CN117522156A - Distributed photovoltaic prediction evaluation method and system based on big data analysis - Google Patents
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Abstract
The invention relates to the technical field of photovoltaic power generation, in particular to a distributed photovoltaic prediction evaluation method and a system based on big data analysis.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a distributed photovoltaic prediction evaluation method and system based on big data analysis.
Background
The distributed photovoltaic technology is a technology for generating power by utilizing solar energy, and is characterized in that in various places such as buildings, parking lots, industrial areas and the like, light energy is converted into electric energy through a photovoltaic module, and building electricity is provided for the places or directly output to a commercial power system, so that the distributed photovoltaic technology has the characteristics of being green, environment-friendly and renewable.
At present, the photovoltaic power of a photovoltaic power station is often required to be predicted in the work of power grid power generation planning, unit peak shaving, frequency modulation and the like, and the power grid is timely scheduled, so that the normal operation of the power grid and the normal power utilization of a user are ensured.
In the prior art, the parameter change of the photovoltaic power generation unit along with the time is not considered in the photovoltaic prediction evaluation, so that the accuracy of the photovoltaic power generation power prediction is reduced.
Disclosure of Invention
The invention aims to provide a distributed photovoltaic prediction evaluation method and system based on big data analysis, which aim to predict the power of photovoltaic power generation more accurately.
In order to achieve the above object, in a first aspect, the present invention provides a distributed photovoltaic prediction evaluation system based on big data analysis, which includes a data collection module, a data processing module, a model generation module, a correction module, an update module, and an evaluation module, wherein the data collection module, the data processing module, the model generation module, the correction module, the update module, and the evaluation module are sequentially connected;
the data collection module is used for collecting historical environment parameters and power generation parameters related to power generation capacity;
the data processing module is used for cleaning the environmental parameter data and dividing the data into a training set and a testing set;
the model generation module is used for inputting the training set into the network model for training to obtain a prediction model;
the correction module is used for correcting the prediction model by combining the power generation parameters;
the updating module is used for updating the training set data set by adopting real-time data;
and the evaluation module is used for evaluating the power generation capacity of the photovoltaic panel in the current area and giving an improvement suggestion.
The historical environment data comprise power generation data, meteorological data and geographic information data, and the power generation parameters comprise conversion efficiency, service life and inclination angle data.
The data collection module comprises a data collection unit and a transmission unit, wherein the data collection unit is used for being connected with the environment parameter database and used for collecting historical environment data, and the transmission unit is connected with the data collection unit and used for uploading collected data to an upper computer.
The data processing module comprises a data normalization unit, a data de-duplication unit, an abnormal value processing unit and a diversity unit, wherein the data normalization unit, the data de-duplication unit, the abnormal value processing unit and the diversity unit are sequentially connected;
the data normalization unit is used for collating the data into a specified format to obtain collated data;
the data deduplication unit is used for removing duplicate data in the tidying data;
the abnormal value processing unit is used for processing abnormal data in the tidying data;
the diversity unit is used for dividing the finishing data of the processing finishing function into a training set and a testing set.
Wherein the correction module comprises a conversion attenuation degree calculation unit, an annual attenuation degree calculation unit, an inclination angle acquisition unit and a power generation capacity calculation unit
The conversion attenuation degree calculation unit is used for calculating the conversion efficiency attenuation degree of the photovoltaic panel based on the illumination intensity and the receiving intensity of the photovoltaic unit;
the service life attenuation degree calculation unit is used for obtaining service life attenuation data of the photovoltaic panel;
the inclination angle acquisition unit is used for acquiring inclination angle data of the photovoltaic panel;
the power generation capacity calculation unit is used for calculating a power generation capacity correction value based on the conversion efficiency attenuation degree, the service life attenuation data and the inclination angle data.
The updating module comprises a real-time data acquisition unit, a replacement data judging unit and an updating unit, wherein the real-time data acquisition unit, the replacement data judging unit and the updating unit are sequentially connected, the real-time data acquisition unit comprises a data acquisition unit, a weather sensor and a photovoltaic array sensor and is used for acquiring data in real time, the replacement data judging unit is used for judging the storage time of historical data in a database, and when the storage date exceeds a preset value, the storage time is judged to be expired data, and the updating unit is used for updating the expired data.
