Photovoltaic power station power prediction system based on multi-source data fusion

Abstract

The invention relates to the technical field of power prediction, and discloses a photovoltaic power station power prediction system based on multi-source data fusion, a multi-source data module collects sub-multi-source data of a multi-source data collection source, and converts the sub-multi-source data based on a preset rule to obtain fused multi-source data; the data fusion module performs data fusion to obtain a fused multi-source data sequence, and determines an initial fused multi-source data sequence based on the sequence retention value; a data calculation module performs recombination analysis on the initial fusion multi-source data sequence to obtain a target fusion multi-source data sequence, and calculates a target multi-source data factor; and the power prediction module obtains the predicted photovoltaic power station power based on the target multi-source data factor and the actual photovoltaic power station power, thereby solving the problem of multi-source data heterogeneity, effectively integrating the multi-source data, overcoming the limitation of a single data source, remarkably improving the precision and reliability of photovoltaic power prediction, and improving the reliability of photovoltaic power prediction. And more accurate decision support is provided for stable operation of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power prediction, and more particularly, to a photovoltaic power station power prediction system based on multi-source data fusion. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, photovoltaic power generation, as a clean and sustainable energy form, has been widely used. However, photovoltaic power generation is affected by various factors such as weather conditions, geographical locations, and equipment status, and has significant intermittency and volatility, which poses great challenges to the stable operation and dispatching of power systems. Therefore, accurately predicting the power generation of photovoltaic power stations is of great significance for improving the stability and economy of power grids.

[0003] Traditional photovoltaic power prediction methods mainly rely on a single data source, such as historical power data or meteorological forecast data. Although these methods can provide prediction results to a certain extent, due to the single data source, the prediction accuracy is often limited. For example, methods that only rely on historical power data cannot fully consider the impact of weather changes, while methods that only rely on meteorological forecast data are difficult to capture changes in equipment status and operating environment. Moreover, there are differences in data formats, time resolutions, and spatial resolutions of different data sources, and how to effectively integrate these heterogeneous data is another technical difficulty, resulting in insufficient prediction capabilities. Summary of the Invention

[0004] Embodiments of the present invention provide a photovoltaic power station power prediction system based on multi-source data fusion. The present invention solves the problem of multi-source data heterogeneity, reduces data redundancy and noise interference, effectively integrates multi-source data, overcomes the limitations of a single data source, significantly improves the accuracy and reliability of photovoltaic power prediction, and provides more accurate decision-making support for the stable operation of power systems.

[0005] To achieve the above object, the present invention provides a photovoltaic power station power prediction system based on multi-source data fusion, including: A multi-source data module, configured to determine multi-source data collection sources, collect sub-multi-source data of each multi-source data collection source, and convert each sub-multi-source data based on preset rules to obtain fused multi-source data; A data fusion module, configured to perform data fusion on all the fused multi-source data to obtain a plurality of fused multi-source data sequences corresponding to the multi-source data collection sources, calculate the fused multi-source data sequences to obtain sequence retention values, and determine an initial fused multi-source data sequence based on the sequence retention values; A data calculation module, configured to reorganize and analyze all the initial fused multi-source data sequences to obtain a target fused multi-source data sequence, and calculate target multi-source data factors of the target fused multi-source data sequence; A power prediction module, configured to obtain the actual power of a photovoltaic power station at the current moment, and based on the target multi-source data factor and the actual power of the photovoltaic power station, obtain the predicted power of the photovoltaic power station of the photovoltaic power station.

[0006] Further, the multi-source data module is configured to: The multi-source data module is configured to determine the reference sub-multi-source data corresponding to each sub-multi-source data, and perform conversion on each sub-multi-source data according to the reference sub-multi-source data; ; Wherein, q is the fused multi-source data corresponding to the sub-multi-source data, w is the weight corresponding to the sub-multi-source data, e is a constant, r is the sub-multi-source data, and r1 is the reference sub-multi-source data.

