Performance evaluation method and device for photovoltaic station, storage medium and electronic device

Abstract

The invention discloses a performance evaluation method for a photovoltaic station, and the method comprises the steps: determining a plurality of evaluation indexes corresponding to the photovoltaic station, and obtaining the index data of the plurality of evaluation indexes under at least two photovoltaic stations; performing correlation analysis on the plurality of evaluation indexes based on the index data to obtain a correlation analysis result, and determining weights of the plurality of evaluation indexes according to the correlation analysis result; the weight is used for representing the influence degree of the evaluation index on the performance of the photovoltaic station; aiming at any one evaluation index in the plurality of evaluation indexes, determining multi-dimensional parameters of the aiming evaluation index under the at least two photovoltaic stations; wherein the multi-dimensional parameters are used for representing the uncertainty of the targeted evaluation indexes; and performing performance evaluation on the at least two photovoltaic stations based on the weights and the multi-dimensional parameters of the plurality of evaluation indexes. The problem of low evaluation accuracy caused by various evaluation standards in the performance evaluation process of the photovoltaic station in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic technology, and more specifically, to a performance evaluation device, storage medium, and electronic device for a photovoltaic station. Background Art

[0002] As global warming and extreme weather phenomena are intensifying, the energy and power supply system based on the three major renewable energy sources of hydropower, wind power and photovoltaic power generation is accelerating. Among them, photovoltaic (PV) power generation, as a clean energy technology, has been widely used.

[0003] Photovoltaic station projects have the characteristics of large investment, long construction period, high technical requirements, stable returns, and great policy influence. It is necessary to comprehensively consider multiple factors such as component performance, project cost, environmental impact, policy support, etc. Therefore, reasonable photovoltaic stations are needed to conduct performance evaluation to provide decision makers with accurate project evaluation and priority determination.

[0004] At present, in the process of photovoltaic station project evaluation, the expert scoring method is widely used in the evaluation. However, there are the following problems in the evaluation of photovoltaic station projects: due to the influence of subjective factors such as the personal ability of experts, the evaluation results are prone to deviations.

[0005] Therefore, how to improve the accuracy of performance evaluation of photovoltaic stations has become an urgent problem to be solved. Summary of the invention

[0006] The embodiment of the present application provides a performance evaluation method for a photovoltaic station, so as to at least solve the problem in the related art that deviations in evaluation results are prone to occur during the evaluation process, resulting in low accuracy in the performance evaluation of the photovoltaic station.

[0007] According to an embodiment of the embodiments of the present application, a performance evaluation method for a photovoltaic station is provided, comprising: determining multiple evaluation indicators corresponding to the photovoltaic station, and obtaining indicator data of the multiple evaluation indicators under at least two of the photovoltaic stations; based on the indicator data, performing correlation analysis on the multiple evaluation indicators to obtain correlation analysis results, and determining weights of the multiple evaluation indicators according to the correlation analysis results; the weights are used to characterize the degree of influence of the evaluation indicators on the performance of the photovoltaic station; for any one of the multiple evaluation indicators, determining multidimensional parameters of the evaluation indicator under the at least two photovoltaic stations; wherein the multidimensional parameters are used to characterize the uncertainty of the evaluation indicator; based on the weights and multidimensional parameters of the multiple evaluation indicators, performing performance evaluation on the at least two photovoltaic stations.

[0008] In an exemplary embodiment, the correlation analysis is performed on the multiple evaluation indicators based on the indicator data to obtain the correlation analysis result, including: determining a first evaluation indicator and a second evaluation indicator from the multiple evaluation indicators, and forming multiple indicator combinations based on the first evaluation indicator and the second evaluation indicator; based on the indicator data, performing a correlation analysis on the first evaluation indicator and the second evaluation indicator in any indicator combination to determine the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the indicator combination; and determining the correlation analysis result based on the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the multiple indicator combinations.

[0009] In an exemplary embodiment, based on the indicator data, a correlation analysis is performed on the first evaluation indicator and the second evaluation indicator in any indicator combination to determine the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the indicator combination, including: based on the indicator data, determining a first evaluation parameter and a second evaluation parameter between the first evaluation indicator and the second evaluation indicator in the indicator combination; wherein the first evaluation parameter is used to represent the degree of synchronization between the first evaluation indicator and the second evaluation indicator; the second evaluation parameter is used to represent the degree of discreteness between the first evaluation indicator and the second evaluation indicator; and determining the quotient of the first evaluation parameter and the second evaluation parameter as the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the indicator combination.

[0010] In an exemplary embodiment, the indicator data is an observed score corresponding to the evaluation indicator; the first evaluation parameter and the second evaluation parameter between the first evaluation indicator and the second evaluation indicator in the targeted indicator combination are determined based on the indicator data, including: extracting target indicator data corresponding to the targeted indicator combination from the indicator data, the target indicator data including: the observed scores corresponding to the first evaluation indicator in the targeted indicator combination under the at least two photovoltaic stations, the observed scores corresponding to the second evaluation indicator under the at least two photovoltaic stations, the average score of the first evaluation indicator under the at least two photovoltaic stations, and the average score of the second evaluation indicator under the at least two photovoltaic stations; according to the target indicator data, the first evaluation parameter and the second evaluation parameter corresponding to the first evaluation indicator and the second evaluation indicator in the targeted indicator combination are determined.