The evaluation module comprises a partitioning unit, a maximum loss calculation unit, a cost calculation unit and an adjustment unit, wherein the partitioning unit is used for partitioning a current area and acquiring the total-day illumination intensity of each block in a preset time period;
the maximum loss calculation unit is used for traversing the ratio of the receiving intensity of the photovoltaic unit to all illumination intensities to obtain a maximum loss value;
the cost calculation unit is used for calculating the difference value between the adjustment cost and the maximum loss;
and the adjusting unit is used for adjusting the parameters of the photovoltaic unit when the cost is smaller than the loss.
In a second aspect, the present invention further provides a distributed photovoltaic prediction evaluation method based on big data analysis, including: collecting historical environmental parameters and power generation parameters related to power generation;
cleaning environmental parameter data, and dividing the data into a training set and a testing set;
inputting the training set into a network model for training to obtain a prediction model;
correcting the prediction model by combining the power generation parameters;
updating the training set data set by adopting real-time data;
the current area photovoltaic panel was evaluated for power generation capability and an improvement suggestion was given.
According to the distributed photovoltaic prediction evaluation method and system based on big data analysis, historical environment parameters related to generated energy in distributed photovoltaic power generation in a target area, including power generation data, meteorological data and geographic information data, can be collected through the data collection module, the power generation parameters include conversion efficiency, service life and inclination angle data, so that the environment parameter data can be cleaned through the data processing module to improve the effectiveness of the data, and then the data are divided into a training set and a test set, and model training is carried out through the model generation module, wherein the model training can be carried out through the steps of selecting model types, defining algorithm formulas and defining loss functions through a deep learning technology, then the correction module is used for correcting an obtained prediction model through combining the power generation parameters of a photovoltaic unit, the database used for training is updated through the updating module, the power of photovoltaic power generation can be predicted more accurately, and finally the effectiveness of the power generation capacity can be evaluated through the evaluation module, and improved suggestions are provided, so that the power generation efficiency can be optimized conveniently, and the power generation efficiency can be improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a block diagram of a distributed photovoltaic prediction evaluation system based on big data analysis according to a first embodiment of the present invention.
Fig. 2 is a block diagram of a data collection module according to a second embodiment of the present invention.
Fig. 3 is a block diagram of a data processing module of a second embodiment of the present invention.
Fig. 4 is a block diagram of a correction module according to a second embodiment of the present invention.
Fig. 5 is a block diagram of an evaluation module according to a second embodiment of the present invention.
Fig. 6 is a block diagram of a partition unit according to a second embodiment of the present invention.
Fig. 7 is a flow chart of a distributed photovoltaic prediction assessment method based on big data analysis according to a third embodiment of the present invention.
The data collection module 101, the data processing module 102, the model generation module 103, the correction module 104, the update module 105, the evaluation module 106, the data acquisition unit 201, the transmission unit 202, the data normalization unit 203, the data deduplication unit 204, the outlier processing unit 205, the diversity unit 206, the real-time data acquisition unit 207, the replacement data judgment unit 208, the update unit 209, the partition unit 210, the maximum loss calculation unit 211, the cost calculation unit 212, the adjustment unit 213, the region division subunit 214, the illumination intensity calculation subunit 215, and the matching unit 216.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
First embodiment
Referring to fig. 1, the invention provides a distributed photovoltaic prediction evaluation system based on big data analysis, which comprises a data collection module 101, a data processing module 102, a model generation module 103, a correction module 104, an update module 105 and an evaluation module 106, wherein the data collection module 101, the data processing module 102, the model generation module 103, the correction module 104, the update module 105 and the evaluation module 106 are sequentially connected; the data collection module 101 is used for collecting historical environmental parameters and power generation parameters related to power generation capacity; the data processing module 102 is configured to clean environmental parameter data and divide the data into a training set and a testing set; the model generating module 103 is configured to input a training set into a network model for training, so as to obtain a prediction model; the correction module 104 is configured to correct the prediction model in combination with the power generation parameter; the updating module 105 is configured to update the training set data set with real-time data; the evaluation module 106 is configured to evaluate the power generation capability of the photovoltaic panel in the current area and make an improvement suggestion.
