CN113176622A - Hourly solar radiation time downscaling method for cumulative exposure - Google Patents
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Abstract
The invention discloses an hourly solar radiation time downscaling method for cumulative exposure, which comprises the following steps of: acquiring initial forecast data; the method is suitable for the crossing field of energy and meteorological prediction, and integrates prediction data obtained by astronomical radiation proportional interpolation, cubic spline interpolation and fixed proportional interpolation in consideration of the superiority of different interpolation schemes in different regions and seasons, so that the limitation of a certain interpolation scheme is avoided, the method has a smoothing effect, the defects that in the prior art, the space-time resolution is limited and accurate prediction is difficult to make are overcome, and the radiation prediction accuracy is improved.
Description
Technical Field
The invention belongs to the crossing field of energy and meteorological prediction, and particularly relates to an hourly solar radiation time downscaling method for accumulated exposure.
Background
Solar radiation is closely related to our life, which means the total radiant quantity of solar radiation short waves received by the ground, is an important mark for measuring a local solar energy resource, is a main influence factor for influencing the power supply of a solar power station, and a downscaling technology which is interpolated by a mathematical method or statistically experiences relationship is a statistical downscaling technology, belongs to the field of pure physical statistics and depends on observation data to a certain extent, and the statistical downscaling technology is widely applied in the field of weather forecast, and the commonly used time downscaling technology has interpolation methods such as cubic spline interpolation and statistical relationship;
however, in the prior art, the superiority of different interpolation schemes in different regions and seasons is not considered, the partial interpolation schemes have limitations, and meanwhile, the spatial-temporal resolution ratio in the prior art is limited, so that accurate prediction is difficult to make, the accuracy of prediction data is low, and the prediction effect is poor.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an hourly solar radiation time downscaling method for cumulative exposure.
In order to achieve the purpose, the invention adopts the following technical scheme:
an hourly solar radiation time downscaling method for cumulative exposure comprising the steps of:
acquiring initial forecast data;
and (3) carrying out time scale reduction processing on the initial forecast data by an astronomical radiation proportion interpolation method to obtain hourly radiation forecast data 1.
Preferably, the acquiring forecast data includes: 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 by 3 h.
Preferably, the astronomical radiation proportion interpolation method includes: acquiring astronomical radiation at different moments every day by using an astronomical radiation calculation program, and obtaining the total radiation amount, the radiant quantity and the astronomical radiant quantity at three moments by subtracting adjacent moments of the cumulative total radiation amount based on the time-by-time astronomical radiation distribution condition, wherein the radiant quantity calculation formula at the three moments is as follows:
wherein X1, X2 and X3 are astronomical radiation amount at three moments, Y31、Y32、Y33The radiation amount at three moments, and Y is the total radiation amount at three moments.
The hourly solar radiation time downscaling method aiming at the accumulated exposure based on the method comprises the following steps of:
acquiring initial forecast data;
based on three time downscaling methods for accumulated exposure, time downscaling processing is carried out on the initial forecast data to obtain three hourly radiation forecast data, wherein the three time downscaling methods for 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 hourly radiation forecast data to obtain final forecast data.
Preferably, the time scale reduction processing on the initial forecast data includes:
processing the initial forecast data by an astronomical radiation proportion interpolation method to obtain hourly radiation forecast data 1;
processing the initial forecast data by a cubic spline interpolation method to obtain hourly radiation forecast data 2;
and processing the initial forecast data by a fixed proportion interpolation method to obtain hourly radiation forecast data 3.
Preferably, the interpolation data integration includes: and 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 processing calculation formula is:
Y0＝aX3+bX2+cX+d
Y1＝3aX2+2bX+c，
where Y0 is the cumulative total solar radiation and Y1 is the solar radiation at each time.