[0011] In an exemplary embodiment, the correlation analysis result includes the correlation coefficients between the multiple evaluation indicators; the weights of the multiple evaluation indicators are determined according to the correlation analysis result, including: the weight of any evaluation indicator is determined by the following steps: determining the observed score of the evaluation indicator under the at least two photovoltaic sites and the average score of the evaluation indicator under the at least two photovoltaic sites; determining the score standard deviation of the evaluation indicator under the at least two photovoltaic sites based on the observed score and the average score; determining the weight of the evaluation indicator based on the correlation coefficient and the score standard deviation corresponding to the evaluation indicator.

[0012] In an exemplary embodiment, the multidimensional parameters include the truth, falsehood and uncertainty of the evaluation index under the at least two photovoltaic stations; the determination of the multidimensional parameters of the evaluation index under the at least two photovoltaic stations for any one of the multiple evaluation indexes includes: the determination of the multidimensional parameters for any one of the evaluation indexes is performed by the following steps: obtaining the observed score and score standard deviation of the evaluation index under the at least two photovoltaic stations; performing normalization processing based on the observed score to obtain the truth of the evaluation index under the at least two photovoltaic stations, and performing normalization processing based on the score standard deviation to obtain the uncertainty of the evaluation index under the at least two photovoltaic stations; determining the falsehood of the evaluation index under the at least two photovoltaic stations based on the truth of the evaluation index.

[0013] In an exemplary embodiment, the performance evaluation of the at least two photovoltaic stations is performed based on the weights of the multiple evaluation indicators and the multi-dimensional parameters, including: the performance evaluation of any one photovoltaic station is performed by the following steps: weighting the multiple evaluation indicators based on the weights and the multi-dimensional parameters of the multiple evaluation indicators under the targeted photovoltaic station to obtain an evaluation score for the targeted photovoltaic station; and performing a performance evaluation on the targeted photovoltaic station according to the evaluation score.

[0014] According to another embodiment of the embodiment of the present application, a performance evaluation device for a photovoltaic station is also provided, including: an acquisition module, used to determine multiple evaluation indicators corresponding to the photovoltaic station, and obtain indicator data of the multiple evaluation indicators under at least two of the photovoltaic stations; an analysis module, used to perform correlation analysis on the multiple evaluation indicators based on the indicator data, obtain correlation analysis results, and determine weights of the multiple evaluation indicators according to the correlation analysis results; the weights are used to characterize the degree of influence of the evaluation indicators on the performance of the photovoltaic station; a processing module, used to determine, for any one of the multiple evaluation indicators, multidimensional parameters of the evaluation indicator under the at least two photovoltaic stations; wherein the multidimensional parameters are used to characterize the uncertainty of the evaluation indicator; an evaluation module, used to perform performance evaluation on the at least two photovoltaic stations based on the weights and multidimensional parameters of the multiple evaluation indicators.

[0015] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above method when running.

[0016] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method through the computer program.

[0017] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0018] In an embodiment of the present application, by determining multiple evaluation indicators corresponding to a photovoltaic station and obtaining indicator data of the multiple evaluation indicators under at least two photovoltaic stations, it is possible to determine the weights corresponding to the multiple evaluation indicators based on the indicator data, and further determine the multidimensional parameters that characterize the uncertainty of the evaluation indicators. By combining the weights and multidimensional parameters of multiple evaluation indicators, the uncertainty of the photovoltaic station is evaluated, which effectively improves the evaluation accuracy. By comprehensively evaluating multiple evaluation indicators of the photovoltaic station, decision makers can be assisted in making more reasonable decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1It is a hardware structure block diagram of a computer terminal of a photovoltaic station performance evaluation method according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of a performance evaluation method for a photovoltaic station according to an embodiment of the present application;

[0022] Figure 3 is a flowchart of a performance evaluation method for a photovoltaic station according to an embodiment of the present application;

[0023] Figure 4 It is a structural block diagram of a performance evaluation device for a photovoltaic station according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices; "plurality" means two or more.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device or cloud platform or independent physical server or software platform, wherein the above software platform is run through one or more servers. Taking running on a computer terminal as an example, Figure 1 is a hardware structure diagram of a photovoltaic station performance evaluation method according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1Only one is shown in the figure) processor 102 and memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the methods in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, the above-mentioned methods are implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a performance evaluation method for a photovoltaic station is provided, which is applied to the above-mentioned computer terminal. Figure 2 : is a flow chart of a method for evaluating the performance of a photovoltaic station according to an embodiment of the present application, the process comprising the following steps:

[0030] Step S202, determining a plurality of evaluation indicators corresponding to the photovoltaic station, and obtaining indicator data of the plurality of evaluation indicators under at least two of the photovoltaic stations;

[0031] The evaluation index is a standard or index used to measure and evaluate the specific attributes or performance of a photovoltaic station. The evaluation index may be the same for different photovoltaic stations, and the index data of the evaluation index under the photovoltaic station may be different.

