Photovoltaic market benefit prediction and evaluation method, system, equipment and medium

Abstract

The invention belongs to the technical field of energy, and discloses a photovoltaic market benefit prediction and evaluation method, system and device and a medium, and the method comprises the steps: obtaining photovoltaic power generation system parameters, economic data and meteorological data of a to-be-evaluated region, and calculating the theoretical power generation amount according to the photovoltaic power generation system parameters and the meteorological data; constructing a generating capacity prediction model of the to-be-evaluated area based on the theoretical generating capacity and the acquired data, and determining the predicted generating capacity of the to-be-evaluated area by using the generating capacity prediction model; according to the predicted generating capacity, income and cost data of the photovoltaic market project are calculated in combination with economic data, and benefits of the photovoltaic market are evaluated by using a net value method and a calculation result. According to the method, the diversity and comprehensiveness of data sources are ensured, the accuracy and reliability of prediction and evaluation are improved, and more powerful support is provided for decision making.

Description

Technical Field

[0001] The present invention relates to the field of energy technology, and in particular to a photovoltaic market benefit forecasting and evaluation method, system, equipment and medium. Background Art

[0002] In the development of the distributed photovoltaic market, accurate prediction and evaluation of market benefits are of great significance for investment decisions, project planning and policy formulation. However, traditional prediction and evaluation methods often have many shortcomings, such as a single data source, a simple prediction model, and failure to fully consider a variety of influencing factors, which leads to inaccurate prediction results and makes it difficult to truly reflect the actual benefits of the distributed photovoltaic market.

[0003] On the one hand, the performance of photovoltaic power generation systems is affected by many factors, including photovoltaic power generation system parameters such as installed capacity, component efficiency, system loss, and meteorological data such as annual average sunshine hours, solar irradiance, temperature, precipitation and dust conditions. Traditional prediction methods often only consider some factors and ignore other important factors, resulting in deviations in the prediction results. On the other hand, the economic benefits of the distributed photovoltaic market are also affected by many factors, such as initial investment costs, operation and maintenance costs, on-grid electricity prices, subsidy policies, etc. The interaction between these factors makes the prediction of market benefits complex and difficult. Traditional prediction methods often find it difficult to fully consider these factors, resulting in inaccurate prediction results and inability to provide strong support for decision-making.

[0004] Therefore, how to provide a photovoltaic market benefit prediction and evaluation method, system, equipment and medium is a problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present invention provide a photovoltaic market benefit forecasting and evaluation method, system, device and medium to solve the problem of inaccurate forecasting results in the prior art.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a photovoltaic market benefit forecasting and evaluation method is provided.

[0008] In one embodiment, the photovoltaic market benefit forecasting and evaluation method includes:

[0009] Obtain the photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated, and calculate the theoretical power generation based on the photovoltaic power generation system parameters and meteorological data;

[0010] Based on the theoretical power generation and the acquired data, a power generation prediction model for the area to be evaluated is constructed, and the predicted power generation of the area to be evaluated is determined using the power generation prediction model;

[0011] Based on the predicted power generation, combined with economic data, the revenue and cost data of photovoltaic market projects are calculated, and the benefits of the photovoltaic market are evaluated using the net value method and the calculation results.

[0012] In one embodiment, the step of obtaining photovoltaic power generation system parameters, economic data, and meteorological data of the area to be evaluated, and calculating theoretical power generation according to the photovoltaic power generation system parameters and meteorological data includes:

[0013] Obtaining the photovoltaic power generation system parameters and economic data of the area to be evaluated through the photovoltaic enterprise system, wherein the photovoltaic power generation system parameters include installed capacity, component efficiency and system loss, and the economic data include initial investment cost, operation and maintenance cost and electricity price data of the area to be evaluated;

[0014] Obtain meteorological data of the area to be evaluated through weather websites, including annual average sunshine hours, solar irradiance, temperature, precipitation and dust conditions;

[0015] Based on the outlier detection method, outlier and noise data are removed from the photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated; and the theoretical power generation is calculated based on the photovoltaic power generation system parameters and meteorological data;

[0016] Among them, the expression of the outlier detection method is:

[0017] when Then x j is the normal value;

[0018] when Then x j is an outlier;

[0019] In the formula, x j represents the jth data in any group of data, represents the mean of any group of data, and σ represents the standard deviation.

[0020] In one embodiment, the calculation formula of the theoretical power generation is:

[0021] E 理 =P 容 ×H 日 ×η×(1-L 损 )

[0022] In the formula, E 理 Indicates theoretical power generation, P 容 Indicates installed capacity, H 日 represents the annual average sunshine hours, η represents the component efficiency, L 损 Indicates system loss.

