CN113176622B - Hour-by-hour solar radiation time downscaling method for accumulated exposure - Google Patents

Abstract

The invention discloses an hour-by-hour solar radiation time downscaling method aiming at accumulated exposure, which comprises the following steps of: acquiring initial forecast data; the method is suitable for the crossing field of energy and weather prediction, and integrates astronomical radiation proportion interpolation, cubic spline interpolation and fixed proportion interpolation to obtain forecast data by considering the superiority of different interpolation schemes in different regions and seasons, so that the limitation of certain interpolation schemes is avoided, the method has a smoothing effect, the defects of limited space-time resolution and difficulty in making accurate prediction in the prior art are overcome, and the radiation forecast accuracy is improved.

Description

Hourly solar radiation temporal downscaling method for cumulative exposure

Technical Field

The present invention belongs to the intersection of energy and meteorological forecasting, and specifically is a method for downscaling hourly solar radiation time for cumulative exposure.

Background Art

Solar radiation is closely related to our lives. It refers to the total short-wave solar radiation received by the ground. It is an important indicator of a place's solar energy resources and the main factor affecting the power supply of solar power stations. The downscaling technology through mathematical interpolation or statistical empirical relationships is a statistical downscaling technology, which belongs to the category of pure physical statistics and depends on observational data to a certain extent. Statistical downscaling technology is widely used in the field of weather forecasting. Commonly used time downscaling technologies include cubic spline interpolation and interpolation methods based on statistical relationships.

However, the existing technology does not take into account the superiority of different interpolation schemes in different regions and seasons, and some interpolation schemes have limitations. At the same time, the temporal and spatial resolution of the existing technology is limited, making it difficult to make precise predictions, the forecast data has low accuracy, and the forecast effect is poor.

Summary of the invention

The purpose of the present invention is to overcome the defects of the prior art and provide a method for downscaling hourly solar radiation for cumulative exposure.

To achieve the above purpose, the present invention adopts the following technical solutions:

The hourly solar radiation temporal downscaling method for cumulative exposure includes the following steps:

Obtain initial forecast data;

The initial forecast data are time-downscaled using the astronomical radiation proportional interpolation method to obtain hourly radiation forecast data1.

Preferably, the obtaining of initial forecast data comprises: selecting a training period of a certain length, obtaining cumulative exposure forecast data, decoding the cumulative exposure forecast data, and obtaining the initial forecast data, wherein the initial forecast data is global model radiation forecast data spatially interpolated to a resolution of 9 km, and the time resolution is accumulated every 3 hours.

Preferably, the astronomical radiation ratio interpolation method includes: using an astronomical radiation calculation program to obtain astronomical radiation at different times of the day, based on the hourly astronomical radiation distribution, the total radiation amount, radiation amount and astronomical radiation amount at three times are obtained by subtracting the adjacent times of the total radiation accumulation amount, and the radiation amount calculation formula at three times is:

Among them, X1, X2, X3 are the astronomical radiation at three moments respectively, Y ₃₁ , Y ₃₂ , Y ₃₃ are the radiation at three moments respectively, and Y is the total radiation at three moments.

The hourly solar radiation time downscaling method for cumulative exposure based on the above method includes the following steps:

Obtain initial forecast data;

Based on three time downscaling methods for cumulative radiation exposure, the initial forecast data are time downscaled to obtain three hourly radiation forecast data. The three time downscaling methods for cumulative radiation exposure are: astronomical radiation ratio interpolation method, cubic spline interpolation method and fixed ratio interpolation method.

The three hourly radiation forecast data are integrated to obtain the final forecast data.

Preferably, the temporal downscaling of the initial forecast data comprises:

The initial forecast data is processed by an astronomical radiation proportional interpolation method to obtain hourly radiation forecast data 1;

The initial forecast data is processed by a cubic spline interpolation method to obtain hourly radiation forecast data 2;

The initial forecast data is processed by a fixed ratio interpolation method to obtain hourly radiation forecast data 3.