In this embodiment, the data collecting module 101 may collect historical environmental parameters related to the generated energy in the distributed photovoltaic power generation in the target area, including power generation data, meteorological data and geographic information data, and the power generation parameters include conversion efficiency, service life and inclination angle data, so that the environmental parameter data may be cleaned by the data processing module 102 to improve the validity of the data, and then divided into a training set and a test set, and the model generating module 103 is used to perform model training, where the model training may use a deep learning technology to perform training through the steps of selecting a model type, defining an algorithm formula and defining a loss function, and then the correction module 104 is used to correct the obtained prediction model in combination with the power generation parameters of the photovoltaic unit itself, and the update module 105 is used to update the database used for training, so that the power of photovoltaic power generation may be predicted more accurately, and finally the evaluation module 106 may evaluate the validity of the power generation capability and provide an improvement suggestion, so that the power generation efficiency may be optimized conveniently, thereby improving the power generation efficiency.
Second embodiment
Referring to fig. 2-6, fig. 2 is a block diagram of a data collection module according to a second embodiment of the present invention. Fig. 3 is a block diagram of a data processing module of a second embodiment of the present invention. Fig. 4 is a block diagram of a correction module according to a second embodiment of the present invention. Fig. 5 is a block diagram of an evaluation module according to a second embodiment of the present invention. Fig. 6 is a block diagram of a partition unit according to a second embodiment of the present invention.
On the basis of the first embodiment, the invention further provides a distributed photovoltaic prediction evaluation system based on big data analysis, the data collection module 101 comprises a data collection unit 201 and a transmission unit 202, the data collection unit 201 is used for being connected with an environmental parameter database and used for collecting historical environmental data, and the transmission unit 202 is connected with the data collection unit 201 and used for uploading collected data to an upper computer.
The data processing module 102 comprises a data normalization unit 203, a data deduplication unit 204, an outlier processing unit 205 and a diversity unit 206, wherein the data normalization unit 203, the data deduplication unit 204, the outlier processing unit 205 and the diversity unit 206 are sequentially connected; the data normalization unit 203 is configured to sort data into a specified format, so as to obtain sorted data; the data deduplication unit 204 is configured to remove duplicate data in the consolidated data; the outlier processing unit 205 is configured to process outlier data in the consolidated data; the diversity unit 206 is configured to divide the finishing data of the finishing function into a training set and a testing set. By the method, the collected data can be normalized, so that repeated data in the data and abnormal data with obviously unreasonable data values can be found, the data can be processed to improve the accuracy of the data, and then the data is divided into corresponding sets through the diversity unit 206 to facilitate modeling.
The correction module 104 comprises a conversion attenuation degree calculation unit, an annual attenuation degree calculation unit, an inclination angle acquisition unit and a power generation capacity calculation unit; the conversion attenuation degree calculation unit is used for calculating the conversion efficiency attenuation degree of the photovoltaic panel based on the illumination intensity and the receiving intensity of the photovoltaic unit; the service life attenuation degree calculation unit is used for obtaining service life attenuation data of the photovoltaic panel; the inclination angle acquisition unit is used for acquiring inclination angle data of the photovoltaic panel; the power generation capacity calculation unit is used for calculating a power generation capacity correction value based on the conversion efficiency attenuation degree, the service life attenuation data and the inclination angle data. The photovoltaic unit can receive the influence of numerous factors in the use, for example because the photovoltaic power generation unit surface covers the light intensity decline that the dust leads to, and the life can lead to the device ageing too long to and the selection of inclination can influence light receiving efficiency etc. to lead to the change along with the time, the parameter of power generation unit itself also changes, consequently influences the accuracy of prediction, and this application adds these parameters and revises the model, in order to improve the prediction accuracy.
The updating module 105 includes a real-time data obtaining unit 207, a replacement data judging unit 208, and an updating unit 209, where the real-time data obtaining unit 207, the replacement data judging unit 208, and the updating unit 209 are sequentially connected, the real-time data obtaining unit 207 includes a data collector, a weather sensor, and a photovoltaic array sensor, and is configured to collect data in real time, the replacement data judging unit 208 is configured to judge a storage time of historical data in a database, and judge that the data is expired when a storage date exceeds a preset value, and the updating unit 209 is configured to update the expired data. By the method, the data in the database can be conveniently updated, so that more accurate prediction can be conveniently made in a period of time.
