels can be set according to different humidity ranges.

[0034] For solar radiation intensity, use professional radiation measurement instruments for measurement, and quantify the measurement results according to energy units (such as watts per square meter).

[0035] Taking each data record in the sub-historical weather dataset as a unit, quantify each type of environmental data according to the quantification standard. For example, for a weather data record at a specific time point, quantify its environmental data such as temperature, humidity, and solar radiation intensity into corresponding values.

[0036] Integrate the reference values of all types of environmental data (such as n types) in the sub-historical weather dataset to form a set R. For example, for a certain sub-dataset, if there are 3 types of environmental data: temperature, humidity, and light intensity (n = 3), obtain their reference values R1 (temperature reference value), R2 (humidity reference value), and R3 (light intensity reference value) respectively, and form the set R.

[0037] Combine all sub-historical weather datasets and the corresponding environmental reference value set R to generate the weather sample set of the current region.

[0038] Integrate all sub-historical weather datasets and their corresponding environmental reference value set R together to form a complete weather sample set. This sample set contains the reference values of various environmental data under different weather types, providing basic data for subsequent construction of the battery panel energy efficiency prediction model.

[0039] Use the quantified values as the reference values for each type of environmental data to ensure that the reference values can accurately reflect the environmental conditions at that time.

[0040] Example 3: When constructing the photovoltaic panel energy efficiency prediction model, it includes: Obtain the photovoltaic panel energy efficiency data corresponding to the current weather sample set; Set the energy efficiency compatibility interval in combination with the energy efficiency data and the environmental reference value set R; Optimize the current sub-historical weather data set in combination with the energy efficiency compatibility interval to generate first-level weather data; Obtain various operating states of the photovoltaic panel based on the historical operating data of the photovoltaic panel; Obtain the first-level weather data corresponding to each operating state of the photovoltaic panel to generate an operating state sample set; Construct a photovoltaic panel energy efficiency prediction model based on the operating state sample set.

[0041] In this embodiment, traverse each data record in the current sub-historical weather data set and extract the reference values of various types of environmental data that have been quantified.

[0042] Combine these reference values in a certain order (such as according to the importance of environmental data types or a fixed arrangement order) to form the environmental reference value set R. During the combination process, ensure the accuracy of each reference value and the correctness of the corresponding relationship.

[0043] Verify the generated environmental reference value set R. Check whether there are unreasonable reference values, such as values outside the normal range or values with logical contradictions with other reference values.

[0044] If problems are found, trace back to the previous quantification and data extraction steps for adjustment. At the same time, perform appropriate standardization processing on the reference values in the set R according to actual needs to make them more convenient for subsequent analysis and calculation.

[0045] Example 4: When setting the energy efficiency compatibility interval in combination with the energy efficiency data and the environmental reference value set R, it includes: Obtain the photovoltaic panel energy efficiency data corresponding to the current weather sample set; Generate corresponding energy efficiency reference values by combining all the photovoltaic panel energy efficiency data, and sort the energy efficiency reference values to generate an energy efficiency reference value set P, P = {P1, P2…P j …P m}; Among them, P1 represents the minimum value of the energy efficiency reference value, P mrepresents the maximum value of the energy efficiency reference value; Pj represents the energy efficiency reference value of the j-th sequence, and m represents the total number of energy efficiency reference values; Set the compatibility interval g based on the energy efficiency reference value set P;

[0046] Among them, is the average value of the energy efficiency reference value set P; Generate the number b of energy efficiency compatibility intervals by combining the compatibility interval g and the energy efficiency reference value set P;

[0047] Divide the energy efficiency reference value set P by combining the compatibility interval g and the number b of energy efficiency compatibility intervals to generate the energy efficiency compatibility interval set PH, PH = {PH1, PH2…PH k …PH b }; Among them, b < m, PH k represents the k-th energy efficiency compatibility interval of the energy efficiency reference value set P; b represents the total number of energy efficiency compatibility intervals; PH k = [Pa, Pb]; Pa = P1 + (k - 1) * g; Pb = P1 + k * g; Pa is the left endpoint of the energy efficiency compatibility interval, and Pb is the right endpoint of the energy efficiency compatibility interval.