Preferably, the interpolation data integration includes: integrating and processing the hourly radiation forecast data 1, the hourly radiation forecast data 2 and the hourly radiation forecast data 3 to obtain final forecast data.

Preferably, the cubic spline interpolation calculation formula is:

Y ₀ = aX ³ + bX ² + cX + d

Y ₁ =3aX ² +2bX+c,

Where Y0 is the total accumulated solar radiation, and Y1 is the solar radiation at each moment.

Preferably, the fixed ratio interpolation method comprises:

The cumulative radiation amount at the three intermediate moments is obtained by subtracting the adjacent times of the cumulative radiation amount from each other and the time evolution of the forecast validity;

The accumulated radiation at the three moments is interpolated to hourly values according to a fixed ratio: the interpolation ratio before 11:00 is 1/6:1/3:1/2, the interpolation ratio from 11:00 to 13:00 is 0.31:0.35:0.34, and the interpolation ratio after 13:00 is 1/2:1/3:1/6;

The radiation calculation formula at three moments is:

0:00-11:00: Y21=Y/6, Y22=Y/3, Y23=Y/2,

11:00-13:00: Y21=0.31*Y, Y22=0.31*Y, Y23=0.31*Y,

13:00-23:00: Y21=Y/2, Y22=Y/3, Y23=Y/6,

Among them, Y21, Y22, and Y23 are the radiation amounts at three moments, and Y is the total radiation amount at three moments.

In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

The present invention is based on the fact that the numerical evolution ratio of astronomical radiation at adjacent moments is consistent with the actual evolution ratio of solar radiation, and uses the size ratio of astronomical radiation at three adjacent moments to calculate the solar radiation at these three moments, thereby improving the accuracy of forecast data and improving the forecast effect;

The present invention takes into account the superiority of different interpolation schemes in different regions and seasons, averages the forecast data obtained by the three interpolation schemes, avoids the limitations of a certain interpolation scheme, has a smoothing effect, and solves the shortcomings of the prior art that the time and space resolution is limited and it is difficult to make precise predictions, thereby improving the accuracy of the forecast data and improving the forecast effect.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for downscaling hourly solar radiation for cumulative exposure according to the present invention;

FIG. 2 is a second flow chart of the hourly solar radiation time downscaling method for cumulative exposure according to the present invention.

DETAILED DESCRIPTION

The following further describes the specific implementation of the hourly solar radiation time downscaling method for cumulative exposure in the present invention in conjunction with Figures 1-2. The hourly solar radiation time downscaling method for cumulative exposure in the present invention is not limited to the description of the following embodiments.

Embodiment 1:

This embodiment provides a specific structure of a method for downscaling hourly solar radiation for cumulative exposure, as shown in FIG1 , including the following steps:

Obtain initial forecast data;

The initial forecast data are time-downscaled using the astronomical radiation proportional interpolation method to obtain hourly radiation forecast data1.

Specifically, obtaining initial forecast data includes: selecting a training period of a certain length, obtaining cumulative exposure forecast data, decoding the cumulative exposure forecast data, and obtaining initial forecast data, wherein the initial forecast data is global model radiation forecast data spatially interpolated to a resolution of 9 km, and the time resolution is accumulated every 3 hours.

Specifically, the astronomical radiation proportional interpolation method includes: using an astronomical radiation calculation program to obtain astronomical radiation at different times of the day, based on the hourly astronomical radiation distribution, the total radiation amount, radiation amount and astronomical radiation amount at three times are obtained by subtracting the adjacent times of the total radiation accumulation amount, and the radiation amount calculation formula at three times is:

Among them, X1, X2, X3 are the astronomical radiation at three moments respectively, Y ₃₁ , Y ₃₂ , Y ₃₃ are the radiation at three moments respectively, and Y is the total radiation at three moments.

