KR20220077468A - Method, server and computer program for predicting solar radiation - Google Patents

Abstract

The solar radiation prediction method includes: obtaining a weather model including weather information based on a grid based on latitude and longitude; extracting at least one variable related to insolation through correlation analysis of the weather information; generating a multiple linear regression model based on the extracted at least one or more variables; generating a virtual grid by adjusting the grid of the weather model based on the multiple linear regression model; and disposing the weather information on the virtual grid, and predicting the amount of insolation based on the arranged weather information.

Description

METHOD, SERVER AND COMPUTER PROGRAM FOR PREDICTING SOLAR RADIATION

The present invention relates to a method for predicting insolation, a server and a computer program.

When a power generation operator that owns a solar power plant submits a future 36-hour generation plan to the Korea Power Exchange, the reliability of the power supplied by the solar power plant is determined according to the prediction error rate. Therefore, since the photovoltaic power plant needs to submit an accurate generation amount plan to increase the reliability of the power supply, it is necessary to accurately predict the insolation amount of the photovoltaic power plant.

As a method of predicting insolation of a solar power plant, it is possible to predict the insolation amount of the power plant from insolation information of a weather station close to the solar power plant by using triangulation method, but this conventional insolation prediction method has significantly lower prediction accuracy.

In addition, as another method of predicting the amount of insolation of a solar power plant, there are a linear regression method using data of past generation of the power plant and a method of directly installing an insolation meter. However, the linear regression method using past generation data may have a big difference from the actual insolation in the data conversion process to convert it to actual power. There is a problem in that the cost burden is large to install one by one.

In addition, in the method of directly installing the insolation meter, when the resolution of the insolation meter is low or the lifespan is short, the reliability of insolation prediction may be reduced, and maintenance costs may increase.

Korean Patent Publication No. 10-2017-0056837 (published on May 24, 2017) Korean Patent Publication No. 10-2093796 (Registered on March 20, 2020)

The present invention is to solve the problems of the prior art, and to provide a solar radiation prediction method, a server, and a computer program that can provide a solar radiation map predicted insolation amount.

In addition, an object of the present invention is to provide an insolation prediction method, a server, and a computer program capable of predicting the insolation amount of a solar power plant using a solar radiation map.

However, the technical problems to be achieved by the present embodiment are not limited to the technical problems described above, and other technical problems may exist.

As a means for achieving the above technical problem, an embodiment of the present invention provides a method for predicting insolation in a target area, the method comprising: obtaining a weather model including weather information based on a grid of latitude and longitude; extracting at least one variable related to insolation through correlation analysis of the weather information; generating a multiple linear regression model based on the extracted at least one or more variables; generating a virtual grid by adjusting the grid of the weather model based on the multiple linear regression model; and disposing the weather information on the virtual grid, and predicting the amount of insolation based on the disposed weather information.

Another embodiment of the present invention, in the solar radiation prediction server of the target area, a weather model unit for obtaining a weather model including weather information based on a grid of latitude and longitude; a variable extraction unit for extracting at least one variable related to insolation through correlation analysis of the weather information; a multiple linear regression model generator for generating a multiple linear regression model based on the extracted at least one or more variables; a virtual grid generator for generating a virtual grid by adjusting the grid of the weather model based on the multiple linear regression model; And it is possible to provide a solar radiation prediction server comprising a solar radiation forecasting unit that arranges the weather information on the virtual grid and predicts the amount of insolation based on the placed weather information.

Another embodiment of the present invention is a computer program stored in a computer-readable recording medium including a sequence of instructions for predicting the amount of insolation in a target area, wherein the computer program is executed based on latitude and longitude when executed by a computing device. Obtaining a weather model including weather information based on a grid, extracting at least one or more variables related to insolation through correlation analysis on the weather information, and a multiple linear regression model based on the extracted at least one or more variables to generate a virtual grid by adjusting the grid of the weather model based on the multiple linear regression model, placing the weather information on the virtual grid, and predicting the amount of insolation based on the placed weather information It is possible to provide a computer program stored in a computer-readable recording medium comprising a sequence of instructions.

The above-described problem solving means are merely exemplary, and should not be construed as limiting the present invention. In addition to the exemplary embodiments described above, there may be additional embodiments described in the drawings and detailed description.