[0048] In this embodiment, first, find the corresponding panel energy efficiency data from the previously established weather sample set. These energy efficiency data reflect relevant indicators such as the working efficiency of the panel under different weather conditions. For example, the weather sample set contains data for different weather conditions such as sunny and cloudy, and correspondingly, there are energy efficiency performance data of the panel under these weather conditions.

[0049] Generate corresponding energy efficiency reference values by combining all the panel energy efficiency data, and sort the energy efficiency reference values to generate the energy efficiency reference value set P, P, P = {P1, P2…P j …P m }; Comprehensively process all the obtained panel energy efficiency data to obtain energy efficiency reference values. These energy efficiency reference values can be obtained through a certain calculation or directly using specific energy efficiency indicators of the panel. Then sort these energy efficiency reference values in ascending order to form the energy efficiency reference value set P. Among them, P1 is the minimum value in this set, Pm is the maximum value, Pj represents the energy efficiency reference value at the j-th sequence position, and m represents the total number of energy efficiency reference values. The purpose of this step is to have an overall and ordered description of the energy efficiency situation of the panel for subsequent operations such as interval division.

[0050] Set a compatibility interval g based on the set P of energy efficiency reference values; Calculate the average value of the set P of energy efficiency reference values. This average value can reflect the overall level of the energy efficiency of the solar panels. Then, set the compatibility interval g based on this average value. The role of the compatibility interval g is to determine the step size for dividing the energy efficiency reference value range. For example, if the average value is large, the compatibility interval g may be set relatively large, and vice versa. It is determined according to the distribution characteristics of the solar panel energy efficiency data and the requirements of subsequent analysis.

[0051] Determine the number b of energy efficiency compatibility intervals through the compatibility interval g and the set P of energy efficiency reference values. This number b is related to the range of the energy efficiency reference values (from P1 to P m ) and the compatibility interval g. Since b < m, the number of generated energy efficiency compatibility intervals is less than the total number of energy efficiency reference values, so that the energy efficiency reference values can be divided into several meaningful intervals.

[0052] Use the determined compatibility interval g and the number b of energy efficiency compatibility intervals to divide the set P of energy efficiency reference values to obtain the set P of energy efficiency compatibility intervals H . For each energy efficiency compatibility interval PH k (where k ranges from 1 to b), its left endpoint Pa and right endpoint Pb are calculated through specific formulas. Among them, Pa = P1+(k - 1)*g, indicating that the left endpoint of the kth interval starts from the minimum value P1 and increases by a multiple of the compatibility interval g (k - 1 times); Pb = P1 + k*g, indicating that the right endpoint starts from the minimum value P1 and is obtained by k times the compatibility interval g. In this way, the set P of energy efficiency reference values is divided into multiple energy efficiency compatibility intervals, each interval has a clear range, which helps to further analyze the performance of the solar panels in different energy efficiency intervals and the relationship with environmental factors, etc.

[0053] Example 5: When generating the first-level weather data, it includes: Generate the first-level weather data based on one or more weather sample sets corresponding to the energy efficiency compatibility intervals; The first-level weather data is a set of environmental reference value intervals; Obtain the set R of environmental reference values in the current first-level weather data, and calculate the correlation between each environmental reference value in the set R of environmental reference values and the energy efficiency reference value to obtain the first correlation reference value; Compare the first correlation reference value with the preset correlation reference value, and eliminate the environmental types corresponding to the environmental reference values lower than the preset correlation reference value to generate the second set R2 of environmental reference values of the current first-level weather data; Take the union of the environmental reference values of the environmental types in the second environmental reference value set R2 corresponding to the first-level weather data to generate the environmental reference value interval of the current environmental type; Combine the environmental reference value intervals of all environmental types to generate an environmental reference value interval set.

[0054] In this embodiment, each energy efficiency compatibility interval is associated with a specific weather sample set. These weather sample sets contain various environmental data (such as temperature, humidity, light intensity, etc.). By integrating and processing one or more weather sample sets related to the energy efficiency compatibility interval, the first-level weather data is generated. Since the first-level weather data is a set of environmental reference value intervals, it means that it is a data set representing environmental data in intervals according to certain rules. For example, for temperature environmental data, an interval such as [10 - 20°C] may be formed, and multiple such intervals together constitute the first-level weather data.