[0023] In one embodiment, the step of constructing a power generation prediction model for the area to be evaluated based on the theoretical power generation and the acquired data, and determining the predicted power generation of the area to be evaluated using the power generation prediction model includes:

[0024] The installed capacity, component efficiency and system loss are used as fixed characteristic variables, and the annual average sunshine hours, solar irradiance, temperature, precipitation and dust conditions are used as dynamic characteristic variables to construct a power generation prediction model.

[0025] The theoretical power generation, photovoltaic power generation system parameters and meteorological data in the predetermined time period of the area to be evaluated are divided into training set data and test set data, and the training set data is used to train the power generation prediction model, and the regression coefficient is obtained by the least squares method;

[0026] Based on the test set data and regression coefficients, the performance of the trained power generation prediction model was evaluated by combining the root mean square error, mean absolute error and determination coefficient.

[0027] According to the trained power generation prediction model, combined with the current photovoltaic power generation system parameters and meteorological data of the area to be evaluated, the predicted power generation of the area to be evaluated within a predetermined time period is determined.

[0028] In one embodiment, the power generation prediction model expression is:

[0029]

[0030] Where Y represents the predicted power generation, b0, b1, b2, …, b n represents different regression coefficients, b j represents the jth group of regression coefficients, x1, x2, …, x n Represents different feature variables, x j represents the jth group of characteristic variables, j represents the jth group of characteristic variables, and n represents the total number of characteristic variables.

[0031] In one embodiment, the calculation formula of the root mean square error is:

[0032]

[0033] The calculation formula of the mean absolute error is:

[0034]

[0035] The calculation formula of the determination coefficient is:

[0036]

[0037] In the formula, R 2 represents the coefficient of determination, RMSE represents the root mean square error, MAE represents the mean absolute error, Y represents the average value of actual power generation, and Y g represents the actual power generation in the g-th sample, It represents the predicted power generation of the gth sample obtained by the power generation prediction model, and m represents the number of samples in the training set data.

[0038] In one embodiment, the income calculation formula is:

[0039] Income=Ele×(Price+Subsidy)

[0040] The cost is calculated as follows:

[0041] C=C0+C m

[0042] The calculation formula of the net value method is:

[0043]

[0044] In the formula, Income represents income, Ele represents the power generation forecast result, Price represents the on-grid electricity price, Subsidy represents the subsidy amount, C represents the total cost, C0 represents the initial investment cost, and C represents the total cost. m represents the annual operation and maintenance cost, NPV represents the net present value, t represents time, T represents the project life span, C t,t represents the income in year t, C t,c represents the cost in year t, and a represents the discount rate.

[0045] According to a second aspect of an embodiment of the present invention, a photovoltaic market benefit forecasting and evaluation system is provided.

[0046] In one embodiment, the photovoltaic market benefit forecasting and evaluation system includes:

[0047] Theoretical power generation calculation module, used to obtain the photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated, and calculate the theoretical power generation according to the photovoltaic power generation system parameters and meteorological data;

[0048] The power generation prediction module is used to construct a power generation prediction model for the area to be evaluated based on the theoretical power generation and the acquired data, and to determine the predicted power generation of the area to be evaluated using the power generation prediction model;

[0049] The benefit evaluation module is used to calculate the revenue and cost data of photovoltaic market projects based on the predicted power generation and combined with economic data, and to evaluate the benefits of the photovoltaic market using the net value method and the calculation results.

[0050] In one embodiment, the theoretical power generation calculation module obtains the photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated, and calculates the theoretical power generation according to the photovoltaic power generation system parameters and meteorological data. The photovoltaic power generation system parameters and economic data of the area to be evaluated are obtained through the photovoltaic enterprise system, and the photovoltaic power generation system parameters include installed capacity, component efficiency and system loss, and the economic data include initial investment cost, operation and maintenance cost and electricity price data of the area to be evaluated; the meteorological data of the area to be evaluated is obtained through the weather website, and the meteorological data includes the annual average sunshine hours, solar irradiance, temperature, precipitation and dust conditions; based on the outlier detection method, the photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated are processed to remove outliers and noise data; and the theoretical power generation is calculated according to the photovoltaic power generation system parameters and meteorological data;

[0051] Among them, the expression of the outlier detection method is:

[0052] when Then x j is the normal value;

[0053] when Then x j is an outlier;

[0054] In the formula, x j represents the jth data in any group of data, represents the mean of any group of data, and σ represents the standard deviation.