According to any one of the above-described problem solving means of the present invention, the amount of insolation can be more accurately predicted by extracting and utilizing a variable highly correlated with the amount of insolation through correlation analysis and predicting the amount of insolation.

In addition, by re-adjusting the grid of the existing weather model based on latitude and longitude to a virtual grid at equal intervals, it is possible to more accurately display the predicted insolation information per unit area.

In addition, it is possible to provide insolation prediction information for predicting the amount of power generation of the power plant by using the insolation map on which the insolation prediction information is built.

1 is a configuration diagram of a solar radiation prediction server according to an embodiment of the present invention.
2 is an exemplary diagram for explaining a candidate multiple linear regression model according to an embodiment of the present invention.
3 is an exemplary diagram for calculating a regression coefficient for each grid based on a thyssen weight according to an embodiment of the present invention.
4A is a grid of a weather model according to an embodiment of the present invention, and (b) is an exemplary diagram for explaining a virtual grid according to an embodiment of the present invention.
5 is an exemplary view for explaining a virtual grid generated by adjusting the grid of a weather model according to an embodiment of the present invention.
6 is a flowchart of a method for predicting solar radiation according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement them. However, the present invention may be embodied in several different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part is "connected" with another part, this includes not only the case of being "directly connected" but also the case of being "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, it means that other components may be further included, rather than excluding other components, unless otherwise stated, and one or more other features However, it is to be understood that the existence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded in advance.

In this specification, a "part" includes a unit realized by hardware, a unit realized by software, and a unit realized using both. In addition, one unit may be implemented using two or more hardware, and two or more units may be implemented by one hardware.

Some of the operations or functions described as being performed by the terminal or device in this specification may be instead performed by a server connected to the terminal or device. Similarly, some of the operations or functions described as being performed by the server may also be performed in a terminal or device connected to the server.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a configuration diagram of a solar radiation prediction server according to an embodiment of the present invention.

Referring to FIG. 1 , the insolation prediction server 100 includes a weather model unit 110 , a variable extraction unit 120 , a multiple linear regression model generation unit 130 , a virtual grid generation unit 140 , and an insolation prediction unit 150 . ) may be included. The variable extraction unit 120 may include a candidate model generation unit 121 , a candidate prediction unit 122 , and a comparison unit 123 , and the multiple linear regression model generation unit 130 includes a grid-specific model generation unit 131 . ) may be included. However, the above components 110 to 150 are merely illustrative of components that can be controlled by the insolation prediction server 100 .

Each component of the solar radiation prediction server 100 of FIG. 1 is generally connected through a network. For example, as shown in FIG. 1 , the virtual model unit 110 , the variable extraction unit 120 , the multiple linear regression model generation unit 130 , the virtual grid generation unit 140 , and the insolation prediction unit 150 ) can be connected simultaneously or at intervals of time.

A network refers to a connection structure in which information can be exchanged between each node, such as terminals and servers, and includes a local area network (LAN), a wide area network (WAN), and the Internet (WWW: World). Wide Web), wired and wireless data communication networks, telephone networks, wired and wireless television networks, and the like. Examples of wireless data communication networks include 3G, 4G, 5G, 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), World Interoperability for Microwave Access (WIMAX), Wi-Fi, Bluetooth communication, infrared communication, ultrasound Communication, Visible Light Communication (VLC), LiFi, and the like are included, but are not limited thereto.

1, the insolation prediction server 100 obtains a weather model including weather information based on a grid of latitude and longitude standards, and through correlation analysis on the obtained weather information, variables highly correlated with insolation. Insolation can be predicted more accurately by extracting and utilizing it to predict insolation.

In addition, the insolation prediction server 100 readjusts the grid of the existing weather model based on latitude and longitude to a virtual grid at equal intervals, and uses the solar radiation map in which the insolation prediction information per unit area is constructed to predict the amount of generation of the power plant. can be more accurately provided.

Hereinafter, each configuration of the insolation prediction server 100 will be described in more detail.

The weather model unit 110 according to an embodiment of the present invention may obtain a weather model including weather information based on a grid based on latitude and longitude. For example, the weather model unit 110 may obtain a weather model including 78 types of weather information from the Korea Meteorological Administration.