[0007] Further, the data fusion module is configured to: The data fusion module is configured to perform curve fitting on the fused multi-source data sequence to obtain a fused multi-source data curve, and determine the curve slope corresponding to each fused multi-source data; The data fusion module is configured to determine the maximum curve slope, and use the product value of the maximum curve slope and the number of the fused multi-source data as the sequence complexity of the fused multi-source data sequence; The data fusion module is configured to calculate the sequence retention value of the fused multi-source data according to the sequence complexity and the fused multi-source data sequence; The data fusion module is configured to obtain a preset sequence retention value. If the sequence retention value is greater than the preset sequence retention value, the fused multi-source data sequence is used as the initial fused multi-source data sequence; The data fusion module is configured to, if the sequence retention value is less than or equal to the sequence retention value, sort the fused multi-source data sequence in ascending order; The data fusion module is configured to select the first fused multi-source data and the second fused multi-source data from the fused multi-source data sequence, and calculate the fused multi-source data difference between the first fused multi-source data and the second fused multi-source data; The data fusion module is configured to obtain a preset fused multi-source data difference. If the fused multi-source data difference is greater than the preset fused multi-source data difference, the first fused multi-source data is removed, the second fused multi-source data is retained, the third fused multi-source data is extracted, and the second fused multi-source data difference between the second fused multi-source data and the third fused multi-source data is calculated; The data fusion module is configured to, if the difference between the fused multi-source data is less than or equal to the preset difference of the fused multi-source data, retain the first fused multi-source data and the second fused multi-source data, extract the third fused multi-source data and the fourth multi-source fused data, and calculate the third difference of the fused multi-source data between the third fused multi-source data and the fourth fused multi-source data; The data fusion module is configured to, by analogy, judge the fused multi-source data in the fused multi-source data sequence to obtain the initial fused multi-source data sequence.

[0008] Further, the data fusion module is configured to: The data fusion module is configured to calculate the sequence retention value of the fused multi-source data according to the following formula: ; where t is the sequence retention value of the fused multi-source data, y1 max is the maximum fused multi-source data in the fused multi-source data sequence, y2 min is the minimum fused multi-source data in the fused multi-source data sequence, u is the sequence complexity, p is the number of fused multi-source data in the fused multi-source data sequence, s i is the i-th fused multi-source data in the fused multi-source data sequence, s i+1 is the (i + 1)-th fused multi-source data in the fused multi-source data sequence.

[0009] Further, the data calculation module is configured to: The data calculation module is configured to extract the target fused multi-source data in each initial fused multi-source data sequence and reorganize them to obtain a target fused multi-source data sequence; The data calculation module is configured to reorganize every a target fused multi-source data in the target fused multi-source data sequence to obtain a plurality of sub-target fused multi-source data sequences; The data calculation module is configured to sum the sub-target fused multi-source data in each sub-target fused multi-source data sequence to obtain a sub-target fused multi-source data sum value; The data calculation module is configured to determine the maximum target fused multi-source data in the target fused multi-source data sequence, calculate the ratio of the maximum target fused multi-source data to the sub-target fused multi-source data sum value as the relative target fused multi-source data; The data calculation module is configured to combine all the relative target fused multi-source data in pairs to obtain a plurality of target fused multi-source data combinations; The data calculation module is configured to calculate the target multi-source data factor of the target fused multi-source data sequence according to all the target fused multi-source data combinations.

[0010] Further, the data calculation module is configured to: The data calculation module is configured to calculate a target multi-source data factor of the target fused multi-source data sequence according to the following formula: ; where d is the target multi-source data factor of the target fused multi-source data sequence, g is the number of target fused multi-source data combinations, h f is a relative target fused multi-source data corresponding to the f-th target fused multi-source data combination, and j f is another relative target fused multi-source data corresponding to the f-th target fused multi-source data combination, is the maximum value of all and is the minimum value of all .

[0011] Further, the power prediction module is configured to: The power prediction module is configured to obtain a preset target multi-source data factor threshold, and judge whether power prediction of the photovoltaic power station needs to be performed according to the relationship between the target multi-source data factor and the target multi-source data factor threshold; The power prediction module is configured to judge that power prediction of the photovoltaic power station does not need to be performed when the target multi-source data factor is less than or equal to the target multi-source data factor threshold; The power prediction module is configured to judge that power prediction of the photovoltaic power station needs to be performed when the target multi-source data factor is greater than the target multi-source data factor threshold.