The specific structure of the hourly solar radiation time downscaling method for cumulative exposure based on the above method is shown in FIG2 , and includes the following steps:

Obtain initial forecast data;

Based on three time downscaling methods for cumulative radiation exposure, the initial forecast data are time downscaled to obtain three hourly radiation forecast data. The three time downscaling methods for cumulative radiation exposure are: astronomical radiation ratio interpolation method, cubic spline interpolation method and fixed ratio interpolation method.

The three hourly radiation forecast data are integrated to obtain the final forecast data.

Specifically, the initial forecast data is temporally downscaled, including:

The initial forecast data is processed by the astronomical radiation proportional interpolation method to obtain hourly radiation forecast data1;

The initial forecast data is processed by cubic spline interpolation method to obtain hourly radiation forecast data 2;

The initial forecast data are processed by the fixed ratio interpolation method to obtain hourly radiation forecast data3.

Furthermore, the interpolation data integration includes: integrating and processing the hourly radiation forecast data 1, the hourly radiation forecast data 2 and the hourly radiation forecast data 3 to obtain the final forecast data.

Furthermore, the cubic spline interpolation calculation formula is:

Y ₀ = aX ³ + bX ² + cX + d

Y ₁ =3aX ² +2bX+c,

Where Y0 is the total accumulated solar radiation, and Y1 is the solar radiation at each moment.

Further, the fixed ratio interpolation method includes:

The cumulative radiation amount at the three intermediate moments is obtained by subtracting the adjacent times of the cumulative radiation amount from each other and the time evolution of the forecast validity;

The accumulated radiation at the three moments is interpolated to hourly values according to a fixed ratio: the interpolation ratio before 11:00 is 1/6:1/3:1/2, the interpolation ratio from 11:00 to 13:00 is 0.31:0.35:0.34, and the interpolation ratio after 13:00 is 1/2:1/3:1/6;

The radiation calculation formula at three moments is:

0:00-11:00: Y21=Y/6, Y22=Y/3, Y23=Y/2,

11:00-13:00: Y21=0.31*Y, Y22=0.31*Y, Y23=0.31*Y,

13:00-23:00: Y21=Y/2, Y22=Y/3, Y23=Y/6,

Among them, Y21, Y22, and Y23 are the radiation amounts at three moments, and Y is the total radiation amount at three moments.

Embodiment 2:

This embodiment introduces the specific implementation process of the present invention with actual data. Taking the national solar radiation model data and observation data for the four seasons from September 1, 2016 to August 31, 2017 as an example, a one-to-one correspondence between the national radiation stations and the model forecast data grid points is achieved through bilinear interpolation. In the data processing process, considering the data quality problem, abnormal data is eliminated and quality control is performed; in the inspection process, indicators such as data absolute error AE, mean absolute error MAE, correlation coefficient CO and root mean square error RMSE are used to characterize the forecast quality of numerical forecast data, and the data forecast effects obtained by the cubic spline interpolation scheme, the fixed ratio interpolation scheme and the astronomical radiation ratio interpolation scheme are analyzed. The calculation formulas of each forecast indicator are as follows:

Absolute error: AE _j = |f _j -o _j |

Mean absolute error:

Root Mean Square Error:

Correlation coefficient:

Where f and o represent the radiation values of the forecast field and the actual field respectively. and is the average value, N is the number of samples, and i represents the sample number of the forecast and observation.

Table 1 shows the overall average correlation coefficient CO, root mean square error RMSE and mean absolute error MAE of radiation data obtained based on three interpolation schemes in different forecast time periods of spring, summer, autumn and winter. A, B and C correspond to cubic spline interpolation, fixed ratio interpolation and astronomical radiation ratio interpolation schemes, respectively.

Comparing different processing schemes, from the perspective of correlation, for different seasons, the correlation results between the forecast data and the observed data obtained by Schemes B and C are not much different, and are significantly stronger than the results of Scheme A. Overall, the correlation result of Scheme C is the best; from the perspective of root mean square error, in autumn, the root mean square error of the data obtained by Scheme A is significantly higher than that of the other two schemes. Except for autumn, the root mean square errors of the data obtained by the three schemes are not much different. Overall, the root mean square error of the data obtained by Scheme C is smaller; from the perspective of absolute error, the data error obtained by Scheme C is smaller in spring and summer, and the data error obtained by Scheme B is smaller in autumn and winter.