[0032] For PV sites, evaluation indicators may include the availability of solar energy resources, including solar radiation intensity and sunshine time; land availability, considering accessibility to the grid, land availability, terrain, shadows and environmental issues; PV panel technology, including the efficiency, durability and cost-effectiveness of monocrystalline, polycrystalline and thin-film solar panels; system capacity and design, determining the capacity and design of PV sites based on energy demand and available land area, including the number of installed PV panels, orientation, inverter power and the quality of interconnection infrastructure; energy conversion efficiency, including the efficiency and performance of PV panels, inverters and system balance equipment; financial factors, including initial investment cost, operating cost and maintenance cost; environmental factors, including impact on local ecosystems, land use and potential environmental regulations; policy and regulatory support, including incentive policies and regulations for new energy; and the impact of the adoption of new technologies on system efficiency and cost.

[0033] The indicator data is the observation score corresponding to the evaluation indicator. For different evaluation indicators, the corresponding observation scores may be different. For the same evaluation indicator, the observation scores under different photovoltaic stations may be the same or different.

[0034] Step S204: Based on the indicator data, a correlation analysis is performed on the multiple evaluation indicators to obtain a correlation analysis result, and weights of the multiple evaluation indicators are determined according to the correlation analysis result; the weights are used to characterize the degree of influence of the evaluation indicators on the performance of the photovoltaic station;

[0035] It can be understood that correlation analysis refers to determining the relationship and degree of connection between multiple evaluation indicators. In the process of performing correlation analysis on multiple evaluation indicators based on indicator data, the indicator data can be normalized and correlation analysis can be carried out according to the normalized indicator data.

[0036] Step S206, for any one of the multiple evaluation indicators, determining a multi-dimensional parameter of the evaluation indicator under the at least two photovoltaic stations; wherein the multi-dimensional parameter is used to characterize the uncertainty of the evaluation indicator;

[0037] Specifically, the multi-dimensional parameters can be determined based on Neutrosophic Sets. For any evaluation indicator, its multi-dimensional parameters under the photovoltaic station can be determined. When there are multiple photovoltaic stations, the multi-dimensional parameters corresponding to the multiple photovoltaic stations can be determined. By determining the uncertainty of the evaluation indicators under the photovoltaic station, better decision-making and reasoning can be made to better deal with complex problems and situations such as diverse evaluation standards.

[0038] Step S208: performing performance evaluation on the at least two photovoltaic stations based on the weights of the multiple evaluation indicators and the multi-dimensional parameters.

[0039] The computer terminal performs performance evaluation on at least two photovoltaic stations by weighting the weights of multiple evaluation indicators and multi-dimensional parameters.

[0040] In an exemplary embodiment, the performance evaluation of the at least two photovoltaic stations is performed based on the weights of the multiple evaluation indicators and the multi-dimensional parameters, including: the performance evaluation of any one photovoltaic station is performed by the following steps: weighting the multiple evaluation indicators based on the weights and the multi-dimensional parameters of the multiple evaluation indicators under the photovoltaic station to obtain an evaluation score for the photovoltaic station; and performing a performance evaluation on the photovoltaic station according to the evaluation score.

[0041] It should be noted that for the performance evaluation of any photovoltaic site, the same steps can be used to calculate the evaluation score, that is, based on the weights of multiple evaluation indicators, the evaluation scores are weighted with the multi-dimensional parameters of the multiple evaluation indicators. For example, for any photovoltaic site, its evaluation indicators include evaluation indicator 1, evaluation indicator 2...evaluation indicator N. For evaluation indicator 1, its multi-dimensional parameter is A and its weight is a; for evaluation indicator 2, its multi-dimensional parameter is B and its weight is b; for evaluation indicator N, its multi-dimensional parameter is N and its weight is n. When calculating the rating score of the photovoltaic site, the evaluation score = A*a+B*b...+N*n.

[0042] In some embodiments, after obtaining the evaluation score, the computer terminal may compare the obtained evaluation score with a set score threshold, and when the evaluation score reaches the score threshold, it is determined that the performance of the photovoltaic station is good. When the evaluation score does not reach the score threshold, it is determined that the performance of the photovoltaic station is average.

[0043] In an embodiment of the present application, by determining multiple evaluation indicators corresponding to a photovoltaic station and obtaining indicator data of the multiple evaluation indicators under at least two photovoltaic stations, it is possible to determine the weights corresponding to the multiple evaluation indicators based on the indicator data, and further determine the multidimensional parameters that characterize the uncertainty of the evaluation indicators. By combining the weights and multidimensional parameters of multiple evaluation indicators, the uncertainty of the photovoltaic station is evaluated, which effectively improves the evaluation accuracy. By comprehensively evaluating multiple evaluation indicators of the photovoltaic station, decision makers can be assisted in making more reasonable decisions.