[0055] In one embodiment, the calculation formula of the theoretical power generation is:

[0056] E 理 =P 容 ×H 日 ×η×(1-L 损 )

[0057] In the formula, E 理 Indicates theoretical power generation, P 容 Indicates installed capacity, H 日 represents the annual average sunshine hours, η represents the component efficiency, L 损 Indicates system loss.

[0058] In one embodiment, the power generation prediction module constructs a power generation prediction model for the area to be evaluated based on the theoretical power generation and the acquired data, and uses the power generation prediction model to determine the predicted power generation of the area to be evaluated. The installed capacity, component efficiency and system loss are used as fixed characteristic variables, and the annual average sunshine hours, solar irradiance, temperature, precipitation and dust conditions are used as dynamic characteristic variables to construct the power generation prediction model; the theoretical power generation, photovoltaic power generation system parameters and meteorological data within a predetermined time period of the area to be evaluated are divided into training set data and test set data, and the training set data is used to train the power generation prediction model, and the regression coefficient is obtained by the least squares method; based on the test set data and the regression coefficient, the performance of the trained power generation prediction model is evaluated in combination with the root mean square error, the mean absolute error and the determination coefficient; according to the trained power generation prediction model, combined with the current photovoltaic power generation system parameters and meteorological data of the area to be evaluated, the predicted power generation within the predetermined time period of the area to be evaluated is determined.

[0059] In one embodiment, the power generation prediction model expression is:

[0060]

[0061] Where Y represents the predicted power generation, b0, b1, b2, …, b n represents different regression coefficients, b j represents the jth group of regression coefficients, x1, x2, …, x n Represents different feature variables, x j represents the jth group of characteristic variables, j represents the jth group of characteristic variables, and n represents the total number of characteristic variables.

[0062] In one embodiment, the calculation formula of the root mean square error is:

[0063]

[0064] The calculation formula of the mean absolute error is:

[0065]

[0066] The calculation formula of the determination coefficient is:

[0067]

[0068] In the formula, R 2 represents the coefficient of determination, RMSE represents the root mean square error, MAE represents the mean absolute error, Represents the average value of actual power generation, Y g represents the actual power generation in the g-th sample, It represents the predicted power generation of the gth sample obtained by the power generation prediction model, and m represents the number of samples in the training set data.

[0069] In one embodiment, the income calculation formula is:

[0070] Income=Ele×(Price+Subsidy)

[0071] The cost is calculated as follows:

[0072] C=C0+C m

[0073] The calculation formula of the net value method is:

[0074]

[0075] In the formula, Income represents income, Ele represents the power generation forecast result, Price represents the on-grid electricity price, Subsidy represents the subsidy amount, C represents the total cost, C0 represents the initial investment cost, and C represents the total cost. m represents the annual operation and maintenance cost, NPV represents the net present value, t represents time, T represents the project life span, C t,t represents the income in year t, C t,c represents the cost in year t, and a represents the discount rate.

[0076] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0077] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0078] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0079] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0080] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0081] The distributed photovoltaic market benefit prediction and evaluation method and system proposed in the present invention ensure the diversity and comprehensiveness of data sources by comprehensively acquiring photovoltaic power generation system parameters and economic data of photovoltaic enterprise systems and meteorological data of weather websites, which helps to improve the accuracy and reliability of prediction and evaluation, and provide more powerful support for decision-making. Based on the acquired data, a power generation prediction model is constructed, and training and optimization are carried out so that the model can better reflect the actual situation and improve the prediction accuracy, providing a scientific basis for the prediction of distributed photovoltaic market benefits. Through the power generation prediction results, combined with subsidy policies and grid-connected electricity price factors, the income and cost of the distributed photovoltaic market are calculated in detail, and the net value method is used to evaluate the benefits with objective evaluation standards, which helps decision makers to understand the economic benefits of the project more accurately and provide a strong basis for investment decisions.

[0082] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0084] Figure 1 is a flow chart of a photovoltaic market benefit prediction and evaluation method according to an exemplary embodiment;

[0085] Figure 2 is a structural block diagram of a photovoltaic market benefit prediction and evaluation system according to an exemplary embodiment;

[0086] Figure 3 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0087] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0088] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like used herein to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this article and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description herein, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal connection of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0089] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0090] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0091] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0092] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0093] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0094] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0095] Figure 1 An embodiment of a photovoltaic market benefit forecasting and evaluation method of the present invention is shown.

[0096] In this optional embodiment, the photovoltaic market benefit forecasting and evaluation method includes:

[0097] Step S101, obtaining photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated, and calculating theoretical power generation according to the photovoltaic power generation system parameters and meteorological data;

[0098] Step S103: construct a power generation prediction model for the area to be evaluated based on the theoretical power generation and the acquired data, and use the power generation prediction model to determine the predicted power generation of the area to be evaluated;

[0099] Step S105: Calculate the revenue and cost data of the photovoltaic market project based on the predicted power generation and economic data, and evaluate the benefits of the photovoltaic market using the net value method and the calculation results.