Combining these three test indicators, overall, for spring and summer, the forecast effect of Scheme C is significantly stronger than that of Schemes A and B, and the effect is better; for autumn and winter, the forecast effect of Scheme B is better, and the forecast effect of Scheme C is slightly inferior; therefore, overall, among the three interpolation schemes, Scheme C should be adopted, that is, the interpolation method based on the astronomical radiation ratio.

By comparing the test results of different seasons, it was found that in winter, the correlation between forecast data and observed data was the best, and the root mean square error and absolute error of forecast data were the smallest. The correlation coefficient was as high as 0.88, and the root mean square error and absolute error were as low as 120 (w.m-2) and 105 (w.m-2), respectively. That is, the data forecast obtained by interpolation in winter had the best forecast effect, followed by spring and autumn. The forecast effect in summer was the worst, which may be related to extreme weather such as summer rainstorms.

By comparing the test results of different forecast time periods, it was found that the forecast effects on the first, second and third days were not much different, but overall the forecast effect on the first day was better, while the forecast effect on the third day was relatively poor.

Table 1 Time interpolation test results of different forecast time periods in spring, summer, autumn and winter

Conclusion 1:

By comparing and analyzing the forecast data obtained by the three processing schemes with the observed data, the forecast effects of the three processing schemes on the national radiation stations were discussed, and the main conclusions are as follows:

By comparing the three time interpolation schemes, it is found that the astronomical radiation proportional interpolation method has the best overall prediction effect, which further improves the radiation prediction effect;

The radiation data obtained by the three interpolation schemes all show that the forecast effect is better in spring and winter, followed by autumn, and relatively worse in summer.

The radiation data obtained by the three interpolation schemes all show that the forecast effect on the first day is better, followed by the second day, and the forecast effect on the third day is relatively poor.

Embodiment 3:

This embodiment introduces the specific implementation process of the present invention with actual data. Taking the national solar radiation model data and observation data for the four seasons from September 1, 2016 to August 31, 2017 as an example, a one-to-one correspondence between the national radiation stations and the model forecast data grid points is achieved through bilinear interpolation. In the data processing process, considering the data quality problem, abnormal data is eliminated and quality control is performed; in the inspection process, indicators such as data absolute error AE, mean absolute error MAE, correlation coefficient CO and root mean square error RMSE are used to characterize the forecast quality of numerical forecast data, and the data forecast effects obtained by three time interpolation schemes and integrated schemes are analyzed. The calculation formulas of various forecast indicators are as follows:

Absolute error: AE _j = |f _j -o _j |

Mean absolute error:

Root Mean Square Error:

Correlation coefficient:

Where f and o represent the radiation values of the forecast field and the actual field respectively. and is the average value, N is the number of samples, and i represents the sample number of the forecast and observation.

Table 2 shows the overall average correlation coefficient CO, root mean square error RMSE and mean absolute error MAE of radiation data obtained based on three interpolation schemes and integrated schemes under different forecast time periods in spring, summer, autumn and winter. A, B and C correspond to cubic spline interpolation, fixed ratio interpolation and astronomical radiation ratio interpolation schemes, respectively, and D corresponds to the integrated scheme of the three interpolation methods.

Comparing different processing schemes, from the perspective of correlation, for different seasons, the correlation results of the forecast data obtained by schemes B, C, and D with the observed data are not much different, and are significantly stronger than the results of scheme A. Overall, the correlation result of scheme D is the best; from the perspective of root mean square error, in autumn, the root mean square error of the data obtained by scheme A is significantly higher than that of the other three schemes. Except for autumn, the root mean square errors of the data obtained by the four schemes are not much different. Overall, the root mean square errors of the data obtained by schemes C and D are smaller; from the perspective of absolute error, the data errors obtained by schemes C and D are smaller in spring and summer, and the data errors obtained by schemes B and D are smaller in autumn and winter. Combining these three test indicators, overall, for spring and summer, the forecast effects of schemes C and D are significantly stronger than those of schemes A and B, and the effect is good; for autumn and winter, the forecast effects of schemes B and D are better, and the forecast effect of scheme C is slightly inferior. Therefore, overall, for the three interpolation schemes, it is advisable to adopt scheme C, that is, the interpolation method based on the astronomical radiation ratio; if combined with the integrated scheme, that is, for the four processing schemes, it is advisable to adopt scheme D, that is, the integrated forecast method based on the three interpolation schemes.

