CN117879490B - Photovoltaic equipment generated power prediction method and system - Google Patents
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Abstract
The disclosure provides a method and a system for predicting the generation power of photovoltaic equipment, and relates to the technical field of new energy generation control. The method comprises the following steps: acquiring photovoltaic equipment data, illumination related data and topographic data; constructing a solar radiation intensity model based on a back propagation neural network according to the acquired data, predicting the current solar radiation intensity, and obtaining a preliminary radiation intensity result; determining the sea distance according to the acquired data, carrying out weight distribution on the water vapor salt fog and the attached salt fog based on the sea distance, and calculating the radiation intensity loss rate influenced by the salt fog; correcting the primary radiation intensity result by the loss rate of the radiation intensity influenced by the salt fog, and reconstructing a solar radiation intensity model according to the correction result; predicting the radiation intensity by using the reconstructed solar radiation intensity model to obtain a final radiation intensity result; and predicting the photovoltaic power generation power according to the final radiation intensity result. The influence of salt mist on the photovoltaic power generation power is considered, and the accuracy of photovoltaic power generation power prediction is improved.
Description
Technical Field
The invention relates to the technical field of new energy power generation control, in particular to a method and a system for predicting power generation of photovoltaic equipment.
Background
Irradiation intensity refers to the amount of solar radiation energy projected vertically onto a certain unit area of the earth for a unit time. In the physical sense, the irradiation of the sun is a direct influence factor which causes the photovoltaic cell to generate the volt effect, the irradiation intensity directly influences the output power of the photovoltaic cell, and the larger the daily irradiation intensity is, the larger the generated power is.
The path of solar radiation to the photovoltaic panel is disturbed by a number of factors, mainly by the shielding effect, which weakens the intensity of the radiation reaching the photovoltaic panel. Wherein, the cloud quantity is a main factor for shielding the radiation path, and the larger the cloud quantity is a meteorological factor for representing the sky shielding degree, the lower the transparency of the air is, the more the solar radiation is weakened, therefore, the prior art mostly considers the solar radiation intensity loss caused by the cloud quantity to improve the accuracy of the photovoltaic power generation power prediction,
However, for coastal, salt water lakes, etc., the attenuation of direct radiation is also associated with atmospheric moisture and liquid particles in aerosols. The air in coastal and salt water lake areas contains a large amount of salt evaporated along with seawater, and salt mist with high concentration is formed by dissolving the salt in small water drops and exists in the atmosphere. The existence forms of salt mist in air can be divided into two main types:
(1) Large liquid drops have short residence time in the air, high deposition speed and multiple sedimentation at offshore places;
(2) The microscopic liquid drops have low sedimentation velocity and can be suspended in the air for a long time.
It can be seen that depending on the distance from sea, the salt mist will exist in different states, causing different degrees of influence on the solar radiation: the water vapor salt fog directly blocks the passage of solar radiation in the atmosphere; the attached salt mist forms a covering layer on the surface of the photovoltaic panel due to the action of static electricity, so that the absorption efficiency of the photovoltaic panel to illumination is affected.
However, in the current research of considering the influence of salt fog, aiming at the corrosion scene of a building, there are few researches of considering the influence of salt fog concentration on the intensity of solar radiation in the photovoltaic power prediction scene. Therefore, how to predict and correct the solar radiation intensity in coastal areas by considering the sea distance and the salt mist so as to obtain a high-efficiency photovoltaic power prediction result is a problem to be solved.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a method and a system for predicting the power of photovoltaic equipment, which take the influence of salt mist in coastal areas on the power of photovoltaic power generation into consideration, integrate diffused salt-containing water vapor in air and salt mist particles attached to the surface of a photovoltaic panel, and correct the actually received radiation intensity under the weakening condition of solar radiation absorption of the photovoltaic equipment so as to obtain a more accurate photovoltaic power generation power prediction result.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
In a first aspect, the present invention provides a method for predicting generated power of a photovoltaic device, including:
Acquiring photovoltaic equipment data, illumination related data and topographic data;
constructing a solar radiation intensity model based on a back propagation neural network according to the acquired data, predicting the current solar radiation intensity, and obtaining a preliminary radiation intensity result;
Determining the sea distance according to the photovoltaic equipment data and the topographic data, distributing weights of the water vapor salt fog and the attached salt fog based on the sea distance, and calculating the radiation intensity loss rate influenced by the salt fog;
correcting the primary radiation intensity result by the loss rate of the radiation intensity influenced by the salt fog, and reconstructing a solar radiation intensity model according to the correction result;
Predicting the radiation intensity by using the reconstructed solar radiation intensity model to obtain a final radiation intensity result;
And predicting the photovoltaic power generation power according to the final radiation intensity result.