[0100] Figure 2 An embodiment of a photovoltaic market benefit forecasting and evaluation system of the present invention is shown.

[0101] In this optional embodiment, the photovoltaic market benefit forecasting and evaluation system includes:

[0102] Theoretical power generation calculation module 201 is used to obtain photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated, and calculate theoretical power generation according to the photovoltaic power generation system parameters and meteorological data;

[0103] The power generation prediction module 203 is used to construct a power generation prediction model for the area to be evaluated based on the theoretical power generation and the acquired data, and determine the predicted power generation of the area to be evaluated using the power generation prediction model;

[0104] The benefit evaluation module 205 is used to calculate the income and cost data of the photovoltaic market project based on the predicted power generation and economic data, and evaluate the benefits of the photovoltaic market using the net value method and the calculation results.

[0105] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are further described below from the perspective of architecture and principle, as follows:

[0106] A method for predicting and evaluating the benefits of distributed photovoltaic market, comprising:

[0107] Through the professional system of photovoltaic enterprises, the parameters and economic data of photovoltaic power generation systems in a specific area can be fully obtained. In terms of photovoltaic power generation system parameters, installed capacity is an important indicator to measure the scale of photovoltaic systems in the region, which determines the maximum power potential that the system can generate. Component efficiency reflects the ability of photovoltaic components to convert solar energy into electrical energy. Efficient components can generate more electricity under the same lighting conditions. System losses include various energy losses, such as line losses and inverter losses. Accurate understanding of these losses helps to more accurately evaluate the actual power generation. In terms of economic data, the initial investment cost covers the purchase and installation costs of photovoltaic components, inverters, and brackets. Operation and maintenance costs include the expenses of regular maintenance, inspection, and cleaning of equipment. At the same time, the electricity price data in the region is crucial to calculating the benefits of photovoltaic systems. Different electricity price policies and price levels will directly affect the economic benefits of photovoltaic systems.

[0108] Use weather websites to collect meteorological data for the corresponding area. The average annual sunshine hours are one of the key factors in evaluating the power generation of photovoltaic systems. Longer sunshine hours usually mean that more solar energy resources can be used. Solar irradiance directly determines the intensity of solar energy received by photovoltaic modules. Photovoltaic systems can generate more electricity under high irradiance conditions. Temperature also affects the performance of photovoltaic systems. Too high or too low temperatures will reduce the efficiency of the modules. Precipitation and dust conditions will affect the surface cleanliness of photovoltaic modules, thereby affecting their ability to receive solar energy.

[0109] The theoretical power generation is calculated based on the acquired data. The calculation of theoretical power generation usually takes into account factors such as installed capacity, solar irradiance, and component efficiency. On this basis, a power generation prediction model for the region is constructed by combining the theoretical power generation and the various acquired data. This model can comprehensively consider the impact of multiple factors on power generation, such as changes in meteorological conditions and dynamic changes in system losses.

[0110] By constructing the power generation prediction model for the region, we can obtain the power generation prediction results for the region within a specific time period. This prediction result can provide important data support for subsequent benefit evaluation.

[0111] Revenue and cost data are calculated based on the power generation forecast results. Revenue mainly comes from the sale of photovoltaic power generation, which is calculated based on the electricity price data and the predicted power generation in the area. Costs include initial investment costs and operation and maintenance costs. By comprehensively analyzing the calculated revenue and cost data, a comprehensive assessment of the benefits of the distributed photovoltaic market can be made. This assessment can help investors, enterprises and government departments better understand the economic feasibility and potential benefits of distributed photovoltaic projects, thereby providing a scientific basis for decision-making.

[0112] The specific data of photovoltaic power generation system parameters and economic data in a certain area obtained through the photovoltaic enterprise system include:

[0113] Taking a single site as a group of data, we collect a wide range of photovoltaic power generation system parameters and economic data. This will provide a more comprehensive understanding of the situation of different sites in the region and provide a rich data basis for subsequent analysis and prediction;

[0114] For the parameters of photovoltaic power generation system, the component efficiency data refers to the photoelectric conversion efficiency of solar panels. This efficiency determines the ability of solar panels to convert solar energy into electrical energy. Efficient solar panels can generate more electricity under the same lighting conditions, thereby improving the power generation efficiency of the entire photovoltaic system. System loss data includes line loss data and inverter loss data of the photovoltaic system during power generation and transmission. Line loss is mainly due to the energy loss generated when the current passes through the wire during transmission, and its size is related to the material, length, cross-sectional area and current of the wire. The inverter loss is more complicated. The efficiency curve provided by the inverter manufacturer is used to determine the loss power according to the actual working power point. The inverter will generate a certain amount of energy loss in the process of converting direct current to alternating current. The efficiency of the inverter is different at different working power points. Therefore, it is necessary to determine the loss power according to the actual situation. The line loss data calculation formula is:

[0115] P loss =I 2 R

[0116] Where Ploss represents line power loss, I represents line current, and R represents line resistance;

[0117] Inverter loss: The inverter manufacturer provides efficiency curves to determine the power loss based on the actual operating power point.