[0055] Obtain the environmental reference value set R in the current first-level weather data, and calculate the correlation between each environmental reference value in the environmental reference value set R and the energy efficiency reference value to obtain the first correlation reference value; Extract the environmental reference value set R from the already generated first-level weather data. The environmental reference value set R here contains reference values of various environmental types. Then calculate the correlation between each environmental reference value in the set R and the energy efficiency reference value. This correlation can be calculated by a specific mathematical method or algorithm. For example, it can be a certain correlation function obtained based on historical data statistical analysis. The calculated result is the first correlation reference value, which reflects the influence degree of each environmental reference value on the energy efficiency of the solar panel.

[0056] Compare the first correlation reference value with the preset correlation reference value, and eliminate the environmental types corresponding to the environmental reference values lower than the preset correlation reference value to generate the second environmental reference value set R2 of the current first-level weather data; Set a preset correlation reference value, which is determined according to experience or the requirements of solar panel energy efficiency analysis. Compare the calculated first correlation reference value with the preset correlation reference value. If the first correlation reference value of an environmental reference value is lower than the preset value, it means that the environmental type corresponding to this environmental reference value has a small or no relevant influence on the energy efficiency of the solar panel. So eliminate these environmental types, and the remaining environmental reference values form the second environmental reference value set R2. The purpose of doing this is to screen out the environmental types that have an important impact on the energy efficiency of the solar panel and reduce unnecessary data interference.

[0057] Take the union of the environmental reference values of the environmental types in the second environmental reference value set R2 corresponding to the first-level weather data to generate the environmental reference value interval of the current environmental type; For each environmental type in the second environmental reference value set R2, take the union of their environmental reference values. For example, for the two environmental types of temperature and humidity, if the environmental reference value range of temperature is [10 - 20°C] and the environmental reference value range of humidity is [30 - 60%], through the union operation, a new and more comprehensive environmental reference value range for the current environmental type (here considering temperature and humidity comprehensively) can be obtained. This range can better reflect the value range of environmental factors related to the energy efficiency of the solar panel.

[0058] Generate an environmental reference value range set by combining the environmental reference value ranges of all environmental types Combine the environmental reference value ranges of all environmental types (after the previous screening and processing) together to form an environmental reference value range set. This set comprehensively describes the value ranges of various environmental factors closely related to the energy efficiency of the solar panel, providing an important data basis for further analyzing the operating state of the solar panel, etc.

[0059] Example 6: When generating the operating state sample set, it includes: Obtain the primary weather data and the energy efficiency compatibility range corresponding to the current operating state of the photovoltaic panel; Generate an operating state sample by combining the current operating state of the photovoltaic panel, the primary weather data, and the energy efficiency compatibility range; Obtain the operating state samples of all operating state types to generate an operating state sample set.

[0060] In this embodiment, for each operating state of the photovoltaic panel, it is necessary to determine the corresponding primary weather data and energy efficiency compatibility range. The primary weather data is a set of environmental reference value ranges after screening and processing, which reflects the value range of environmental factors closely related to the energy efficiency of the panel. The energy efficiency compatibility range is the range divided from the energy efficiency reference value of the panel, reflecting the situation of the panel at different energy efficiency levels. Obtaining these data is to construct samples that can reflect the characteristics of the operating state.

[0061] When the operating state, primary weather data, and energy efficiency compatibility range are obtained, combine them together to form an operating state sample. This sample contains information about the operating state of the panel, as well as information about the related environmental factors (reflected by the primary weather data) and energy efficiency levels (reflected by the energy efficiency compatibility range).

[0062] For example, for the efficient operation state, the temperature range in the corresponding primary weather data may be [20 - 30°C], the light intensity range is [800 - 1000 W / m²], and the energy efficiency compatibility range is [80% - 90%]. Combining these pieces of information yields an operation state sample for the efficient operation state.