[0012] Further, the power prediction module is configured to: The power prediction module is configured to preset a first preset target multi-source data factor and a second preset target multi-source data factor; The power prediction module is configured to preset a first preset prediction factor, a second preset prediction factor, and a third preset prediction factor; The power prediction module is configured to calculate a first product value of the first preset prediction factor and the actual photovoltaic power station power as the predicted photovoltaic power station power when the target multi-source data factor is less than the first preset target multi-source data factor; The power prediction module is configured to calculate a second product value of the second preset prediction factor and the actual photovoltaic power station power as the predicted photovoltaic power station power when the target multi-source data factor is greater than or equal to the first preset target multi-source data factor and less than the second preset target multi-source data factor; The power prediction module is used to calculate a third product value of the third preset prediction factor and the actual photovoltaic power station power as the predicted photovoltaic power station power of the photovoltaic power station when the target multi-source data factor is greater than or equal to the second preset target multi-source data factor.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention discloses a photovoltaic power station power prediction system based on multi-source data fusion. The multi-source data module collects sub-multi-source data of a multi-source data acquisition source, converts the sub-multi-source data based on a preset rule to obtain fused multi-source data; the data fusion module performs data fusion to obtain a fused multi-source data sequence, and determines an initial fused multi-source data sequence based on a sequence retention value; the data calculation module reorganizes and analyzes the initial fused multi-source data sequence to obtain a target fused multi-source data sequence, and calculates a target multi-source data factor; the power prediction module obtains the predicted photovoltaic power station power based on the target multi-source data factor and the actual photovoltaic power station power, solves the problem of multi-source data heterogeneity, effectively integrates multi-source data, overcomes the limitations of a single data source, significantly improves the accuracy and reliability of photovoltaic power prediction, and provides more accurate decision-making support for the stable operation of the power system. Description of the Drawings

[0014] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 A schematic structural diagram of a photovoltaic power station power prediction system based on multi-source data fusion in an embodiment of the present invention is shown. Detailed Embodiments

[0015] The following combines the drawings and embodiments to further describe in detail the specific embodiments of the present invention. The following embodiments are used to illustrate the present invention but are not used to limit the scope of the present invention.

[0016] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present application.

[0017] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0018] In the description of this application, it should be noted that unless otherwise clearly specified and limited, the terms "install", "connect", and "couple" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0019] The following is a description of the preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0020] As Figure 1 shown, the embodiments of the present invention disclose a photovoltaic power station power prediction system based on multi-source data fusion, including: A multi-source data module for determining multi-source data acquisition sources and collecting sub-multi-source data of each multi-source data acquisition source, and converting each sub-multi-source data based on a preset rule to obtain fused multi-source data; A data fusion module for performing data fusion on all the fused multi-source data to obtain a plurality of fused multi-source data sequences corresponding to the multi-source data acquisition sources, calculating the sequences to obtain sequence retention values, and determining an initial fused multi-source data sequence based on the sequence retention values; A data calculation module for reorganizing and analyzing all the initial fused multi-source data sequences to obtain a target fused multi-source data sequence and calculating the target multi-source data factors of the target fused multi-source data sequence; A power prediction module for obtaining the actual photovoltaic power station power at the current moment, and obtaining the predicted photovoltaic power station power of the photovoltaic power station based on the target multi-source data factors and the actual photovoltaic power station power.

[0021] The beneficial effects of the above technical solutions are: The present invention solves the problem of multi-source data heterogeneity, effectively integrates multi-source data, overcomes the limitations of a single data source, significantly improves the accuracy and reliability of photovoltaic power prediction, and provides more accurate decision-making support for the stable operation of the power system.

[0022] In some embodiments of this application, the multi-source data module is used for: The multi-source data module is used to determine the reference sub-multi-source data corresponding to each sub-multi-source data, and convert each sub-multi-source data according to the reference sub-multi-source data; ; where q is the fused multi-source data corresponding to the sub-multi-source data, w is the weight corresponding to the sub-multi-source data, e is a constant, r is the sub-multi-source data, and r1 is the reference sub-multi-source data.

[0023] In this embodiment, the multi-source data acquisition source refers to the data acquisition direction, which includes historical power data, weather forecast data, equipment status data, geographical information data, etc., which are not shown one by one here.