The evaluation module 106 includes a partition unit 210, a maximum loss calculation unit 211, a cost calculation unit 212, and an adjustment unit 213, where the partition unit 210 is configured to block a current area and obtain an all-day illumination intensity of each block within a preset period of time; the maximum loss calculation unit 211 is configured to traverse the ratio of the received intensity of the photovoltaic unit to all the illumination intensities to obtain a maximum loss value; the cost calculation unit 212 is configured to calculate a difference between the adjustment cost and the maximum loss; the adjusting unit 213 is configured to adjust parameters of the photovoltaic unit when the cost is less than the loss. The current power generation area can be divided into grids by the partition unit 210, and the difference between the receiving intensity and the expected intensity of the photovoltaic units in the grids is calculated to obtain the maximum loss value, and then the cost calculation unit 212 is used for calculating whether the parameter of the photovoltaic power generation unit is greater than the loss or not in one period, and if the parameter is greater than the loss, the adjustment can be performed, so that the economic benefit of power generation can be improved.
The partition unit 210 includes a region dividing subunit 214, an illumination intensity calculating subunit 215, and a matching unit 216, where the region dividing subunit 214 is configured to divide a target region to obtain a region grid, the illumination intensity calculating subunit 215 is configured to obtain an illumination intensity of the whole day in the grid and calculate an average intensity, and the matching unit 216 is configured to match and correspond the average intensity to the grid.
The adjusting unit 213 includes a position adjusting subunit, a cleaning subunit and an inclination angle adjusting subunit, where the position adjusting subunit is configured to swap the position of the photovoltaic unit where the photovoltaic unit is moved to the lowest loss value; the cleaning subunit is used for cleaning the surface of the photovoltaic unit, and the inclination angle adjusting subunit is used for adjusting the inclination angle of the photovoltaic unit.
The specific mode of moving the photovoltaic units to the position of the photovoltaic unit with the lowest loss value is to acquire the moving routes of all the photovoltaic units needing to be moved, optimize the moving routes based on the contact ratio of the moving routes, and swap the photovoltaic units based on the optimized moving routes.
Third embodiment
Referring to fig. 7, fig. 7 is a flowchart of a distributed photovoltaic prediction estimation method based on big data analysis according to a third embodiment of the present invention. On the basis of the first embodiment, the invention also provides a distributed photovoltaic prediction evaluation method based on big data analysis, which comprises the following steps:
s101, collecting historical environment parameters and power generation parameters related to power generation;
s102, cleaning environmental parameter data, and dividing the data into a training set and a testing set;
s103, inputting the training set into a network model for training to obtain a prediction model;
s104, correcting the prediction model by combining the power generation parameters;
s105, updating the training set data set by adopting real-time data;
s106 evaluates the power generation capability of the current area photovoltaic panel and gives an improvement suggestion.
Historical environmental parameters related to the generated energy in the distributed photovoltaic power generation in the target area, including power generation data, meteorological data and geographic information data, can be collected through the data collection module 101, and the power generation parameters include conversion efficiency, service life and inclination angle data, so that the environmental parameter data can be cleaned through the data processing module 102 to improve the effectiveness of the data, then the data are divided into a training set and a test set, and model training is performed through the model generation module 103, wherein the model training can be performed through the steps of selecting model types, defining algorithm formulas and defining loss functions through a deep learning technology, then the correction module 104 is used for correcting an obtained prediction model through combining the power generation parameters of the photovoltaic unit, the database used for training is updated through the update module 105, the power of the photovoltaic power generation can be predicted more accurately, finally the effectiveness of the power generation capacity can be estimated through the evaluation module 106, and an improvement suggestion is provided, so that the power generation network can be optimized conveniently, and the efficiency is improved.
The above disclosure is only a preferred embodiment of the present invention, and it should be understood that the scope of the invention is not limited thereto, and those skilled in the art will appreciate that all or part of the procedures described above can be performed according to the equivalent changes of the claims, and still fall within the scope of the present invention.