For example, the weather model unit 110 may obtain a single-sided numerical forecast model as a weather model from the Korea Meteorological Administration, and extract, for example, 78 variables therefrom. Herein, it is described that the weather model unit 110 extracts 78 variables by way of example, but the present invention is not limited thereto, and the weather model unit 110 may extract a number of variables capable of accurately predicting the amount of insolation.

The weather model unit 110 may store 78 variable values for each date and time for each grid based on latitude and longitude.

The 78 types of weather information included in the weather model include weather forecast information for the future 48 hours (+0 o'clock to +48 o'clock). The 78 types of weather information may include surface shortwave radiation, direct shortwave radiation, and scattered shortwave radiation in addition to insolation information.

The variable extraction unit 120 according to an embodiment of the present invention may extract at least one variable related to the amount of insolation through correlation analysis with respect to weather information. For example, the variable extraction unit 120 may extract a variable that has a large influence on the amount of insolation from among 78 types of weather information.

The variable extractor 120 may extract at least one variable related to the amount of insolation through correlation analysis with respect to the weather information.

For example, the variable extractor 120 may extract a variable related to insolation from among 78 types of weather prediction information included in the weather model based on correlation analysis and multiple linear regression with respect to the weather information. The variable extracted by the variable extractor 120 may be used as an input variable of a solar radiation prediction model for insolation prediction.

The variable extractor 120 may perform correlation analysis for each grid based on latitude and longitude included in the weather model. Here, the correlation analysis may be a Pearson correlation analysis. The variable extractor 120 may compare Pearson correlation coefficients through Pearson correlation analysis. Here, the Pearson correlation coefficient is a numerical value obtained by quantifying the linear correlation between variables.

The candidate model generator 121 according to an embodiment of the present invention may generate a plurality of candidate multiple linear regression models based on a plurality of variable sets including at least one different variable.

2 is an exemplary diagram for explaining a candidate multiple linear regression model according to an embodiment of the present invention. Referring to FIG. 2 , the candidate model generator 121 may generate a plurality of variable sets including at least one or more different variables from at least one or more extracted variables. The candidate model generator 121 may generate, for example, six candidate multiple linear regression models based on the generated plurality of variable sets.

For example, the first candidate multiple linear regression model (211, TEST 0) is the predicted insolation, precipitation, east-west wind, north-south wind, temperature, relative humidity, visibility, dew point temperature, lowest stratum, low stratum, extracted through correlation analysis. , mesostratus and upper clouds can be used as input variables.

For the second candidate multiple linear regression model (212, TEST 1), the predicted insolation, precipitation, east-west wind, north-south wind, temperature, relative humidity, dew point temperature, lowest cloud, low-stratum cloud, meso-stratum, and upper cloud are inputted through correlation analysis. It can be used as a variable.

The third candidate multiple linear regression model 213 ( TEST2 ) may use the predicted insolation, lowest stratum, low stratum, mesostratum, and upper stratum extracted through correlation analysis as input variables.

The fourth candidate multiple linear regression model 214 ( TEST3 ) may use predicted insolation, lowest cloud, lower cloud, middle cloud, upper cloud, and precipitation extracted through correlation analysis as input variables.

The fifth candidate multiple linear regression model (215, TEST4) may use the predicted insolation, lowest cloud, lower cloud, middle cloud, upper cloud, and visibility extracted through correlation analysis as input variables.

The sixth candidate multiple linear regression model (216, TEST5) can use the predicted insolation, precipitation, east-west wind, north-south wind, temperature, relative humidity, visibility and dew point temperature extracted through correlation analysis as input variables.

The candidate predictor 122 according to an embodiment of the present invention may predict solar radiation based on a plurality of candidate multiple linear regression models. For example, the candidate predictor 122 may predict solar radiation through multiple linear regression analysis for each of the plurality of candidate multiple linear regression models 211 to 216 . Also, the candidate predictor 122 may predict the amount of insolation for each time through time-based linear regression analysis for each of the plurality of candidate multiple linear regression models 211 to 216 .

The comparator 123 according to an embodiment of the present invention may compare the insolation predicted based on a plurality of candidate multiple linear regression models with the actual insolation based on the weather model.

For example, the comparator 123 may compare the actual insolation 221 and X_RAW based on the weather model with the predicted insolation 222 and X_NEW based on the candidate multiple linear regression model.