[0063] Repeat the operations of the previous two steps for all operation state types of the photovoltaic panel (such as normal, inefficient, efficient, etc.) to obtain the operation state samples corresponding to each operation state type. Then collect these operation state samples to form an operation state sample set. This sample set comprehensively describes the relationship between the operation state of the panel under different operation states, environmental factors, and energy efficiency levels, providing an important data basis for subsequent construction of an energy efficiency prediction model for the panel, etc.

[0064] Example 7: When generating the operation state of the current photovoltaic panel, it includes: The weather data at the currently monitored time node obtained; Based on the weather data, obtain the set R of environmental reference values at the currently monitored time node; Combine the set R of environmental reference values and the set of environmental reference value ranges to determine the corresponding primary weather data; Obtain the energy efficiency data of the panel at the currently monitored time node and generate a real-time energy efficiency reference value Ps; Determine the operation state of the photovoltaic panel at the currently monitored time node by comparing the real-time energy efficiency reference value and the primary weather data with the operation state sample set.

[0065] In this embodiment, at the current moment (the monitored time node), weather data is obtained through relevant meteorological sensors or data acquisition devices. This weather data contains the values of various environmental factors such as temperature, humidity, light intensity, etc., which are one of the basic data for determining the operation state of the panel. For example, a temperature sensor can measure the current environmental temperature, and a light intensity sensor can measure the light intensity value, etc.

[0066] According to the previously set rules for quantifying weather data, convert the weather data at the currently monitored time node obtained into the set R of environmental reference values. This process is similar to classifying the actually measured values according to a certain range or standard. For example, divide the temperature values into different temperature ranges to obtain the corresponding reference values, and convert the humidity values into the corresponding reference values according to a certain percentage range, etc.

[0067] The set of environmental reference value intervals was obtained through previous processing and contains the reference value intervals of environmental factors related to the energy efficiency of the solar panel. The set of environmental reference values R at the current monitoring time node is matched or compared with this set of environmental reference value intervals to determine the corresponding primary weather data. The primary weather data is a set of environmental reference value intervals that have been screened and optimized. Determining it helps to further analyze the operating state of the solar panel in the current environment.

[0068] At the current monitoring time node, measure the energy efficiency data of the photovoltaic panel, such as the power generation efficiency of the panel. Then, according to a certain calculation or conversion method, convert these energy efficiency data into the real-time energy efficiency reference value Ps. This real-time energy efficiency reference value can reflect the working efficiency of the panel at the current moment.

[0069] The operating state sample set contains information related to the solar panel under different operating states, such as the operating state, primary weather data, energy efficiency compatibility intervals, etc. Compare the current real-time energy efficiency reference value Ps and the determined primary weather data with the data in the operating state sample set. Similarity comparison, interval matching and other methods can be used to find the operating state corresponding to the sample most similar to the current situation, so as to determine the operating state of the photovoltaic panel at the current monitoring time node, such as judging whether the panel is in normal operation, low-efficiency operation or high-efficiency operation, etc.

[0070] Example 8: When generating the control instruction according to the current operating state of the photovoltaic panel, it includes: Set the occurrence degree evaluation model for each operating state based on historical data; Calculate the occurrence degree evaluation value mq of the current operating state based on the set of environmental reference values R of the photovoltaic panel at the current monitoring time node;

[0071] Among them, d1 represents the first fixed coefficient, d2 represents the second fixed coefficient, v represents the number of environmental types in the second set of environmental reference values R2, represents the environmental reference value of the xth type, c x represents the weight of the environmental reference value of the xth type, represents the real-time energy efficiency reference value; Generate multi-level control instructions by comparing the occurrence degree evaluation value mq of the current operating state with the preset evaluation value.

[0072] In this embodiment, by using the historical operation data of the photovoltaic panels, an occurrence degree evaluation model is established for each possible operation state (such as normal operation, inefficient operation, efficient operation, etc.). This model is constructed based on the data accumulated in the past, and it takes into account the influence of various factors on the occurrence degree of the operation state. For example, the historical data contains records of the operation states of the panels under different environmental conditions. By analyzing these data, the relationships between environmental factors, the performance of the panels themselves, etc. and the occurrence degree of the operation state can be found, and then the evaluation model can be constructed.