[0024] In this embodiment, the sub-multi-source data refers to the detailed data corresponding to each multi-source data acquisition source. For example, the historical power data includes current data and voltage data, etc., and the weather prediction data includes solar radiation data and temperature data, etc., which are not shown one by one here.

[0025] In this embodiment, each sub-multi-source data has a corresponding reference sub-multi-source data, and they are in one-to-one correspondence. For example, the reference sub-multi-source data corresponding to the temperature data is 25 degrees Celsius, etc., which are not shown one by one here.

[0026] The beneficial effects of the above technical solution are: According to the reference sub-multi-source data, the present invention converts each sub-multi-source data, which can unify all sub-multi-source data to one dimension, solves the problem of multi-source data heterogeneity, and effectively integrates multi-source data.

[0027] In some embodiments of the present application, the data fusion module is used for: The data fusion module is used to perform curve fitting on the fused multi-source data sequence to obtain a fused multi-source data curve, and determine the curve slope corresponding to each fused multi-source data; The data fusion module is used to determine the maximum curve slope, and use the product value of the maximum curve slope and the number of fused multi-source data as the sequence complexity of the fused multi-source data sequence; The data fusion module is used to calculate the sequence retention value of the fused multi-source data according to the sequence complexity and the fused multi-source data sequence; The data fusion module is used to obtain a preset sequence retention value. If the sequence retention value is greater than the preset sequence retention value, the fused multi-source data sequence is used as the initial fused multi-source data sequence; The data fusion module is used to perform ascending sorting on the fused multi-source data sequence if the sequence retention value is less than or equal to the sequence retention value; The data fusion module is used to select the first multi-source fusion data and the second multi-source fusion data from the multi-source fusion data sequence, and calculate the difference of the multi-source fusion data between the first multi-source fusion data and the second multi-source fusion data; The data fusion module is used to obtain a preset difference of the multi-source fusion data. If the difference of the multi-source fusion data is greater than the preset difference of the multi-source fusion data, the first multi-source fusion data is excluded, the second multi-source fusion data is retained, the third multi-source fusion data is extracted, and the second difference of the multi-source fusion data between the second multi-source fusion data and the third multi-source fusion data is calculated; The data fusion module is used to if the difference of the multi-source fusion data is less than or equal to the preset difference of the multi-source fusion data, the first multi-source fusion data and the second multi-source fusion data are retained, the third multi-source fusion data and the fourth multi-source fusion data are extracted, and the third difference of the multi-source fusion data between the third multi-source fusion data and the fourth multi-source fusion data is calculated; The data fusion module is used to judge the multi-source fusion data in the multi-source fusion data sequence by analogy to obtain the initial multi-source fusion data sequence.

[0028] In this embodiment, ascending order sorting means sorting from small to large. Therefore, the first multi-source fusion data refers to the smallest multi-source fusion data.

[0029] In this embodiment, the preset sequence retention value is preferably 8 here, and it can be adjusted according to the actual situation specifically.

[0030] In this embodiment, the preset difference of the multi-source fusion data is preferably 3 here, and it can be adjusted according to the actual situation specifically.

[0031] The beneficial effects of the above technical solutions are: The present invention calculates the sequence retention value of the multi-source fusion data according to the sequence complexity and the multi-source fusion data sequence, which can provide a basis for the processing of the multi-source fusion data sequence, ensure the accuracy of the initial multi-source fusion data sequence, and at the same time remove noise data to ensure the calculation accuracy.

[0032] In some embodiments of the present application, the data fusion module is used for: The data fusion module is used to calculate the sequence retention value of the multi-source fusion data according to the following formula: ; where t is the sequence retention value of the multi-source fusion data, y1 max is the maximum multi-source fusion data in the multi-source fusion data sequence, y2 min is the minimum multi-source fusion data in the multi-source fusion data sequence, u is the sequence complexity, p is the number of multi-source fusion data in the multi-source fusion data sequence, si For the i-th fused multi-source data in the fused multi-source data sequence, s i+1 is the (i + 1)-th fused multi-source data in the fused multi-source data sequence.