The multiple linear regression model generator 130 according to an embodiment of the present invention may generate a multiple linear regression model based on at least one extracted variable. For example, the multiple linear regression model generator 130 may generate a candidate multiple linear regression model with the most similar insolation to actual insolation as a multiple linear regression model. In this way, more accurate insolation prediction information can be generated by extracting and utilizing a variable highly correlated with insolation to generate a multiple linear regression model.

For example, the multiple linear regression model generation unit 130 compares the insolation amount predicted by the comparison unit 123 based on the plurality of candidate multiple linear regression models 211 to 216 with the insolation amount based on the weather model, and the insolation prediction rate The fourth candidate multiple linear regression model 214 ( TEST3 ) confirmed to have the highest value may be selected as the multiple linear regression model. Here, the set of variables used in the selected multiple linear regression model may include, for example, at least one of predicted insolation, stratus cloud, stratus cloud, mesocloud, stratus cloud, and precipitation data. Equation 1 below represents the selected multiple linear regression model.

<Equation 1>

Here, 'n' denotes a grid number based on latitude and longitude, and 'ß' denotes a regression coefficient of each variable.

The grid-specific model generator 131 calculates the regression coefficient of each of at least one or more variables for each grid based on the distance weight and the thyssen weight based on the distance between the weather station and the target region for the meteorological model, thereby generating a multi-linear regression model for each grid. can create

For example, the grid-specific model generator 131 may calculate a regression coefficient of each of the at least one or more variables for each grid based on latitude and longitude in Equation 1 selected based on the multiple linear regression model.

For example, the grid-specific model generator 131 may calculate a regression coefficient ß of each variable for each grid n based on latitude and longitude based on Equation 2 below.

<Equation 2>

Here, when 'distance weight + thyssen weight' is '1', the corresponding grid means a weather station.

3 is an exemplary diagram for calculating a regression coefficient for each grid based on a thyssen weight according to an embodiment of the present invention.

The thyssen method is a method of calculating an average value by assigning a weight to the area ratio of a thyssen polygon drawn around a reference point. The grid-specific model generator 131 may construct a thyssen polygon based on ß _n (300) in order to calculate ß _n (300) by using the Thyssen method.

For example, the grid-by-lattice model generator 2131 shows an inner triangle based on ß _n (300) in the thyssen polygon, and sets the thyssen polygon based on the inner triangle as three regions inside the thyssen polygon and four outside the thyssen polygon. can be divided into areas. The grid-by-lattice model generation unit 131, for example, sets the thyssen polygons A _{1 inside} (311), A _{1 outside} (321), A _{2 inside} (312), A _{2 outside} (322), A _{3 inside} ( 313) and A _{3 outside} (323).

In this case, the model generating unit 131 for each grid may derive ß _{n thyssen} in which only the thyssen weight for a specific grid 'n' is reflected, for example, based on Equation 3 below.

<Equation 3>

The grid-specific model generator 131 may derive ß _n in which both the thyssen weight W ₁ and the distance weight W ₂ for a specific grid 'n' are reflected, for example, based on Equation 4 below.

<Equation 4>

The virtual grid generator 140 according to an embodiment of the present invention may generate a virtual grid by adjusting the grid of the weather model based on the selected multiple linear regression model. For example, the virtual grid generator 140 may adjust the grid of the weather model based on a multi-linear regression model for each grid based on latitude and longitude.

4A is a grid of a weather model according to an embodiment of the present invention, and (b) is an exemplary diagram for explaining a virtual grid according to an embodiment of the present invention.

Referring to FIG. 4A , the grid of the meteorological model is formed based on general latitude and longitude standards used in the earth model. However, the unit of solar radiation is expressed per unit area as W/m ² , and thus, the unit area of the meteorological model needs to be constant. That is, when the meteorological model shown in FIG. 4 (a) is used as it is in deriving the insolation, the unit area of the grid becomes wider as the latitude is closer to 0 degrees (A), so the insolation may be displayed inaccurately.

Accordingly, the virtual grid generating unit 140 may generate a virtual grid obtained by adjusting the grid of the weather model, as shown in FIG. 4B . Here, the virtual grid may be equally spaced. For example, the virtual grid generator 140 may generate an equally spaced virtual grid based on 1.5 km based on the multi-linear regression model for each grid.