[0044] In an exemplary embodiment, the correlation analysis is performed on the multiple evaluation indicators based on the indicator data to obtain the correlation analysis result, including: determining a first evaluation indicator and a second evaluation indicator from the multiple evaluation indicators, and forming multiple indicator combinations based on the first evaluation indicator and the second evaluation indicator; based on the indicator data, performing a correlation analysis on the first evaluation indicator and the second evaluation indicator in any indicator combination to determine the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the indicator combination; and determining the correlation analysis result based on the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the multiple indicator combinations.

[0045] It can be understood that when performing correlation analysis on multiple evaluation indicators, pairs of the multiple evaluation indicators can be combined. During the combination process, one of the evaluation indicators can be determined as the first evaluation indicator, and the other evaluation indicator can be determined as the second evaluation indicator to form multiple indicator combinations.

[0046] Specifically, for the first evaluation indicator and the second evaluation indicator in the indicator combination, the computer terminal can calculate the correlation coefficient between the two, and determine the correlation analysis result through the correlation coefficient.

[0047] In the above embodiment, the correlation coefficients are calculated for the first evaluation indicator and the second evaluation indicator in the indicator combination respectively, and the correlation coefficients between any two evaluation indicators in the multiple evaluation indicators are obtained, and then the weight calculation can be carried out.

[0048] In an exemplary embodiment, based on the indicator data, a correlation analysis is performed on the first evaluation indicator and the second evaluation indicator in any indicator combination to determine the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the indicator combination, including: based on the indicator data, determining a first evaluation parameter and a second evaluation parameter between the first evaluation indicator and the second evaluation indicator in the indicator combination; wherein the first evaluation parameter is used to represent the degree of synchronization between the first evaluation indicator and the second evaluation indicator; the second evaluation parameter is used to represent the degree of discreteness between the first evaluation indicator and the second evaluation indicator; and determining the quotient of the first evaluation parameter and the second evaluation parameter as the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the indicator combination.

[0049] In an exemplary embodiment, the indicator data is an observed score corresponding to the evaluation indicator; the first evaluation parameter and the second evaluation parameter between the first evaluation indicator and the second evaluation indicator in the targeted indicator combination are determined based on the indicator data, including: extracting target indicator data corresponding to the targeted indicator combination from the indicator data, the target indicator data including: the observed scores corresponding to the first evaluation indicator in the targeted indicator combination under the at least two photovoltaic stations, the observed scores corresponding to the second evaluation indicator under the at least two photovoltaic stations, the average score of the first evaluation indicator under the at least two photovoltaic stations, and the average score of the second evaluation indicator under the at least two photovoltaic stations; according to the target indicator data, the first evaluation parameter and the second evaluation parameter corresponding to the first evaluation indicator and the second evaluation indicator in the targeted indicator combination are determined.

[0050] Specifically, refer to the following formula, which is a formula for calculating the correlation coefficient:

[0051]

[0052] Wherein, CCjt refers to the correlation coefficient, j and t refer to the evaluation index, j, t = 1, 2, 3, ..., n. i refers to the photovoltaic station, i = 1, 2, 3, ..., m. The numerator of the formula of the correlation coefficient is the first evaluation parameter, and the denominator of the formula of the correlation coefficient is the second evaluation parameter.

[0053] Combined with the formula of the correlation coefficient, the calculation process of the correlation coefficient of any two evaluation indicators under one photovoltaic station is explained, j can refer to the first evaluation indicator, and t can refer to the second evaluation indicator. ij Refers to the observation score corresponding to the first evaluation indicator at one of the photovoltaic stations (if i=1, it can be the first photovoltaic station); Refers to the average score of the first evaluation indicator under at least two photovoltaic sites; f it Refers to the observation score corresponding to the second evaluation indicator at one of the photovoltaic stations; Refers to the average score of the second evaluation indicator under at least two PV stations.

[0054] It is understandable that when there are multiple photovoltaic stations, the computer terminal needs to obtain the observation scores corresponding to the first evaluation indicator under multiple photovoltaic stations, the observation scores corresponding to the second evaluation indicator under multiple photovoltaic stations, the average score of the first evaluation indicator under multiple photovoltaic stations, and the average score of the second evaluation indicator under multiple photovoltaic stations.

[0055] It is understandable that, for the same evaluation indicator, correlation coefficients may be calculated between the same evaluation indicator and multiple other evaluation indicators other than itself.

[0056] In the above embodiment, by using the calculation formula of the correlation coefficient, the target indicator data extracted from the indicator data is brought into the calculation formula of the correlation coefficient, so that the correlation coefficient can be accurately calculated, which can improve the calculation accuracy of the subsequent weights.