[0073] Embodiment 9: When generating the multi-level control instructions, it includes: Combining the current sub-historical weather data and the occurrence degree evaluation model to calculate the occurrence degree evaluation values at different stages of the current operation state; And setting corresponding preset evaluation values according to the occurrence degree evaluation values at different stages, including: the first preset evaluation value e1, the second preset evaluation value e2; If mq < e1, then generate a first-level control instruction; If e1 ≤ mq < e2, then generate a second-level control instruction; If e2 ≤ mq, then generate a third-level control instruction.

[0074] In this embodiment, if mq < e1, then a first-level control instruction is generated. This level of control instruction may be to make a preliminary adjustment to the panel, such as slightly adjusting the angle of the panel to improve the light reception, or issuing a low-level warning to indicate that there may be some factors slightly affecting the operation of the panel.

[0075] If e1 ≤ mq < e2, then a second-level control instruction is generated. This may involve more in-depth adjustments, such as checking some components of the panel or issuing a medium-level warning, indicating that the operation state of the panel has been affected to a certain extent and further attention is needed.

[0076] If e2 ≤ mq, then a third-level control instruction is generated. This may mean that a comprehensive inspection of the panel is required, such as checking the electrical connections of the panel, the performance of the cells, etc., or issuing a high-level warning, because at this time there may be relatively large problems with the operation state of the panel and timely treatment is needed to ensure the normal operation of the panel.

[0077] Finally, it should be noted that: Obviously, those skilled in the art can make various changes and deformations to the present invention without departing from the spirit and scope of the present invention. In this way, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and deformations.

[0078] The above are only the preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention.

Claims

1. A management method based on photovoltaic panel energy efficiency analysis, characterized in that: include: Establish a weather sample set based on the acquired historical weather data of the current area; Get the panel energy efficiency data corresponding to the current weather sample set; A solar panel energy efficiency prediction model is constructed by combining the weather sample set and the solar panel energy efficiency data corresponding to the weather sample set; The current operating status of the photovoltaic panel is generated by combining the weather data of the current monitoring time node and the panel energy efficiency prediction model, and the control instructions are generated according to the current operating status of the photovoltaic panel.

2. The management method based on photovoltaic panel energy efficiency analysis according to claim 1, characterized in that: The establishment of the weather sample set includes: Classify historical weather data based on weather types to generate multiple sub-historical weather data sets; Quantify each type of environmental data based on the current sub-historical weather data to generate a reference value for each type of environmental data; Obtain the reference values ​​of all types of environmental data in the current sub-historical weather data set to generate an environmental reference value set R, R = {R1, R2…R i …R n }; Among them, R i represents the reference value of the i-th type of environmental data in the current sub-historical weather data set, and n represents the total number of environmental data types in the current sub-historical weather data set; Combine all sub-historical weather data sets and the corresponding environmental reference value set R to generate a weather sample set for the current area.

3. The management method based on photovoltaic panel energy efficiency analysis according to claim 2, characterized in that: The construction of the solar panel energy efficiency prediction model includes: Get the panel energy efficiency data corresponding to the current weather sample set; The energy efficiency compatibility interval is set based on the energy efficiency data and the environmental reference value set R; Combined with the energy efficiency compatibility interval, the current sub-historical weather data set is optimized to generate the first-level weather data; Obtain various operating states of photovoltaic panels based on historical operating data of photovoltaic panels; Obtain the first-level weather data corresponding to each operating state of the photovoltaic panel to generate an operating state sample set; A solar panel energy efficiency prediction model is constructed based on the operating status sample set.