5 is an exemplary view for explaining a virtual grid generated by adjusting the grid of a weather model according to an embodiment of the present invention. Referring to FIG. 5 , the virtual grid generating unit 140 may adjust the grid of the weather model at equal intervals, for example, based on the regression coefficient ß _n of the multi-linear regression model for each grid.

For example, the virtual grid generating unit 140 is based on the areas of A1 ( 511 ), A2 ( 512 ), A4 ( 513 ), and A5 ( 514 ), which are grids of the weather model overlapping the equally spaced grid N1 ( 520 ). Thus, a regression coefficient for N1 520 may be calculated. For example, the virtual grid generator 140 may calculate a regression coefficient for N1 520 based on Equation 5 below.

<Equation 5>

where ß _N1 is are the regression coefficients for N1(520), ß _A1 to ß _A4 are the regression coefficients for A1(511), A2(512), A4(513), A5(514), respectively, and W ₁ to W ₄ are A1( 511), A2(512), A4(513), and A5(514) are weights based on a ratio of an area overlapping with N1(520) to the total area of each.

The insolation prediction unit 150 according to an embodiment of the present invention may arrange weather information on a virtual grid and predict solar radiation based on the disposed weather information.

The insolation predictor 150 may map the grid of the weather model and the generated virtual grid based on latitude and longitude, and rearrange the weather information of the weather model to the virtual grid. The insolation predictor 150 may extract a variable highly correlated with insolation from the rearranged weather information through correlation analysis, and build solar radiation prediction information per virtual grid unit based on the extracted variable.

For example, the insolation prediction unit 150 generates solar radiation generation forecast information per virtual grid unit at 6-hour intervals (eg, UTC standard, 00:00, 06:00, 12:00, 18:00) 4 times a day (eg, prediction for 48 hours) , +0 hours to +48 hours) can be built. As another example, the insolation prediction unit 150 may build solar radiation prediction information of the power plant by matching the latitude and longitude of the power plant with the virtual grid. Insolation forecast information of power plants can be used as data when bidding for power generation at the power exchange.

According to the present invention, by constructing the insolation prediction information of the power plant based on the virtual grid at equal intervals, it is possible to accurately compare the insolation amount between power plants located at different locations.

6 is a flowchart of a method for predicting solar radiation according to an embodiment of the present invention. The method of predicting the amount of insolation shown in FIG. 6 includes steps of time-series processing according to the embodiments shown in FIGS. 1 to 5 . Therefore, even if omitted below, it is also applied to the method of predicting the amount of insolation in the insolation prediction server 100 according to the embodiment shown in FIGS. 1 to 5 .

In step S610, the insolation prediction server 100 may obtain a weather model including weather information based on a grid of latitude and longitude criteria.

In step S620, the insolation prediction server 100 may extract at least one or more variables related to insolation through correlation analysis of the weather information.

In step S630, the solar radiation prediction server 100 may generate a multiple linear regression model based on the extracted at least one or more variables.

In step S640, the insolation prediction server 100 may generate a virtual grid by adjusting the grid of the weather model based on the multiple linear regression model (finally determined). The solar radiation prediction server 100 may include a virtual grid generator 140 .

In step S650, the solar radiation prediction server 100 may arrange the weather information on the virtual grid, and predict the solar radiation amount based on the disposed weather information. The insolation prediction server 100 may include an insolation prediction unit 150 .

In the above description, steps S610 to S650 may be further divided into additional steps or combined into fewer steps, according to an embodiment of the present invention. In addition, some steps may be omitted as needed, and the order between the steps may be switched.

The method of predicting insolation in the insolation prediction server 100 described through FIGS. 1 to 6 is a computer program stored in a computer readable recording medium executed by a computer or a form of a recording medium including instructions executable by the computer can also be implemented. In addition, the method of predicting insolation in the insolation prediction server 100 described with reference to FIGS. 1 to 6 may be implemented in the form of a computer program stored in a computer-readable recording medium executed by a computer.