Further technical solution, the illumination related data includes: solar radiation intensity, temperature information and cloud cover information acquired by local meteorological sites.
According to a further technical scheme, the specific process for constructing the solar radiation intensity model based on the back propagation neural network according to the acquired data comprises the following steps:
Determining a radiation intensity influence factor, and acquiring radiation intensity influence factor data of the photovoltaic equipment;
Dividing regions according to the distance from sea, constructing a back propagation neural network of each region and training;
and predicting the solar radiation intensity by using the trained back propagation neural network as a solar radiation intensity model to predict the radiation intensity influence factor data, so as to obtain a preliminary radiation intensity result.
Further technical scheme, the radiation intensity influence factors comprise illumination intensity, shielding condition and sea distance.
According to a further technical scheme, the training process of the back propagation neural network comprises the following steps:
And constructing a back propagation neural network architecture, and performing weight training on the back propagation neural network by adopting radiation intensity influence factor data to obtain a trained back propagation neural network.
According to a further technical scheme, weight distribution is carried out on the water vapor salt fog and the attached salt fog based on the distance from the sea, and the specific steps of calculating the radiation intensity loss influenced by the salt fog are as follows:
according to the sea distance, weight is distributed to the water vapor salt fog and the attached salt fog;
acquiring the loss rate of the water vapor salt mist-radiation intensity and the loss rate of the attached salt mist-radiation intensity;
and calculating the loss rate of the salt fog affecting the radiation intensity according to the weight and the loss rate.
According to the technical scheme, a photovoltaic power prediction model is built based on the long-term and short-term memory network.
In a second aspect, the present invention provides a photovoltaic device generated power prediction system, comprising:
The data acquisition module is used for acquiring photovoltaic equipment data, illumination related data and topographic data;
the preliminary prediction module is used for constructing a solar radiation intensity model based on a back propagation neural network according to the acquired data, predicting the current solar radiation intensity and obtaining a preliminary radiation intensity result;
The salt fog calculation module is used for determining the sea distance according to the photovoltaic equipment data and the topographic data, carrying out weight distribution on the water vapor salt fog and the attached salt fog based on the sea distance, and calculating the radiation intensity loss rate influenced by the salt fog;
The model reconstruction module is used for correcting the primary radiation intensity result by the loss rate of the radiation intensity affected by the salt fog, and reconstructing a solar radiation intensity model according to the corrected result;
the radiation prediction module is used for predicting radiation intensity by using the reconstructed solar radiation intensity model to obtain a final radiation intensity result;
And the power prediction module is used for predicting the photovoltaic power generation power according to the final radiation intensity result.
In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements the steps of a photovoltaic apparatus generated power prediction method according to the first aspect.
In a fourth aspect, the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in a method for predicting generated power of a photovoltaic device according to the first aspect when executing the program.
Compared with the prior art, the beneficial effects of the present disclosure are:
The invention discloses a method and a system for predicting the generated power of photovoltaic equipment, which aim at the problem that the solar radiation intensity is weakened and difficult to calculate due to salt fog in different states in special natural environment and coastal areas, so that the photovoltaic generated power is not predicted accurately, and a radiation intensity prediction model is constructed by utilizing a back propagation neural network, and the radiation intensity is corrected by comprehensively considering the salt fog containing salt vapor and the attached salt fog, so that a more accurate photovoltaic generated power prediction result is obtained.
According to the invention, when the weakening condition of salt fog on solar radiation intensity is considered, the influence of the sea distance on the salt fog form is fully analyzed, and the weight distribution calculation is added, so that the radiation intensity loss condition is accurately obtained.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain and do not limit the disclosure.
Fig. 1 is a flowchart of a method for predicting generated power of a photovoltaic device according to the present disclosure.
Detailed Description
The invention will be further described with reference to the drawings and examples.