[0057] In an exemplary embodiment, the correlation analysis result includes the correlation coefficient between the multiple evaluation indicators; the weights of the multiple evaluation indicators are determined according to the correlation analysis result, including: the weight of any evaluation indicator is determined by the following steps: determining the observed score of the evaluation indicator under the at least two photovoltaic sites and the average score of the evaluation indicator under the at least two photovoltaic sites; determining the score standard deviation of the evaluation indicator under the at least two photovoltaic sites based on the observed score and the average score; determining the weight of the evaluation indicator based on the correlation coefficient and the score standard deviation corresponding to the evaluation indicator.

[0058] When calculating the weight of the evaluation index, the computer terminal also needs to determine the scoring standard deviation of the evaluation index under at least two photovoltaic sites, and jointly calculate the weight of the evaluation index through the correlation coefficient and the scoring standard deviation.

[0059] Specifically, refer to the following formula, which is the calculation formula for the scoring standard deviation:

[0060]

[0061] Among them, SDF j Refers to the score standard deviation of the jth evaluation indicator, i represents a photovoltaic station. ij refers to the observed score of the jth evaluation indicator at one of the photovoltaic stations, Refers to the average score of the jth evaluation indicator under at least two PV stations. There are multiple PV stations. Therefore, when obtaining the observed score for calculating the score standard deviation, the observed score of the jth evaluation indicator under other PV stations should also be obtained.

[0062] In some embodiments, the weight calculation formula is as shown in the following formula:

[0063]

[0064] Among them, FW j Refers to the weight of the jth evaluation indicator. SDF j refers to the standard deviation of the score of the jth evaluation indicator, t represents the evaluation indicator, CC jt It represents the correlation coefficient between the jth evaluation indicator and the tth evaluation indicator. It can be understood that for the same evaluation indicator, it can have a correlation coefficient with multiple other evaluation indicators except itself.

[0065] In the above embodiment, by setting a weight formula and bringing the calculated weight of the evaluation index and the scoring standard deviation of the evaluation index into the set weight formula, the weight can be accurately calculated, which provides a basis for the subsequent performance evaluation of the photovoltaic station and improves the accuracy of the performance evaluation of the photovoltaic station.

[0066] In an exemplary embodiment, the multidimensional parameters include the truth, falsehood and uncertainty of the evaluation index under the at least two photovoltaic stations; the determination of the multidimensional parameters of the evaluation index under the at least two photovoltaic stations for any one of the multiple evaluation indexes includes: the determination of the multidimensional parameters for any one of the evaluation indexes is performed by the following steps: obtaining the observed score and score standard deviation of the evaluation index under the at least two photovoltaic stations; performing normalization processing based on the observed score to obtain the truth of the evaluation index under the at least two photovoltaic stations, and performing normalization processing based on the score standard deviation to obtain the uncertainty of the evaluation index under the at least two photovoltaic stations; determining the falsehood of the evaluation index under the at least two photovoltaic stations based on the truth of the evaluation index.

[0067] Specifically, the computer terminal can obtain the observed scores and score standard deviations of the evaluation indicators under at least two photovoltaic stations to determine the multi-dimensional parameters. The determination of the multi-dimensional parameters calculated by the computer terminal can refer to the following formula:

[0068]

[0069] in, It refers to the multi-dimensional parameters determined by the Kth decision maker for the jth evaluation indicator under the evaluation of the i-th photovoltaic station. is the truth value of standard j under the evaluation of PV station i by the kth decision maker, is the false value of standard j under the evaluation of PV station i by the kth decision maker, is the uncertainty of standard j in the evaluation of photovoltaic station i by the kth decision maker. For example, the decision maker's score for the availability of solar energy resources (i.e., the observation score) is 80. After normalizing the score, the true value is 0.8 and the false value is 1-0.8=0.2. The uncertainty is the normalization of the standard deviation of the score of the availability of solar energy resources, and the final result is between [0,1].

[0070] In order to better understand the process of the above-mentioned photovoltaic station performance evaluation method, the above-mentioned photovoltaic station performance evaluation method is described below in combination with an optional embodiment, but is not used to limit the technical solution of the embodiment of the present application.

[0071] Global warming and extreme weather phenomena are intensifying, prompting people to pay more attention to the design of renewable energy systems. Photovoltaic (PV) power generation, as a clean energy technology, has been widely used around the world. However, existing project evaluation methods have difficulty dealing with complex environments and diverse standards, resulting in uncertainty in the evaluation of photovoltaic sites in terms of performance, economic feasibility and environmental sustainability. Therefore, this application proposes a performance evaluation method for photovoltaic sites based on Neutrosophic Sets and CRiteria Importance Through Inter-criteria Correlation (CRITIC, determining the importance of criteria through inter-criteria correlation), which is used to comprehensively evaluate multiple criteria of photovoltaic sites and help decision makers make decisions.