[0118] In terms of economic data, the initial investment cost data includes the purchase cost of photovoltaic equipment and the installation and commissioning costs. The purchase cost of photovoltaic equipment covers the cost of the main equipment such as solar panels, inverters, and brackets. The prices of equipment of different brands, models, and specifications vary greatly, and need to be selected according to actual needs. The installation and commissioning costs include the installation, wiring, and commissioning costs of the equipment. This part of the cost will also vary depending on the difficulty of installation and the scale of the project. The operation and maintenance cost data is determined according to the operation and maintenance cost accounting standards. The operation and maintenance costs mainly include the regular maintenance, inspection, cleaning costs of the equipment, and the repair costs of any faults that occur. By formulating reasonable operation and maintenance cost accounting standards, the cost expenditure of the photovoltaic system during operation can be accurately calculated. The calculation method is:

[0119] C year =C initial ×rate

[0120] In the formula, C year represents the annual operation and maintenance cost, C initial The table represents the initial investment cost, and rate represents the operation and maintenance cost rate.

[0121] After obtaining the photovoltaic power generation system parameters, economic data and meteorological data of a certain region, the data is preprocessed to remove outliers and noise data. The outlier detection method is as follows:

[0122] Let the jth data in any set of data be x j , whose average value is The standard deviation is σ, then:

[0123] like Then x j is a normal value; if Then x j is an outlier.

[0124] The theoretical power generation calculated based on the acquired data specifically includes:

[0125] Based on the photovoltaic enterprise system, the photovoltaic power generation system parameters and economic data of a certain region are obtained, and the weather website obtains the corresponding regional meteorological data, and pre-processes the data;

[0126] Based on the preprocessed data, the theoretical power generation is calculated, where the calculation formula for the theoretical power generation is:

[0127] E 理 =P容 ×H 日 ×η×(1-L 损 )

[0128] In the formula, E 理 Indicates theoretical power generation; P 容 Indicates installed capacity; H 日 represents the annual average sunshine hours; η represents the component efficiency; L 损 Indicates system loss.

[0129] The construction of the power generation prediction model for this region specifically includes:

[0130] Based on the preprocessed photovoltaic enterprise system, the photovoltaic power generation system parameters and economic data of a certain region are obtained, and the meteorological data of the corresponding region is obtained from the weather website. Preprocessing this data is a very important step, which includes removing outliers and data standardization operations to ensure the quality and reliability of the data. The photovoltaic power generation system parameters and economic data obtained through the photovoltaic enterprise system provide the model with key information about the photovoltaic system itself, while the meteorological data obtained from the weather website reflects the impact of the external environment on photovoltaic power generation;

[0131] The installed capacity, component efficiency, and system loss are used as fixed characteristic variables, and the annual average sunshine hours, solar irradiance, temperature, precipitation, and dust conditions are used as dynamic characteristic variables to construct a power generation prediction model. The installed capacity determines the maximum power generation potential of the photovoltaic system, the component efficiency reflects the efficiency of converting solar energy into electrical energy, and the system loss will reduce the actual power generation. These fixed characteristic variables are relatively stable within a certain period of time. However, the dynamic characteristic variables of annual average sunshine hours, solar irradiance, temperature, precipitation, and dust conditions will change with time and weather conditions, and have an important impact on photovoltaic power generation. By combining these fixed and dynamic characteristic variables, a power generation prediction model is constructed;

[0132] The photovoltaic power generation, photovoltaic power generation system parameters and meteorological data in a certain period of time are divided into a training set and a test set. The model is trained using the training set data, and the regression coefficient is obtained by the least squares method. The least squares method is a commonly used parameter estimation method. It determines the regression coefficient by minimizing the sum of squares of the error between the actual value and the predicted value. The least squares method is used to obtain the regression coefficient:

[0133] Assume that the training set data has m samples, and each sample has n feature variables, that is:

[0134] (x g1, x g2, …x gn, Y g ),g=1,2,…,m

[0135] In the formula, x gn represents the nth feature variable in the gth sample, Y g represents the actual power generation in the g-th sample.