Example 1
As shown in fig. 1, the embodiment discloses a method for predicting generated power of photovoltaic equipment, which includes:
S1: acquiring photovoltaic equipment data, illumination related data and topographic data;
S2: constructing a solar radiation intensity model based on a back propagation neural network according to the acquired data, predicting the current solar radiation intensity, and obtaining a preliminary radiation intensity result;
S3: determining the sea distance according to the photovoltaic equipment data and the topographic data, distributing weights of the water vapor salt fog and the attached salt fog based on the sea distance, and calculating the radiation intensity loss rate influenced by the salt fog;
S4: correcting the primary radiation intensity result by the loss rate of the radiation intensity influenced by the salt fog, and reconstructing a solar radiation intensity model according to the correction result;
s5: predicting the radiation intensity by using the reconstructed solar radiation intensity model to obtain a final radiation intensity result;
S6: and predicting the photovoltaic power generation power according to the final radiation intensity result.
In S1, the photovoltaic device data includes: photovoltaic equipment position coordinates, photovoltaic panel area, photovoltaic panel efficiency, photovoltaic controller and battery charge-discharge efficiency's power efficiency.
The illumination related data includes: solar radiation intensity, temperature information and cloud cover information acquired by a meteorological station.
The terrain data includes: elevation, slope direction and coastline coordinates. The topography height is in particular the height relative to sea level.
In S2, the specific process of constructing the solar radiation intensity model based on the back propagation neural network according to the acquired data is as follows:
And determining radiation intensity influence factors, dividing the areas according to the distance from the photovoltaic equipment to the sea, and acquiring radiation intensity influence factor data of the photovoltaic equipment in each area. The radiation intensity influence factors comprise illumination intensity, shielding condition and sea distance.
Specifically, the illumination intensity is the intensity of solar radiation acquired from an off-shore meteorological site. The shielding condition comprises cloud shielding and tree shadow shielding. The shielding condition is counted by the number and the area of the barriers in the southeast, the southwest and the northwest directions of the photovoltaic equipment, when the number and the area of the barriers of a certain photovoltaic equipment are calculated, other photovoltaic equipment with shielding influence on the photovoltaic equipment are used as the barriers, and meanwhile, other shielding objects such as other mountain bodies, buildings and the like are also counted as the barriers. The calculation mode of the offshore distance is that the offshore distance classification is carried out on the photovoltaic equipment by adopting a k-means clustering algorithm according to the coastline direction, the coordinates and the coordinates of the photovoltaic equipment. Photovoltaic devices of similar distance are classified into one category. The sea distance classification is used to obtain preliminary radiation intensity results.
S3, determining the sea distance according to the photovoltaic equipment data and the topographic data, wherein the sea distance is specifically:
S3101: collecting position coordinates and coastline coordinates of the photovoltaic equipment, and generating a coastline coordinate data set;
s3102: traversing the coastline coordinate data set based on the position coordinates of the photovoltaic equipment to obtain coastline coordinates closest to the position coordinates of the photovoltaic equipment;
S3103: and calculating the shortest distance between the position coordinates of the photovoltaic equipment and the coastline coordinates closest to the position coordinates as the sea distance of the photovoltaic equipment.
It should be understood that the coordinates are longitude and latitude coordinates.
In S3102, traversing the coastline coordinate data set based on the photovoltaic device position coordinates specifically includes:
S31021: setting a maximum value as an initial nearest distance between the photovoltaic equipment and a coastline;
S31022: calculating a first distance between the photovoltaic device position coordinates and a first set of coastline coordinates from the first set of coastline coordinates of the coastline coordinate data set;
S31023: if the first distance is smaller than the initial nearest distance, updating the initial nearest distance with the first distance, otherwise, reserving the initial nearest distance; and calculating a second set of coastline coordinates that is moved forward to the coastline coordinate data set until the coastline coordinate data set is traversed.
S3, weight distribution is carried out on the water vapor salt fog and the attached salt fog based on the distance from the sea, and the radiation intensity loss influenced by the salt fog is calculated, specifically:
S3201: according to the sea distance, weight is distributed to the water vapor salt fog and the attached salt fog;
s3202: acquiring the loss rate of the water vapor salt mist-radiation intensity and the loss rate of the attached salt mist-radiation intensity;
s3203: and calculating the loss rate of the salt fog affecting the radiation intensity according to the weight and the loss rate.
In S3201, the specific process of assigning weights to the water vapor salt mist and the attached salt mist according to the sea distance is as follows:
S32011: determining a weight distribution strategy of the water vapor salt fog and the attached salt fog according to the sea distance;
S32012: when the photovoltaic equipment is close to the sea, a small weight is distributed to the water vapor salt mist, and a large weight is distributed to the attached salt mist;
s32013: when the photovoltaic equipment is far away from the sea, a large weight is distributed for the water vapor salt fog, and a small weight is distributed for the attached salt fog.