[0072] First, the CRITIC method is used to calculate the weight of each criterion (i.e., evaluation index), and the Neutrosophic Sets are used to process uncertainty information (i.e., multi-dimensional parameters). Finally, the two are combined to obtain the score of the PV station. Figure 3 As shown in the figure, it is a flowchart for evaluating the performance of photovoltaic stations, which is mainly divided into the following four steps:

[0073] Step S301: Data collection. A questionnaire is designed to collect expert ratings for multiple criteria for evaluating photovoltaic sites. The criteria mainly include the availability of solar energy resources, including solar radiation intensity and sunshine time; land availability, considering accessibility to the power grid, land availability, terrain, shadows and environmental issues; photovoltaic panel technology, including the efficiency, durability and cost-effectiveness of monocrystalline, polycrystalline and thin-film solar panels; system capacity and design, determining the capacity and design of photovoltaic sites based on energy demand and available land area, including the number of installed photovoltaic panels, direction, inverter power and the quality of interconnection infrastructure; energy conversion efficiency, including the efficiency and performance of photovoltaic panels, inverters and system balance equipment; financial factors, including initial investment costs, operating costs and maintenance costs; environmental factors, including impacts on local ecosystems, land use and potential environmental regulations; policy and regulatory support, including incentive policies and regulations for new energy; the impact of the adoption of new technologies on system efficiency and cost.

[0074] Step S302: Determine weights. Specifically, the standard weights may be assigned by the CRITIC method. First, the correlation coefficients between the standards are calculated, and based on the correlation coefficients between the standards, the weights of the standards are calculated.

[0075] Step S303: Determine multi-dimensional parameters, including designing a neutrosophic matrix and constructing a single-valued neutrosophic set for each decision maker's observed score.

[0076] Step S304: Decision analysis: Combining the standard weights and the single-value neutrosophic set, a multi-standard weighted score for the photovoltaic station is obtained.

[0077] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0078] The present application also provides a structural block diagram of a performance evaluation device for a photovoltaic station. Figure 4 is a structural block diagram of a performance evaluation device for a photovoltaic station according to an embodiment of the present application; Figure 4 As shown, including:

[0079] An acquisition module 42 is used to determine a plurality of evaluation indicators corresponding to a photovoltaic station, and to acquire indicator data of the plurality of evaluation indicators under at least two of the photovoltaic stations;

[0080] An analysis module 44 is used to perform a correlation analysis on the multiple evaluation indicators based on the indicator data to obtain a correlation analysis result, and determine the weights of the multiple evaluation indicators according to the correlation analysis result; the weights are used to characterize the degree of influence of the evaluation indicators on the performance of the photovoltaic station;

[0081] The processing module 46 is used to determine, for any one of the multiple evaluation indicators, a multi-dimensional parameter of the evaluation indicator under the at least two photovoltaic stations; wherein the multi-dimensional parameter is used to characterize the uncertainty of the evaluation indicator;

[0082] The evaluation module 48 is used to perform performance evaluation on the at least two photovoltaic stations based on the weights of the multiple evaluation indicators and the multi-dimensional parameters.

[0083] In an exemplary embodiment, the analysis module 44 is also used to determine the first evaluation indicator and the second evaluation indicator from the multiple evaluation indicators, and to form multiple indicator combinations based on the first evaluation indicator and the second evaluation indicator; based on the indicator data, to perform a correlation analysis on the first evaluation indicator and the second evaluation indicator in any indicator combination, to determine the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the indicator combination; based on the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the multiple indicator combinations, to determine the correlation analysis result.

[0084] In an exemplary embodiment, the analysis module 44 is also used to determine, based on the indicator data, a first evaluation parameter and a second evaluation parameter between the first evaluation indicator and the second evaluation indicator in the targeted indicator combination; wherein the first evaluation parameter is used to indicate the degree of synchronization between the first evaluation indicator and the second evaluation indicator; the second evaluation parameter is used to indicate the degree of discreteness between the first evaluation indicator and the second evaluation indicator; and the quotient of the first evaluation parameter and the second evaluation parameter is determined as the correlation coefficient between the first evaluation indicator and the second evaluation indicator in the targeted indicator combination.

[0085] In an exemplary embodiment, the indicator data is an observed score corresponding to the evaluation indicator; the analysis module 44 is also used to extract target indicator data corresponding to the target indicator combination from the indicator data, and the target indicator data includes: the observed scores corresponding to the first evaluation indicator in the target indicator combination under the at least two photovoltaic stations, the observed scores corresponding to the second evaluation indicator under the at least two photovoltaic stations, the average score of the first evaluation indicator under the at least two photovoltaic stations, and the average score of the second evaluation indicator under the at least two photovoltaic stations; according to the target indicator data, determine the first evaluation parameter and the second evaluation parameter corresponding to the first evaluation indicator and the second evaluation indicator in the target indicator combination.