[0136] Minimize the residual sum of squares, where the expression for minimizing the residual sum of squares is:

[0137]

[0138] In the formula, S represents the residual sum of squares, represents the predicted power generation of the g-th sample obtained by the power generation prediction model;

[0139] The regression coefficient b j Find the partial derivative and set it to 0 to get the regression coefficient value;

[0140] The trained model was evaluated using the test set data using the root mean square error, mean absolute error, and coefficient of determination R 2 Evaluate the performance of the model. The root mean square error and mean absolute error can measure the error between the model prediction value and the actual value. The determination coefficient R 2 It reflects the degree of fit of the model to the data;

[0141] The trained power generation prediction model is applied to actual scenarios, and the current photovoltaic power generation system parameters and meteorological data are input to obtain the power generation prediction results for a certain period of time in the area. This prediction result provides an important reference for the operational decisions of photovoltaic enterprises, the planning of power dispatching departments, and the decision-making of investors.

[0142] The power generation prediction model expression is:

[0143]

[0144] Where Y represents the predicted power generation, b0, b1, b2, …, b n represents different regression coefficients, b j represents the jth group of regression coefficients, x1, x2, …, x n Represents different feature variables, x j represents the jth group of characteristic variables, j represents the jth group of characteristic variables, and n represents the total number of characteristic variables.

[0145] The root mean square error, mean absolute error and coefficient of determination R 2 The performance of the evaluation model specifically includes:

[0146] The calculation formula of the root mean square error is:

[0147]

[0148] The formula for calculating the mean absolute error is:

[0149]

[0150] Coefficient of determination R 2 The calculation formula is:

[0151]

[0152] In the formula, R 2 represents the coefficient of determination, RMSE represents the root mean square error, MAE represents the mean absolute error, Represents the average value of actual power generation, Y g represents the actual power generation in the g-th sample, It represents the predicted power generation of the gth sample obtained by the power generation prediction model, and m represents the number of samples in the training set data.

[0153] The calculation of revenue and cost data based on power generation forecast results specifically includes:

[0154] When calculating income, the subsidy policy and on-grid electricity price are taken into account, and the income calculation formula is:

[0155] Income=Ele×(Price+Subsidy)

[0156] In the formula, Income represents income, Ele represents the power generation forecast result, Price represents the on-grid electricity price, and Subsidy represents the subsidy amount;

[0157] Subsidy policies play a key role in promoting the development of distributed photovoltaics. In order to encourage the development of renewable energy, many regions will introduce a series of subsidy policies, which can directly increase the income of photovoltaic projects. Subsidies can reduce the investment payback period of projects, improve the economic feasibility of projects, and attract more investors to enter the distributed photovoltaic field.

[0158] The on-grid electricity price is one of the core factors that determine the income of photovoltaic projects. The electricity generated by distributed photovoltaics is connected to the power grid. The level of the on-grid electricity price directly affects the electricity sales revenue. The on-grid electricity prices in different regions vary due to energy policies and power market supply and demand factors. Reasonable consideration of the on-grid electricity price can accurately evaluate the project's profitability in different market environments and provide a reliable basis for the project's investment decisions.

[0159] The cost calculation formula is:

[0160] C=C0+C m

[0161] In the formula, C represents the total cost, C0 represents the initial investment cost, and C0 represents the annual cost. mIndicates the annual operation and maintenance cost.

[0162] The effectiveness assessment includes:

[0163] The net value method is used to evaluate the benefits, and the calculation formula of the net value method is:

[0164]

[0165] In the formula, NPV represents net present value, t represents time, T represents the project life, and C t,t represents the income in year t, C t,c represents the cost in year t, and a represents the discount rate;

[0166] When NPV>0, it means the project is beneficial.

[0167] The net value method takes into account the time value of money. The benefits and costs of distributed photovoltaic projects are usually distributed at different time points. The net value method can be used to discount cash flows from different future periods to the current moment. This helps to accurately measure the true benefits of the project and avoid the misleading effect of simple addition, allowing investors to more intuitively see the current economic value of the project.

[0168] The net value method can comprehensively evaluate the project benefits. It takes into account both revenue and cost factors, including not only the sales revenue and subsidy revenue of photovoltaic power generation, but also the initial investment cost, operation and maintenance cost and all project-related income and expenditure. This can more accurately reflect the profitability of distributed photovoltaic projects throughout their life cycle, help investors determine whether a project is worth investing in, and provide a strong basis for project planning and optimization.