It is known that the salt mist concentration decreases with increasing distance from the sea, i.e. the salt mist is deposited on the surface of the object in an offshore area and exists in a form of adhering salt mist; in the open sea area, can be suspended in the air for a long time, the water vapor salt fog exists mostly. Therefore, based on the distance factor, the weight is assigned to the salt mist of the two forms.
It should be understood that the value of the weight and the weight increment set for different sea distances can be customized according to actual needs by a person skilled in the art, and the customized setting is realized by adopting a conventional algorithm in the art.
And because the weakening effect of the salt fog with two forms on the radiation absorption intensity of the photovoltaic panel is different: the salt mist is attached to form a covering layer on the surface of the photovoltaic panel, so that the absorption efficiency of the photovoltaic panel to illumination is affected; the vapor salt mist directly affects the radiation path. Therefore, the weakening loss rate of the salt mist of different forms on the radiation intensity, namely the water vapor salt mist-radiation intensity loss rate and the attached salt mist-radiation intensity loss rate, is set based on different influences of different forms.
The loss rate of the water vapor salt fog-radiation intensity is the absorption radiation coefficient of aerosolAre available to those skilled in the art;
The adhering salt mist-radiation intensity loss rate Can be calculated by the following formula:
wherein, For the solar radiation intensity of clean photovoltaic panel without coating layer,/>Solar radiation intensity of the photovoltaic panel attached with salt mist; /(I)For measuring solar radiation intensity in clean photovoltaic panel without coating layer,/>Measuring the solar radiation intensity outside the clean photovoltaic panel without the coating; /(I)In-panel solar radiation intensity measurement for salt mist attached photovoltaic panel,/>The solar radiation intensity is measured outside the photovoltaic panel attached with salt mist.
S3203, calculating the loss rate of the salt fog affecting the radiation intensity according to the weight and the loss rateThe method comprises the following steps:
wherein, Is the weight value of water vapor salt fog,/>The weight of the attached salt fog is the weight of the attached salt fog.
S4, correcting the primary radiation intensity result by the loss rate of the radiation intensity influenced by the salt fog, and reconstructing a solar radiation intensity model according to the correction result, wherein the method specifically comprises the following steps:
wherein, For the primary radiation intensity,/>Is the corrected solar radiation intensity.
And retraining the back propagation neural network, and adjusting the structure and the weight of the back propagation neural network to obtain a reconstructed solar radiation intensity model.
And S6, constructing a photovoltaic power prediction model based on the long-term and short-term memory network, and predicting the photovoltaic power generation power.
The long-term and short-term memory network comprises an input door, a forgetting door and an output door, and is used for inputting photovoltaic power influence factors, such as solar radiation intensity, air pressure, temperature, humidity, cloud quantity and the like. And extracting and analyzing the characteristics through forward propagation to finally obtain the predicted photovoltaic power. Wherein the solar radiation intensity is predicted from a previously reconstructed solar radiation intensity model.
Compared with the prior art, the method considers the influence of salt fog, which is a special natural environment, on the photovoltaic power generation power, integrates the diffused salt-containing water vapor in the air and salt fog particles attached to the surface of the photovoltaic panel, and corrects the actually received radiation intensity under the weakening condition of solar radiation absorption of photovoltaic equipment, thereby improving the accuracy of photovoltaic power generation power prediction.
Example two
The embodiment provides a photovoltaic device generated power prediction system, including:
The data acquisition module is used for acquiring photovoltaic equipment data, illumination related data and topographic data;
the preliminary prediction module is used for constructing a radiation intensity model based on a back propagation neural network according to the acquired data, predicting the current radiation intensity and obtaining a preliminary radiation intensity prediction result;
The salt fog effect module is used for determining topography information and the sea distance of the photovoltaic equipment according to the topography information, and determining salt fog influence superposition data based on the topography information and the sea distance;
The model reconstruction module is used for correcting the primary radiation intensity measurement result according to the salt fog influence superposition data and reconstructing the radiation intensity model according to the correction result of radiation intensity prediction;
The radiation prediction module is used for predicting radiation intensity by using the reconstructed radiation intensity model to obtain a final radiation intensity prediction result;
And the power prediction module is used for predicting the photovoltaic power generation power through a photovoltaic power prediction model according to the final radiation intensity prediction result.