By comparing the test results of different seasons, it was found that in winter, the correlation between forecast data and observed data was the best, and the root mean square error and absolute error of forecast data were the smallest. The correlation coefficient was as high as 0.88, and the root mean square error and absolute error were as low as 120 (w.m-2) and 105 (w.m-2), respectively. That is, the data forecast obtained by interpolation in winter had the best forecast effect, followed by spring and autumn. The forecast effect in summer was the worst, which may be related to extreme weather such as summer rainstorms.

By comparing the test results of different forecast time periods, it was found that the forecast effects on the first, second and third days were not much different, but overall the forecast effect on the first day was better, while the forecast effect on the third day was relatively poor.

Table 2 Time interpolation test results of different forecast time periods in spring, summer, autumn and winter

in conclusion:

By comparing and analyzing the forecast data obtained by the four processing schemes with the observed data, the forecast effects of the four processing schemes on the national radiation stations were discussed, and the main conclusions are as follows:

Comparison of three time interpolation schemes and integrated processing scheme shows that the integrated processing scheme has the best overall forecast effect, which further improves the radiation forecast effect.

The radiation data obtained by the four processing schemes all show that the forecast effect is better in spring and winter, followed by autumn, and relatively worse in summer.

The radiation data obtained through the four processing schemes all show that the forecast effect on the first day is better, followed by the second day, and the forecast effect on the third day is relatively poor.

Working principle:

Astronomical radiation ratio interpolation process:

Firstly, a training period of a certain length is selected through the cumulative radiation exposure forecast data acquisition unit to obtain the cumulative radiation exposure forecast data, and the cumulative radiation exposure forecast data is input into the data decoding and extraction unit to decode the cumulative radiation exposure forecast data and output the initial forecast data;

Secondly, the initial forecast data is input into the astronomical radiation ratio interpolation module, and the hourly radiation forecast data 1 is output;

Integration solution process:

Firstly, a training period of a certain length is selected through the cumulative radiation exposure forecast data acquisition unit to obtain the cumulative radiation exposure forecast data, and the cumulative radiation exposure forecast data is input into the data decoding and extraction unit to decode the cumulative radiation exposure forecast data and output the initial forecast data;

Secondly, the initial forecast data are input into the astronomical radiation ratio interpolation module, the cubic spline interpolation module and the fixed ratio interpolation module respectively, and the three modules output hourly radiation forecast data 1, hourly radiation forecast data 2 and hourly radiation forecast data 3 respectively;

Finally, the hourly radiation forecast data 1, the hourly radiation forecast data 2 and the hourly radiation forecast data 3 are input into the integration module, and the final forecast data is output;

The present invention is based on the fact that the numerical evolution ratio of astronomical radiation at adjacent moments is consistent with the actual evolution ratio of solar radiation, and uses the size ratio of astronomical radiation at three adjacent moments to calculate the solar radiation at these three moments, thereby improving the accuracy of forecast data and improving the forecast effect;

The present invention takes into account the superiority of different interpolation schemes in different regions and seasons, integrates the forecast data obtained by three interpolation schemes, avoids the limitations of a certain interpolation scheme, has a smoothing effect, solves the shortcomings of the existing technology that the time and space resolution is limited and it is difficult to make precise predictions, and improves the accuracy of radiation forecast.