[0033] In some embodiments of the present application, the data calculation module is configured to: The data calculation module is configured to extract the target fused multi-source data in each initial fused multi-source data sequence and perform recombination to obtain a target fused multi-source data sequence; The data calculation module is configured to recombine every a target fused multi-source data in the target fused multi-source data sequence to obtain a plurality of sub-target fused multi-source data sequences; The data calculation module is configured to perform a summation calculation on the sub-target fused multi-source data in each sub-target fused multi-source data sequence to obtain a sub-target fused multi-source data sum value; The data calculation module is configured to determine the maximum target fused multi-source data of the target fused multi-source data sequence, calculate the target fused multi-source data ratio of the maximum target fused multi-source data and the sub-target fused multi-source data sum value as the relative target fused multi-source data; The data calculation module is configured to combine all the relative target fused multi-source data in pairs to obtain a plurality of target fused multi-source data combinations; The data calculation module is configured to calculate the target multi-source data factor of the target fused multi-source data sequence according to all the target fused multi-source data combinations.

[0034] In this embodiment, a is preferably 4, that is, every 4 target fused multi-source data are recombined.

[0035] In this embodiment, if there is a single relative target fused multi-source data that is not combined, this relative target fused multi-source data can be deleted.

[0036] The beneficial effects of the above technical solution are: The present invention calculates the target multi-source data factor of the target fused multi-source data sequence according to all the target fused multi-source data combinations, realizes the fusion calculation of multi-source data, avoids the limitations of single data calculation, ensures the fusion accuracy of multi-source data, and thus lays a foundation for the power prediction of photovoltaic power stations.

[0037] In some embodiments of the present application, the data calculation module is configured to: The data calculation module is configured to calculate the target multi-source data factor of the target fused multi-source data sequence according to the following formula: ; where d is the target multi-source data factor of the target fused multi-source data sequence, g is the number of target fused multi-source data combinations, hf is a relative target fusion multi-source data corresponding to the f-th target fusion multi-source data combination, j f is another relative target fusion multi-source data corresponding to the f-th target fusion multi-source data combination is for all the maximum value of is for all the minimum value of

[0038] In some embodiments of the present application, the power prediction module is configured to: The power prediction module is configured to obtain a preset target multi-source data factor threshold, and determine whether power prediction of the photovoltaic power station needs to be performed according to the relationship between the target multi-source data factor and the target multi-source data factor threshold; The power prediction module is configured to determine that power prediction of the photovoltaic power station does not need to be performed when the target multi-source data factor is less than or equal to the target multi-source data factor threshold; The power prediction module is configured to determine that power prediction of the photovoltaic power station needs to be performed when the target multi-source data factor is greater than the target multi-source data factor threshold.

[0039] In this embodiment, the target multi-source data factor threshold is 0.

[0040] In this embodiment, when the target multi-source data factor is less than the target multi-source data factor threshold, at this time, the power of the photovoltaic power station will not change, and the actual power of the photovoltaic power station is used as the predicted power for the next moment.

[0041] In some embodiments of the present application, the power prediction module is configured to: The power prediction module is configured to preset a first preset target multi-source data factor and a second preset target multi-source data factor; The power prediction module is configured to preset a first preset prediction factor, a second preset prediction factor, and a third preset prediction factor; The power prediction module is configured to calculate a first product value of the first preset prediction factor and the actual power of the photovoltaic power station as the predicted power of the photovoltaic power station when the target multi-source data factor is less than the first preset target multi-source data factor; The power prediction module is configured to calculate a second product value of the second preset prediction factor and the actual power of the photovoltaic power station as the predicted power of the photovoltaic power station when the target multi-source data factor is greater than or equal to the first preset target multi-source data factor and less than the second preset target multi-source data factor; When the target multi-source data factor is greater than or equal to the second preset target multi-source data factor, the power prediction module calculates the third product value of the third preset prediction factor and the actual photovoltaic power station power as the predicted photovoltaic power station power of the photovoltaic power station.

[0042] In this embodiment, the first preset target multi-source data factor is less than the second preset target multi-source data factor. The first preset target multi-source data factor is preferably 6, and the second preset target multi-source data factor is preferably 10. Specifically, it can also be adjusted according to the actual situation.