Claims (8)
1. A distributed photovoltaic predictive evaluation system based on big data analysis is characterized in that,
the system comprises a data collection module, a data processing module, a model generation module, a correction module, an updating module and an evaluation module, wherein the data collection module, the data processing module, the model generation module, the correction module, the updating module and the evaluation module are sequentially connected;
the data collection module is used for collecting historical environment parameters and power generation parameters related to power generation capacity;
the data processing module is used for cleaning the environmental parameter data and dividing the data into a training set and a testing set;
the model generation module is used for inputting the training set into the network model for training to obtain a prediction model;
the correction module is used for correcting the prediction model by combining the power generation parameters;
the updating module is used for updating the training set data set by adopting real-time data;
and the evaluation module is used for evaluating the power generation capacity of the photovoltaic panel in the current area and giving an improvement suggestion.
2. The distributed photovoltaic predictive evaluation system based on big data analysis of claim 1,
the historical environment data comprises power generation data, meteorological data and geographic information data, and the power generation parameters comprise conversion efficiency, service life and dip angle data.
3. The big data analysis based distributed photovoltaic predictive assessment system of claim 2,
the data collection module comprises a data collection unit and a transmission unit, wherein the data collection unit is used for being connected with the environment parameter database and used for collecting historical environment data, and the transmission unit is connected with the data collection unit and used for uploading collected data to an upper computer.
4. A distributed photovoltaic predictive evaluation system based on big data analysis as claimed in claim 3,
the data processing module comprises a data normalization unit, a data de-duplication unit, an outlier processing unit and a diversity unit, wherein the data normalization unit, the data de-duplication unit, the outlier processing unit and the diversity unit are sequentially connected;
the data normalization unit is used for collating the data into a specified format to obtain collated data;
the data deduplication unit is used for removing duplicate data in the tidying data;
the abnormal value processing unit is used for processing abnormal data in the tidying data;
the diversity unit is used for dividing the finishing data of the processing finishing function into a training set and a testing set.
5. The distributed photovoltaic predictive evaluation system based on big data analysis of claim 4,
the correction module comprises a conversion attenuation degree calculation unit, an annual attenuation degree calculation unit, an inclination angle acquisition unit and a power generation capacity calculation unit
The conversion attenuation degree calculation unit is used for calculating the conversion efficiency attenuation degree of the photovoltaic panel based on the illumination intensity and the receiving intensity of the photovoltaic unit;
the service life attenuation degree calculation unit is used for obtaining service life attenuation data of the photovoltaic panel;
the inclination angle acquisition unit is used for acquiring inclination angle data of the photovoltaic panel;
the power generation capacity calculation unit is used for calculating a power generation capacity correction value based on the conversion efficiency attenuation degree, the service life attenuation data and the inclination angle data.
6. The distributed photovoltaic predictive evaluation system based on big data analysis of claim 5,
the updating module comprises a real-time data acquisition unit, a replacement data judging unit and an updating unit, wherein the real-time data acquisition unit, the replacement data judging unit and the updating unit are sequentially connected, the real-time data acquisition unit comprises a data acquisition device, a weather sensor and a photovoltaic array sensor and is used for acquiring data in real time, the replacement data judging unit is used for judging the storage time of historical data in a database, and when the storage date exceeds a preset value, the storage time is judged to be expired data, and the updating unit is used for updating the expired data.
7. The big data analysis based distributed photovoltaic predictive assessment system of claim 6,
the evaluation module comprises a partition unit, a maximum loss calculation unit, a cost calculation unit and an adjustment unit, wherein the partition unit is used for partitioning the current area and acquiring the total-day illumination intensity of each block within a preset time period;
the maximum loss calculation unit is used for traversing the ratio of the receiving intensity of the photovoltaic unit to all illumination intensities to obtain a maximum loss value;
the cost calculation unit is used for calculating the difference value between the adjustment cost and the maximum loss;
and the adjusting unit is used for adjusting the parameters of the photovoltaic unit when the cost is smaller than the loss.
8. The distributed photovoltaic prediction evaluation method based on big data analysis is applied to the distributed photovoltaic prediction evaluation system based on big data analysis as claimed in any one of claims 1 to 7, and is characterized in that,
comprising the following steps: collecting historical environmental parameters and power generation parameters related to power generation;
cleaning environmental parameter data, and dividing the data into a training set and a testing set;
inputting the training set into a network model for training to obtain a prediction model;
correcting the prediction model by combining the power generation parameters;
updating the training set data set by adopting real-time data;
the current area photovoltaic panel was evaluated for power generation capability and an improvement suggestion was given.
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