4. The management method based on photovoltaic panel energy efficiency analysis according to claim 3, characterized in that: The energy efficiency compatibility interval is set by combining the energy efficiency data and the environmental reference value set R, including: Get the panel energy efficiency data corresponding to the current weather sample set; Combine all the panel energy efficiency data to generate the corresponding energy efficiency reference value, and sort the energy efficiency reference values ​​to generate an energy efficiency reference value set P, P = {P1, P2…P j …P m }; Among them, P1 represents the minimum reference value of energy efficiency, P m represents the maximum value of the energy efficiency reference value; Pj represents the energy efficiency reference value of the jth sequence, and m represents the total number of energy efficiency reference values; Setting a compatible interval g based on the energy efficiency reference value set P; in, is the average value of the energy efficiency reference value set P; Combine the compatible interval g and the energy efficiency reference value set P to generate the energy efficiency compatible interval number b; Combine the compatible interval g and the number of energy efficiency compatible intervals b to divide the energy efficiency reference value set P to generate the energy efficiency compatible interval set PH, PH = {PH1, PH2…PH k …PH b }; Among them, b <m,PH k represents the kth energy efficiency compatibility interval of the energy efficiency reference value set P; b represents the total number of energy efficiency compatibility intervals; PH k =[Pa,Pb]; Pa=P1+(k-1)*g; Pb=P1+k*g; Pa is the left endpoint of the energy efficiency compatibility interval, and Pb is the right endpoint of the energy efficiency compatibility interval.

5. The management method based on photovoltaic panel energy efficiency analysis according to claim 4, characterized in that: The generation of the first-level weather data includes: Generate first-level weather data based on one or more weather sample sets corresponding to the energy efficiency compatibility interval; The first-level weather data is a set of environmental reference value intervals; Obtain an environmental reference value set R in the current first-level weather data, and calculate the correlation between each environmental reference value in the environmental reference value set R and the energy efficiency reference value to obtain a first correlation reference value; Compare the first correlation reference value with the preset correlation reference value, eliminate the environment types corresponding to the environment reference values ​​lower than the preset correlation reference value, and generate a second environment reference value set R2 of the current first-level weather data; Taking a union of the environmental reference values ​​of the environmental type in the second environmental reference value set R2 corresponding to the first-level weather data, and generating an environmental reference value interval of the current environmental type; The environmental reference value intervals of all environmental types are combined to generate an environmental reference value interval set.

6. The management method based on photovoltaic panel energy efficiency analysis according to claim 5, characterized in that: The generating of the running status sample set includes: Obtain the first-level weather data and energy efficiency compatibility range corresponding to the current operating status of the photovoltaic panel; Generate an operating status sample by combining the current operating status of the photovoltaic panel, the first-level weather data, and the energy efficiency compatibility interval; The running status samples of all running status types are obtained to generate a running status sample set.

7. The management method based on photovoltaic panel energy efficiency analysis according to claim 6, characterized in that: When generating the operating state of the current photovoltaic panel, it includes: Weather data at the current monitoring time node obtained; Based on the weather data, obtaining the set R of environmental reference values at the current monitoring time node; Combining the set R of environmental reference values and the set of environmental reference value intervals to determine the corresponding primary weather data; Obtaining the panel energy efficiency data at the current monitoring time node and generating the real-time energy efficiency reference value Ps; Determining the operating state of the photovoltaic panel at the current monitoring time node by comparing the real-time energy efficiency reference value and the primary weather data with the operating state sample set.

8. The management method based on photovoltaic panel energy efficiency analysis according to claim 7, characterized in that: When generating the control instruction according to the operating state of the current photovoltaic panel, it includes: Based on historical data, setting the occurrence degree evaluation model for each operating state; Calculating the occurrence degree evaluation value mq of the current operating state based on the set R of environmental reference values of the photovoltaic panel at the current monitoring time node; Wherein, d1 represents the first fixed coefficient, d2 represents the second fixed coefficient, v represents the number of environment types in the second environment reference value set R2, Indicates the environmental reference value of the xth type, c x represents the weight of the x-th type of environmental reference value, Indicates the real-time energy efficiency reference value; Generating multi-level control instructions by comparing the occurrence degree evaluation value mq of the current operating state with the preset evaluation value.

9. The management method based on photovoltaic panel energy efficiency analysis according to claim 8, characterized in that: When generating the multi-level control instructions, it includes: Combining the current sub-historical weather data and the occurrence degree evaluation model to calculate the occurrence degree evaluation values at different stages of the current operating state; And setting the corresponding preset evaluation values according to the occurrence degree evaluation values at different stages, including: the first preset evaluation value e1, the second preset evaluation value e2; If mq < e1, generating a first-level control instruction; If e1 ≤ mq < e2, generating a second-level control instruction; If e2 ≤ mq, generating a third-level control instruction.

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