[0043] In this embodiment, the first preset prediction factor is less than the second preset prediction factor and less than the third preset prediction factor. The first preset prediction factor is preferably 0.9, the second preset prediction factor is preferably 1.1, and the third preset prediction factor is preferably 1.2. Specifically, it can also be adjusted according to the actual situation.

[0044] The beneficial effects of the above technical solutions are as follows: According to the target multi-source data factor, the first preset target multi-source data factor, and the second preset target multi-source data factor, the present invention selects the corresponding preset prediction factor, thereby realizing the adjustment of the actual photovoltaic power station power, obtaining the predicted photovoltaic power station power of the photovoltaic power station, ensuring the accuracy of the predicted photovoltaic power station power, avoiding the errors existing in manual prediction, significantly improving the accuracy and reliability of photovoltaic power prediction, and providing more accurate decision-making support for the stable operation of the power system.

[0045] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0046] Although the present invention has been described above with reference to the embodiments, various improvements can be made to it and components therein can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed in the present invention can be combined with each other in any way. The situation of these combinations is not fully described in this specification only for the consideration of saving space and resources.

[0047] Those of ordinary skill in the art can understand that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A photovoltaic power station power prediction system based on multi-source data fusion, characterized in that include: The multi-source data module is used to determine the multi-source data collection source, collect sub-multi-source data of each multi-source data collection source, and convert each sub-multi-source data based on preset rules to obtain fused multi-source data; A data fusion module is used to perform data fusion on all fused multi-source data to obtain multiple fused multi-source data sequences corresponding to the multi-source data acquisition sources, calculate the fused multi-source data sequences to obtain sequence retention values, and determine an initial fused multi-source data sequence based on the sequence retention values; A data calculation module is used to reorganize and analyze all initial fused multi-source data sequences to obtain a target fused multi-source data sequence, and calculate a target multi-source data factor of the target fused multi-source data sequence; The power prediction module is used to obtain the actual photovoltaic power station power at the current moment, and obtain the predicted photovoltaic power station power of the photovoltaic power station based on the target multi-source data factor and the actual photovoltaic power station power.

2. The photovoltaic power station power prediction system based on multi-source data fusion according to claim 1, wherein, The multi-source data module is used for: The multi-source data module is used to determine the reference sub-multi-source data corresponding to each sub-multi-source data, and convert each sub-multi-source data according to the reference sub-multi-source data; ； Among them, q is the fused multi-source data corresponding to the sub-multi-source data, w is the weight corresponding to the sub-multi-source data, e is a constant, r is the sub-multi-source data, and r1 is the reference sub-multi-source data.

3. The photovoltaic power station power prediction system based on multi-source data fusion according to claim 1, characterized in that, The data fusion module is used for: The data fusion module is used to perform curve fitting on the fused multi-source data sequence to obtain a fused multi-source data curve and determine the slope of the curve corresponding to each fused multi-source data; The data fusion module is used to determine the maximum curve slope, and the product value of the maximum curve slope and the number of fused multi-source data is used as the sequence complexity of the fused multi-source data sequence; The data fusion module is used to calculate the sequence retention value of the fused multi-source data according to the sequence complexity and the fused multi-source data sequence; The data fusion module is used to obtain a preset sequence retention value, and if the sequence retention value is greater than the preset sequence retention value, the fused multi-source data sequence is used as an initial fused multi-source data sequence; The data fusion module is used for sorting the fused multi-source data sequence in ascending order if the sequence retention value is less than or equal to the sequence retention value; The data fusion module is used to select the first fused multi-source data and the second fused multi-source data from the fused multi-source data sequence, and calculate the fused multi-source data difference between the first fused multi-source data and the second fused multi-source data; The data fusion module is used to obtain a preset fused multi-source data difference. If the fused multi-source data difference is greater than the preset fused multi-source data difference, the first fused multi-source data is discarded, the second fused multi-source data is retained, the third fused multi-source data is extracted, and the second fused multi-source data difference between the second fused multi-source data and the third fused multi-source data is calculated; The data fusion module is used to, if the difference between the fused multi-source data is less than or equal to the preset difference of the fused multi-source data, retain the first fused multi-source data and the second fused multi-source data, extract the third fused multi-source data and the fourth multi-source fused data, and calculate the third difference of the fused multi-source data between the third fused multi-source data and the fourth fused multi-source data; The data fusion module is used to, and so on, judge the fused multi-source data in the fused multi-source data sequence to obtain the initial fused multi-source data sequence.