Example III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in a photovoltaic apparatus generated power prediction method as described in the above embodiment.
Example IV
The present embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the program to implement the steps in a method for predicting generated power of a photovoltaic device according to the foregoing embodiment.
The steps or modules in the second to fourth embodiments correspond to the first embodiment, and the detailed description of the first embodiment may be referred to in the related description section of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media including one or more sets of instructions; it should also be understood to include any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any one of the methods of the present invention.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A photovoltaic device generated power prediction method, comprising:
Acquiring photovoltaic equipment data, illumination related data and topographic data;
Constructing a solar radiation intensity model based on a back propagation neural network according to the acquired data, predicting the current solar radiation intensity, and obtaining a preliminary radiation intensity result; the specific process is as follows: determining a radiation intensity influence factor, and acquiring radiation intensity influence factor data of the photovoltaic equipment; dividing regions according to the distance from sea, constructing a back propagation neural network of each region and training; the trained back propagation neural network is used as a solar radiation intensity model to predict the solar radiation intensity of the radiation intensity influence factor data, so as to obtain a preliminary radiation intensity result;
Determining the sea distance according to the photovoltaic equipment data and the topographic data, distributing weights of the water vapor salt fog and the attached salt fog based on the sea distance, and calculating the radiation intensity loss rate influenced by the salt fog;
correcting the primary radiation intensity result by the loss rate of the radiation intensity influenced by the salt fog, and reconstructing a solar radiation intensity model according to the correction result;
Predicting the radiation intensity by using the reconstructed solar radiation intensity model to obtain a final radiation intensity result;
And predicting the photovoltaic power generation power according to the final radiation intensity result.
2. The method for predicting the generated power of a photovoltaic device of claim 1, wherein the illumination-related data comprises: solar radiation intensity, temperature information and cloud cover information acquired by local meteorological sites.
3. The method for predicting the generated power of a photovoltaic device of claim 1, wherein the radiation intensity influencing factors comprise illumination intensity, shielding condition, and sea distance.
4. The method for predicting the generated power of a photovoltaic device according to claim 1, wherein the training process of the back propagation neural network is as follows:
And constructing a back propagation neural network architecture, and performing weight training on the back propagation neural network by adopting radiation intensity influence factor data to obtain a trained back propagation neural network.
5. The method for predicting the generated power of the photovoltaic equipment according to claim 1, wherein the specific steps of calculating the radiation intensity loss affected by the salt mist are as follows:
according to the sea distance, weight is distributed to the water vapor salt fog and the attached salt fog;
acquiring the loss rate of the water vapor salt mist-radiation intensity and the loss rate of the attached salt mist-radiation intensity;
and calculating the loss rate of the salt fog affecting the radiation intensity according to the weight and the loss rate.
6. The method for predicting the generated power of a photovoltaic device according to claim 1, wherein the photovoltaic power prediction model is constructed based on a long-term and short-term memory network.
7. A photovoltaic device generated power prediction system, comprising:
The data acquisition module is used for acquiring photovoltaic equipment data, illumination related data and topographic data;
the preliminary prediction module is used for constructing a solar radiation intensity model based on a back propagation neural network according to the acquired data, predicting the current solar radiation intensity and obtaining a preliminary radiation intensity result;
The salt fog calculation module is used for determining the sea distance according to the photovoltaic equipment data and the topographic data, carrying out weight distribution on the water vapor salt fog and the attached salt fog based on the sea distance, and calculating the radiation intensity loss influenced by the salt fog;
the model reconstruction module is used for correcting the primary radiation intensity result by the loss of the radiation intensity influenced by the salt fog, and reconstructing a solar radiation intensity model according to the corrected result;
the radiation prediction module is used for predicting radiation intensity by using the reconstructed solar radiation intensity model to obtain a final radiation intensity result;
And the power prediction module is used for predicting the photovoltaic power generation power according to the final radiation intensity result.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of a photovoltaic apparatus generated power prediction method according to any one of claims 1-6.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of a method for predicting generated power of a photovoltaic device according to any one of claims 1-6 when the program is executed by the processor.
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| WO2023024822A1 (en) * | 2021-08-24 | 2023-03-02 | 领鞅科技(杭州)有限公司 | Low-cost high-precision measurement method for solar radiation |
| CN117254447A (en) * | 2023-08-11 | 2023-12-19 | 华能新能源股份有限公司山西分公司 | Photovoltaic power generation rate prediction method and system considering haze |
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