[0086] In an exemplary embodiment, the analysis module 44 is also used to determine the observed scores of the evaluation indicators under the at least two photovoltaic stations and the average scores of the evaluation indicators under the at least two photovoltaic stations; determine the score standard deviation of the evaluation indicators under the at least two photovoltaic stations based on the observed scores and the average scores; determine the weight of the evaluation indicators based on the correlation coefficient and the score standard deviation corresponding to the evaluation indicators.

[0087] In an exemplary embodiment, the multidimensional parameters include the truth value, false value and uncertainty of the evaluation index under the at least two photovoltaic stations; the meteorological detection model includes an embedding layer, an associated learning layer and a forecasting layer; the processing module 66 is also used to obtain the observed score and score standard deviation of the evaluation index under the at least two photovoltaic stations; normalization processing is performed based on the observed score to obtain the truth value of the evaluation index under the at least two photovoltaic stations, and normalization processing is performed based on the score standard deviation to obtain the uncertainty of the evaluation index under the at least two photovoltaic stations; based on the truth value of the evaluation index, the false value of the evaluation index under the at least two photovoltaic stations is determined.

[0088] In an exemplary embodiment, the evaluation module 68 is also used to obtain an evaluation score of the photovoltaic station based on the weights of the multiple evaluation indicators and the multi-dimensional parameters of the multiple evaluation indicators under the photovoltaic station; and perform performance evaluation on the photovoltaic station according to the evaluation score.

[0089] Through the above-mentioned device, by determining multiple evaluation indicators corresponding to the photovoltaic station and obtaining indicator data of the multiple evaluation indicators under at least two photovoltaic stations, it is possible to determine the weights corresponding to the multiple evaluation indicators according to the indicator data, and further determine the multidimensional parameters that characterize the uncertainty of the evaluation indicators. By combining the weights and multidimensional parameters of the multiple evaluation indicators, the uncertainty of the photovoltaic station is evaluated, which effectively improves the evaluation accuracy. By comprehensively evaluating multiple evaluation indicators of the photovoltaic station, decision makers can be assisted in making more reasonable decisions.

[0090] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0091] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0092] S1, determining a plurality of evaluation indicators corresponding to a photovoltaic station, and obtaining indicator data of the plurality of evaluation indicators under at least two of the photovoltaic stations;

[0093] S2, based on the indicator data, performing a correlation analysis on the multiple evaluation indicators to obtain a correlation analysis result, and determining weights of the multiple evaluation indicators according to the correlation analysis result; the weights are used to characterize the degree of influence of the evaluation indicators on the performance of the photovoltaic station;

[0094] S3, for any one of the multiple evaluation indicators, determining a multi-dimensional parameter of the evaluation indicator under the at least two photovoltaic stations; wherein the multi-dimensional parameter is used to characterize the uncertainty of the evaluation indicator;

[0095] S4, performing performance evaluation on the at least two photovoltaic stations based on the weights of the multiple evaluation indicators and the multi-dimensional parameters.

[0096] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0097] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0098] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0099] S1, determining a plurality of evaluation indicators corresponding to a photovoltaic station, and obtaining indicator data of the plurality of evaluation indicators under at least two of the photovoltaic stations;

[0100] S2, based on the indicator data, performing a correlation analysis on the multiple evaluation indicators to obtain a correlation analysis result, and determining weights of the multiple evaluation indicators according to the correlation analysis result; the weights are used to characterize the degree of influence of the evaluation indicators on the performance of the photovoltaic station;

[0101] S3, for any one of the multiple evaluation indicators, determining a multi-dimensional parameter of the evaluation indicator under the at least two photovoltaic stations; wherein the multi-dimensional parameter is used to characterize the uncertainty of the evaluation indicator;

[0102] S4, performing performance evaluation on the at least two photovoltaic stations based on the weights of the multiple evaluation indicators and the multi-dimensional parameters.

[0103] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0104] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0105] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0106] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0107] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0108] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0109] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A performance evaluation method for a photovoltaic station, characterized in that: include: Determine a plurality of evaluation indicators corresponding to the photovoltaic station, and obtain indicator data of the plurality of evaluation indicators under at least two of the photovoltaic stations; Based on the indicator data, performing a correlation analysis on the multiple evaluation indicators to obtain a correlation analysis result, and determining the weights of the multiple evaluation indicators according to the correlation analysis result; The weight is used to characterize the degree of influence of the evaluation index on the performance of the photovoltaic station; For any one of the multiple evaluation indicators, determine a multi-dimensional parameter of the evaluation indicator under the at least two photovoltaic stations; wherein the multi-dimensional parameter is used to characterize the uncertainty of the evaluation indicator; Based on the weights of the multiple evaluation indicators and the multi-dimensional parameters, performance evaluation is performed on the at least two photovoltaic stations.