4. The photovoltaic power station power prediction system based on multi-source data fusion according to claim 3, characterized in that, The data fusion module is used for: The data fusion module is used to calculate the sequence retention value of the fused multi-source data according to the following formula: ； Among them, t is the sequence retention value of the fused multi-source data, y1 max is the maximum fused multi-source data in the fused multi-source data sequence, y2 min is the minimum fused multi-source data in the fused multi-source data sequence, u is the sequence complexity, p is the number of fused multi-source data in the fused multi-source data sequence, s i is the i-th fused multi-source data in the fused multi-source data sequence, s i+1 is the (i + 1)-th fused multi-source data in the fused multi-source data sequence.

5. The photovoltaic power station power prediction system based on multi-source data fusion according to claim 1, wherein The data calculation module is used for: The data calculation module is used to extract the target fused multi-source data in each initial fused multi-source data sequence and reorganize them to obtain a target fused multi-source data sequence; The data calculation module is used to reorganize every a target fused multi-source data in the target fused multi-source data sequence to obtain multiple sub-target fused multi-source data sequences; The data calculation module is used to sum up the sub-target fused multi-source data in each sub-target fused multi-source data sequence to obtain the sum value of the sub-target fused multi-source data; The data calculation module is used to determine the maximum target fused multi-source data in the target fused multi-source data sequence, calculate the ratio of the maximum target fused multi-source data to the sum value of the sub-target fused multi-source data as the relative target fused multi-source data; The data calculation module is used to combine all the relative target fused multi-source data in pairs to obtain multiple target fused multi-source data combinations; The data calculation module is used to calculate the target multi-source data factor of the target fused multi-source data sequence according to all the target fused multi-source data combinations.

6. The photovoltaic power station power prediction system based on multi-source data fusion according to claim 5, wherein The data calculation module is used for: The data calculation module is used to calculate the target multi-source data factor of the target fused multi-source data sequence according to the following formula: ； Among them, d is the target multi-source data factor of the target fused multi-source data sequence, g is the number of target fused multi-source data combinations, and h f is a relative target fused multi-source data corresponding to the f-th target fused multi-source data combination, and j f is another relative target fused multi-source data corresponding to the f-th target fused multi-source data combination, is the maximum value of all and is the minimum value of all .

7. The photovoltaic power station power prediction system based on multi-source data fusion according to claim 1, characterized in that, The power prediction module is used for: The power prediction module is used to obtain a preset target multi-source data factor threshold, and judge whether power prediction of the photovoltaic power station is required according to the relationship between the target multi-source data factor and the target multi-source data factor threshold; The power prediction module is used to, when the target multi-source data factor is less than or equal to the target multi-source data factor threshold, judge that power prediction of the photovoltaic power station is not required; The power prediction module is used to, when the target multi-source data factor is greater than the target multi-source data factor threshold, judge that power prediction of the photovoltaic power station is required.

8. The photovoltaic power station power prediction system based on multi-source data fusion according to claim 1, characterized in that The power prediction module is used for: The power prediction module is used to preset a first preset target multi-source data factor and a second preset target multi-source data factor; The power prediction module is used to preset a first preset prediction factor, a second preset prediction factor and a third preset prediction factor; The power prediction module is used to calculate the first product value of the first preset prediction factor and the actual photovoltaic power station power as the predicted photovoltaic power station power of the photovoltaic power station when the target multi-source data factor is less than the first preset target multi-source data factor; The power prediction module is used to calculate the second product value of the second preset prediction factor and the actual photovoltaic power station power as the predicted photovoltaic power station power of the photovoltaic power station when the target multi-source data factor is greater than or equal to the first preset target multi-source data factor and less than the second preset target multi-source data factor; The power prediction module is used to calculate the third product value of the third preset prediction factor and the actual photovoltaic power station power as the predicted photovoltaic power station power of the photovoltaic power station when the target multi-source data factor is greater than or equal to the second preset target multi-source data factor.

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