A computer-readable recording medium may be any available medium that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer-readable recording medium may include a computer storage medium. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

The above description of the present invention is for illustration, and those of ordinary skill in the art to which the present invention pertains can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a dispersed form, and likewise components described as distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

100: Insolation prediction server
110: weather model unit
120: variable extraction unit
130: multiple linear regression model generator
140: virtual grid generation unit
150: insolation prediction unit

Claims (15)

In the method of predicting insolation in the target area,
obtaining a weather model including weather information based on a grid of latitude and longitude criteria;
extracting at least one variable related to insolation through correlation analysis of the weather information;
generating a multiple linear regression model based on the extracted at least one or more variables;
generating a virtual grid by adjusting the grid of the weather model based on the multiple linear regression model; and
Placing the weather information on the virtual grid, predicting the amount of insolation based on the arranged weather information
Including, insolation prediction method.
The method of claim 1,
The correlation analysis will be a Pearson correlation analysis, insolation prediction method.
The method of claim 1,
The step of extracting the at least one or more variables,
generating a plurality of candidate multiple linear regression models based on a plurality of variable sets including at least one different variable;
predicting insolation based on the plurality of candidate multiple linear regression models; and
and comparing the predicted insolation based on the plurality of candidate multiple linear regression models with the actual insolation based on the weather model.
4. The method of claim 3,
The step of generating the multiple linear regression model comprises:
The method further comprising the step of generating a candidate multiple linear regression model with the most similar insolation to the actual insolation as the multiple linear regression model.
5. The method of claim 4,
The method of claim 1, wherein the variable set used in the multiple linear regression model includes at least one of predicted insolation, stratus, stratus, meso, stratus, and precipitation data.
The method of claim 1,
The step of generating the multiple linear regression model comprises:
Generating the multiple linear regression model for each grid by calculating the regression coefficient of each of the at least one or more variables for each grid based on the distance weight and the thyssen weight based on the distance between the weather station and the target region related to the weather model Including, insolation prediction method.
The method of claim 1,
The virtual grid is equally spaced, insolation prediction method.
In the insolation prediction server of the target area,
a weather model unit for obtaining a weather model including weather information based on a grid based on latitude and longitude;
a variable extraction unit for extracting at least one variable related to insolation through correlation analysis of the weather information;
a multiple linear regression model generator for generating a multiple linear regression model based on the extracted at least one or more variables;
a virtual grid generator for generating a virtual grid by adjusting the grid of the weather model based on the multiple linear regression model; and
Insolation prediction unit for arranging the weather information on the virtual grid and predicting the amount of insolation based on the arranged weather information
Including, insolation prediction server.
9. The method of claim 8,
The correlation analysis will be a Pearson correlation analysis, insolation prediction server.
9. The method of claim 8,
The variable extraction unit,
a candidate model generator configured to generate a plurality of candidate multiple linear regression models based on a plurality of variable sets including at least one different variable;
a candidate predictor for predicting solar radiation based on the plurality of candidate multiple linear regression models; and
Insolation prediction server comprising a comparator for comparing the insolation predicted based on the plurality of candidate multiple linear regression models and the actual insolation based on the weather model.
11. The method of claim 10,
The multiple linear regression model generation unit,
The insolation prediction server that generates a candidate multiple linear regression model with the most similar insolation to the actual insolation as the multiple linear regression model.
12. The method of claim 11,
The set of variables used in the multiple linear regression model is to include at least one of predicted insolation, stratus, stratus, meso, stratus, and precipitation data.
9. The method of claim 8,
The multiple linear regression model generation unit,
A grid-specific model for generating a multi-linear regression model for each grid by calculating a regression coefficient of each of the at least one or more variables for each grid based on a distance weight and a thyssen weight based on a distance between a weather station and a target region for the weather model Further comprising a generator, insolation prediction server.
9. The method of claim 8,
The virtual grid is equally spaced, insolation prediction server.
In a computer program stored in a computer-readable recording medium comprising a sequence of instructions for predicting the amount of insolation of a target area,
When the computer program is executed by a computing device,
obtaining a weather model including weather information based on a grid of latitude and longitude criteria;
At least one variable related to insolation is extracted through correlation analysis of the weather information,
generating a multiple linear regression model based on the extracted at least one or more variables;
adjusting the grid of the weather model based on the multiple linear regression model to generate a virtual grid;
and a sequence of instructions for disposing the weather information on the virtual grid and predicting the amount of insolation based on the arranged weather information.

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