2. The method according to claim 1, characterized in that The step of performing correlation analysis on the multiple evaluation indicators based on the indicator data to obtain correlation analysis results includes: Determine a first evaluation indicator and a second evaluation indicator from the plurality of evaluation indicators, and form a plurality of indicator combinations based on the first evaluation indicator and the second evaluation indicator; Based on the indicator data, performing a correlation analysis on the first evaluation indicator and the second evaluation indicator in any indicator combination to determine a correlation coefficient between the first evaluation indicator and the second evaluation indicator in the indicator combination; A correlation analysis result is determined based on a correlation coefficient between the first evaluation indicator and the second evaluation indicator in the plurality of indicator combinations.

3. The method according to claim 2, characterized in that The step of performing a correlation analysis on the first evaluation indicator and the second evaluation indicator in any indicator combination based on the indicator data to determine a correlation coefficient between the first evaluation indicator and the second evaluation indicator in the indicator combination includes: Based on the indicator data, determine a first evaluation parameter and a second evaluation parameter between the first evaluation indicator and the second evaluation indicator in the indicator combination; wherein the first evaluation parameter is used to indicate the degree of synchronization between the first evaluation indicator and the second evaluation indicator; and the second evaluation parameter is used to indicate the degree of discreteness between the first evaluation indicator and the second evaluation indicator; A quotient of the first evaluation parameter and the second evaluation parameter is determined as a correlation coefficient between the first evaluation indicator and the second evaluation indicator in the target indicator combination.

4. The method according to claim 3, characterized in that The indicator data is the observation score corresponding to the evaluation indicator; The determining, based on the indicator data, a first evaluation parameter and a second evaluation parameter between the first evaluation indicator and the second evaluation indicator in the indicator combination, comprises: Extracting target indicator data corresponding to the target indicator combination from the indicator data, the target indicator data including: the observed scores corresponding to the first evaluation indicator in the target indicator combination under the at least two photovoltaic stations, the observed scores corresponding to the second evaluation indicator under the at least two photovoltaic stations, the average score of the first evaluation indicator under the at least two photovoltaic stations, and the average score of the second evaluation indicator under the at least two photovoltaic stations; According to the target indicator data, a first evaluation parameter and a second evaluation parameter corresponding to the first evaluation indicator and the second evaluation indicator in the targeted indicator combination are determined.

5. The method according to claim 1, characterized in that The correlation analysis result includes the correlation coefficients between the multiple evaluation indicators; and determining the weights of the multiple evaluation indicators according to the correlation analysis result includes: The weight of any evaluation indicator is determined by the following steps: Determine the observation scores of the evaluation index under the at least two photovoltaic stations and the average score of the evaluation index under the at least two photovoltaic stations; Determining the score standard deviation of the evaluation indicator under the at least two photovoltaic sites according to the observed score and the score mean; The weight of the evaluation indicator is determined according to the correlation coefficient and the scoring standard deviation corresponding to the evaluation indicator.

6. The method according to claim 1, characterized in that The multidimensional parameters include the truth value, false value and uncertainty of the evaluation index under the at least two photovoltaic stations; for any one of the multiple evaluation indicators, determining the multidimensional parameters of the evaluation index under the at least two photovoltaic stations includes: The determination of the multi-dimensional parameters of any evaluation indicator is carried out in the following steps: Obtaining the observed scores and score standard deviations of the evaluation indicators under the at least two photovoltaic stations; Performing normalization processing based on the observed scores to obtain the truth value of the evaluation index under the at least two photovoltaic stations, and performing normalization processing based on the score standard deviation to obtain the uncertainty of the evaluation index under the at least two photovoltaic stations; Based on the truth degree of the evaluation indicator, the falseness degree of the evaluation indicator under the at least two photovoltaic stations is determined.

7. The method according to claim 1, characterized in that The performing performance evaluation on the at least two photovoltaic stations based on the weights of the multiple evaluation indicators and the multi-dimensional parameters includes: The performance evaluation of any photovoltaic station is carried out in the following steps: Based on the weights of the multiple evaluation indicators and the multi-dimensional parameters of the multiple evaluation indicators under the targeted photovoltaic station, an evaluation score of the targeted photovoltaic station is obtained; According to the evaluation score, a performance evaluation is performed on the photovoltaic station.

8. A photovoltaic station performance evaluation device, characterized in that: include: An acquisition module, used to determine a plurality of evaluation indicators corresponding to a photovoltaic station, and to acquire indicator data of the plurality of evaluation indicators under at least two of the photovoltaic stations; An analysis module, configured to perform a correlation analysis on the multiple evaluation indicators based on the indicator data, obtain a correlation analysis result, and determine the weights of the multiple evaluation indicators according to the correlation analysis result; The weight is used to characterize the degree of influence of the evaluation index on the performance of the photovoltaic station; A processing module, for determining, for any one of the multiple evaluation indicators, a multi-dimensional parameter of the evaluation indicator under the at least two photovoltaic stations; wherein the multi-dimensional parameter is used to characterize the uncertainty of the evaluation indicator; An evaluation module is used to perform performance evaluation on the at least two photovoltaic stations based on the weights of the multiple evaluation indicators and the multi-dimensional parameters.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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