The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims (7)

1. An hour-by-hour solar radiation time downscaling method for cumulative exposure, characterized by comprising the steps of:

Acquiring initial forecast data;

performing time downscaling processing on the initial forecast data through an astronomical radiation proportion interpolation method to obtain hour-by-hour radiation forecast data 1, wherein the astronomical radiation proportion interpolation method comprises the following steps: obtaining astronomical radiation at different times of day by using an astronomical radiation calculation program, and obtaining the total radiation quantity, the radiation quantity and the astronomical radiation quantity at three times by subtracting adjacent times of the total radiation accumulation quantity based on the time-by-time astronomical radiation distribution condition, wherein the radiation quantity calculation formulas at the three times are as follows:

Wherein, X1, X2 and X3 are astronomical radiation amounts at three moments respectively, Y ₃₁、Y₃₂、Y₃₃ is radiation amount at three moments respectively, and Y is radiation total amount at three moments.

2. The hour-by-hour solar radiation time downscaling method for cumulative exposure of claim 1, wherein the acquiring initial forecast data comprises: selecting a training period with a certain length, acquiring accumulated exposure forecast data, and decoding the accumulated exposure forecast data to obtain initial forecast data, wherein the initial forecast data is global mode radiation forecast data with spatial interpolation to 9km resolution, and the time resolution is accumulated every 3 hours.

3. An hour-by-hour solar radiation time downscaling method for cumulative exposure based on the method according to any one of claims 1-2, characterized by comprising the steps of:

Acquiring initial forecast data;

Performing time downscaling processing on the initial forecast data based on three time downscaling methods aiming at the accumulated exposure to obtain three hour-by-hour radiation forecast data, wherein the three time downscaling methods aiming at the accumulated exposure are as follows: an astronomical radiation proportion interpolation method, a cubic spline interpolation method and a fixed proportion interpolation method;

and integrating the obtained three kinds of hour-by-hour radiation forecast data to obtain final forecast data.

4. An hour-by-hour solar radiation time downscaling method for cumulative exposure as claimed in claim 3 wherein said time downscaling the initial forecast data comprises:

The initial forecast data is processed by an astronomical radiation proportion interpolation method to obtain hour-by-hour radiation forecast data 1;

The initial forecast data is processed by a cubic spline interpolation method to obtain hour-by-hour radiation forecast data 2;

the initial forecast data is processed by a fixed proportion interpolation method to obtain hour-by-hour radiation forecast data 3.

5. The hour-by-hour solar radiation time downscaling method for cumulative exposure of claim 4, wherein the interpolation data integration comprises: and integrating the hour-by-hour radiation forecast data 1, the hour-by-hour radiation forecast data 2 and the hour-by-hour radiation forecast data 3 to obtain final forecast data.

6. The hour-by-hour solar radiation time downscaling method for cumulative exposure of claim 4, wherein: the cubic spline interpolation processing calculation formula is as follows:

Y₀=aX³+bX²+cX+d

Y₁=3aX²+2bX+c，

Where Y0 is the total cumulative solar radiation and Y1 is the solar radiation at each instant.

7. The hour-by-hour solar radiation time downscaling method for cumulative exposure of claim 4, characterized in that the fixed ratio interpolation method comprises:

Subtracting adjacent times of the total quantity of accumulated radiation to obtain the time evolution of the accumulated radiation quantity at the middle three times along with the forecast aging;

Interpolation of the cumulative radiation amounts at three moments to hour by hour is performed according to a fixed ratio: the interpolation ratio before 11 is 1/6:1/3:1/2, the interpolation ratio between 11 and 13 is 0.31:0.35:0.34, and the interpolation ratio after 13 is 1/2:1/3:1/6;

The radiation calculation formulas at three moments are:

0 time-11 time: y21=y/6, y22=y/3, y23=y/2,

11-13: Y21=0.31×y, y22=0.31×y, y23=0.31×y,

13-23: Y21=y/2, y22=y/3, y23=y/6,

Wherein Y21, Y22 and Y23 are radiation amounts at three moments, and Y is the total radiation amount at three moments.