[0169] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0170] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0171] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0172] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0173] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0174] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A photovoltaic market benefit forecasting and evaluation method, characterized in that: include: Obtain the photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated, and calculate the theoretical power generation based on the photovoltaic power generation system parameters and meteorological data; Based on the theoretical power generation and the acquired data, a power generation prediction model for the area to be evaluated is constructed, and the predicted power generation of the area to be evaluated is determined using the power generation prediction model; Based on the predicted power generation, combined with economic data, the revenue and cost data of photovoltaic market projects are calculated, and the benefits of the photovoltaic market are evaluated using the net value method and the calculation results.

2. The photovoltaic market benefit forecasting and evaluation method according to claim 1, characterized in that: The obtaining of photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated, and calculating theoretical power generation according to the photovoltaic power generation system parameters and meteorological data includes: Obtaining the photovoltaic power generation system parameters and economic data of the area to be evaluated through the photovoltaic enterprise system, wherein the photovoltaic power generation system parameters include installed capacity, component efficiency and system loss, and the economic data include initial investment cost, operation and maintenance cost and electricity price data of the area to be evaluated; Obtain meteorological data of the area to be evaluated through weather websites, including annual average sunshine hours, solar irradiance, temperature, precipitation and dust conditions; Based on the outlier detection method, outlier and noise data are removed from the photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated; and the theoretical power generation is calculated based on the photovoltaic power generation system parameters and meteorological data; Among them, the expression of the outlier detection method is: when Then x j is the normal value; when Then x j is an outlier; In the formula, x j represents the jth data in any group of data, represents the mean of any group of data, and σ represents the standard deviation.

3. The photovoltaic market benefit forecasting and evaluation method according to claim 2 is characterized in that: The calculation formula of the theoretical power generation is: AND 理 =P 容 ×H 日 ×η×(1-L 损 ) In the formula, E 理 Indicates theoretical power generation, P 容 Indicates installed capacity, H 日 represents the annual average sunshine hours, η represents the component efficiency, L 损 Indicates system loss.

4. The photovoltaic market benefit forecasting and evaluation method according to claim 2, characterized in that: The method of constructing a power generation prediction model for the area to be evaluated based on the theoretical power generation and the acquired data, and determining the predicted power generation of the area to be evaluated by using the power generation prediction model includes: The installed capacity, component efficiency and system loss are used as fixed characteristic variables, and the annual average sunshine hours, solar irradiance, temperature, precipitation and dust conditions are used as dynamic characteristic variables to construct a power generation prediction model. The theoretical power generation, photovoltaic power generation system parameters and meteorological data in the predetermined time period of the area to be evaluated are divided into training set data and test set data, and the power generation prediction model is trained using the training set data, and the regression coefficient is obtained by the least squares method; Based on the test set data and regression coefficients, the performance of the trained power generation prediction model was evaluated by combining the root mean square error, mean absolute error and determination coefficient. According to the trained power generation prediction model, combined with the current photovoltaic power generation system parameters and meteorological data of the area to be evaluated, the predicted power generation of the area to be evaluated within a predetermined time period is determined.

5. The photovoltaic market benefit forecasting and evaluation method according to claim 4 is characterized in that: The power generation prediction model expression is: Where Y represents the predicted power generation, b0, b1, b2, …, b n represents different regression coefficients, b j represents the jth group of regression coefficients, x1, x2, …, x n Represents different feature variables, x j represents the jth group of characteristic variables, j represents the jth group of characteristic variables, and n represents the total number of characteristic variables.

6. The photovoltaic market benefit forecasting and evaluation method according to claim 4, characterized in that: The calculation formula of the root mean square error is: The calculation formula of the mean absolute error is: The calculation formula of the determination coefficient is: In the formula, R 2 represents the coefficient of determination, RMSE represents the root mean square error, MAE represents the mean absolute error, Represents the average value of actual power generation, Y g represents the actual power generation in the g-th sample, It represents the predicted power generation of the gth sample obtained by the power generation prediction model, and m represents the number of samples in the training set data.

7. The photovoltaic market benefit forecasting and evaluation method according to claim 1, characterized in that: The income calculation formula is: Income=Ele×(Price+Subsidy) The cost is calculated as follows: C=C0+C m The calculation formula of the net value method is: In the formula, Income represents income, Ele represents the power generation forecast result, Price represents the on-grid electricity price, Subsidy represents the subsidy amount, C represents the total cost, C0 represents the initial investment cost, and C represents the m represents the annual operation and maintenance cost, NPV represents the net present value, t represents time, T represents the project life span, C t,t represents the income in year t, C t,c represents the cost in year t, and a represents the discount rate.

8. A photovoltaic market benefit forecasting and evaluation system, characterized in that: include: Theoretical power generation calculation module, used to obtain the photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated, and calculate the theoretical power generation according to the photovoltaic power generation system parameters and meteorological data; The power generation prediction module is used to construct a power generation prediction model for the area to be evaluated based on the theoretical power generation and the acquired data, and to determine the predicted power generation of the area to be evaluated using the power generation prediction model; The benefit evaluation module is used to calculate the revenue and cost data of photovoltaic market projects based on the predicted power generation and combined with economic data, and to evaluate the benefits of the photovoltaic market using the net value method and the calculation results.

9. The photovoltaic market benefit forecasting and evaluation system according to claim 8, characterized in that: When the theoretical power generation calculation module obtains the photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated, and calculates the theoretical power generation according to the photovoltaic power generation system parameters and meteorological data, the photovoltaic power generation system parameters and economic data of the area to be evaluated are obtained through the photovoltaic enterprise system, the photovoltaic power generation system parameters include installed capacity, component efficiency and system loss, and the economic data include initial investment cost, operation and maintenance cost and electricity price data of the area to be evaluated; the meteorological data of the area to be evaluated is obtained through the weather website, and the meteorological data includes annual average sunshine hours, solar irradiance, temperature, precipitation and dust conditions; based on the outlier detection method, the outlier and noise data are removed from the photovoltaic power generation system parameters, economic data and meteorological data of the area to be evaluated; and the theoretical power generation is calculated according to the photovoltaic power generation system parameters and meteorological data; Among them, the expression of the outlier detection method is: when Then x j is the normal value; when Then x j is an outlier; In the formula, x j represents the jth data in any group of data, represents the mean of any group of data, and σ represents the standard deviation.

10. The photovoltaic market benefit forecasting and evaluation system according to claim 9, characterized in that: The calculation formula of the theoretical power generation is: AND 理 =P 容 ×H 日 ×η×(1-L 损 ) In the formula, E 理 Indicates theoretical power generation, P 容 Indicates installed capacity, H 日 represents the annual average sunshine hours, η represents the component efficiency, L 损 Indicates system loss.

11. The photovoltaic market benefit forecasting and evaluation system according to claim 9, characterized in that: The power generation prediction module constructs a power generation prediction model for the area to be evaluated based on the theoretical power generation and the acquired data, and uses the power generation prediction model to determine the predicted power generation of the area to be evaluated. The installed capacity, component efficiency and system loss are used as fixed characteristic variables, and the annual average sunshine hours, solar irradiance, temperature, precipitation and dust conditions are used as dynamic characteristic variables to construct the power generation prediction model; the theoretical power generation, photovoltaic power generation system parameters and meteorological data within a predetermined time period of the area to be evaluated are divided into training set data and test set data, and the training set data is used to train the power generation prediction model, and the regression coefficient is obtained by the least squares method; Based on the test set data and regression coefficient, the performance of the trained power generation prediction model is evaluated in combination with the root mean square error, mean absolute error and determination coefficient; according to the trained power generation prediction model, combined with the current photovoltaic power generation system parameters and meteorological data of the area to be evaluated, the predicted power generation of the area to be evaluated within a predetermined time period is determined.

12. The photovoltaic market benefit forecasting and evaluation system according to claim 11, characterized in that: The power generation prediction model expression is: Where Y represents the predicted power generation, b0, b1, b2, …, b n represents different regression coefficients, b j represents the jth group of regression coefficients, x1, x2, …, x n Represents different feature variables, x j represents the jth group of characteristic variables, j represents the jth group of characteristic variables, and n represents the total number of characteristic variables.

13. The photovoltaic market benefit forecasting and evaluation system according to claim 11, characterized in that: The calculation formula of the root mean square error is: The calculation formula of the mean absolute error is: The calculation formula of the determination coefficient is: In the formula, R 2 represents the coefficient of determination, RMSE represents the root mean square error, MAE represents the mean absolute error, Represents the average value of actual power generation, Y g represents the actual power generation in the g-th sample, It represents the predicted power generation of the gth sample obtained by the power generation prediction model, and m represents the number of samples in the training set data.

14. The photovoltaic market benefit forecasting and evaluation system according to claim 8, characterized in that: The income calculation formula is: Income=Ele×(Price+Subsidy) The cost is calculated as follows: C=C0+C m The calculation formula of the net value method is: In the formula, Income represents income, Ele represents the power generation forecast result, Price represents the on-grid electricity price, Subsidy represents the subsidy amount, C represents the total cost, C0 represents the initial investment cost, and C represents the m represents the annual operation and maintenance cost, NPV represents the net present value, t represents time, T represents the project life span, C t,t represents the income in year t, C t,c represents the cost in year t, and a represents the discount rate.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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