Preferably, the fixed ratio interpolation method includes:
the time evolution of the accumulated radiation amount at the middle three moments along with the forecast aging is obtained by subtracting the adjacent moments of the accumulated radiation amount;
interpolating the cumulative radiation dose at three moments to hourly according to a fixed proportion, wherein the fixed proportion is as follows: the interpolation ratio before 11 was 1/6:1/3:1/2, the interpolation ratio from 11 to 13 was 0.31:0.35:0.34, and the interpolation ratio after 13 was 1/2:1/3: 1/6;
the radiation calculation formula at three moments is as follows:
0 hour-11 hours: Y21-Y/6, Y22-Y/3, Y23-Y/2,
time 11-13: Y21-0.31Y, Y22-0.31Y, Y23-0.31Y,
13 th-23 th: Y21-Y/2, Y22-Y/3, Y23-Y/6,
wherein Y21, Y22 and Y23 are the radiation amount at three moments, and Y is the total radiation amount at three moments.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
according to the method, based on the fact that the numerical evolution proportion of the astronomical radiation at the adjacent moments is consistent with the actual solar radiation evolution proportion, the solar radiation at the three moments is calculated by adopting the magnitude proportion of the astronomical radiation at the three adjacent moments, so that the accuracy of forecast data is improved, and the forecast effect is improved;
the invention takes the advantages of different interpolation schemes in different regions and seasons into consideration, averages the forecast data obtained by the three interpolation schemes, avoids the limitation of a certain interpolation scheme, has a smoothing effect, and solves the defects that the space-time resolution is limited and accurate forecast is difficult to make in the prior art, thereby improving the accuracy of the forecast data and improving the forecast effect.
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FIG. 1 is a first flowchart of an hourly solar radiation time downscaling method for cumulative exposure in accordance with the present invention;
FIG. 2 is a flow chart of the hourly solar radiation time downscaling method for cumulative exposure of the present invention.
Detailed Description
The following describes the embodiment of the hourly solar radiation time scaling method for cumulative exposure according to the present invention with reference to fig. 1-2. The hourly solar radiation timescale method of the present invention for cumulative exposure is not limited to the description of the following embodiments.
Example 1:
the embodiment provides a specific structure of an hourly solar radiation time scaling method for cumulative exposure, and as shown in fig. 1, the method comprises the following steps:
acquiring initial forecast data;
and (3) carrying out time scale reduction processing on the initial forecast data by an astronomical radiation proportion interpolation method to obtain hourly radiation forecast data 1.
Specifically, obtaining forecast data includes: 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 by 3 h.
Specifically, the astronomical radiation proportion interpolation method comprises the following steps: acquiring astronomical radiation at different moments every day by using an astronomical radiation calculation program, and obtaining the total radiation amount, the radiant quantity and the astronomical radiant quantity at three moments by subtracting adjacent moments of the cumulative total radiation amount based on the time-by-time astronomical radiation distribution condition, wherein the radiant quantity calculation formula at the three moments is as follows:
wherein X1, X2 and X3 are astronomical radiation amount at three moments, Y31、Y32、Y33The radiation amount at three moments, and Y is the total radiation amount at three moments.
The specific structure of the hourly solar radiation time scaling method for cumulative exposure based on the above method is shown in fig. 2, and comprises the following steps:
acquiring initial forecast data;
based on three time downscaling methods for accumulated exposure, time downscaling processing is carried out on the initial forecast data to obtain three hourly radiation forecast data, wherein the three time downscaling methods for 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 hourly radiation forecast data to obtain final forecast data.
Specifically, the time scale reduction processing is performed on the initial forecast data, and comprises the following steps:
processing the initial forecast data by an astronomical radiation proportion interpolation method to obtain hourly radiation forecast data 1;
processing the initial forecast data by a cubic spline interpolation method to obtain hourly radiation forecast data 2;
the initial forecast data is processed by a fixed proportion interpolation method to obtain hourly radiation forecast data 3.
Further, the interpolation data integration comprises: and 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.
Further, the cubic spline interpolation processing calculation formula is as follows:
Y0＝aX3+bX2+cX+d
Y1＝3aX2+2bX+c，
where Y0 is the cumulative total solar radiation and Y1 is the solar radiation at each time.
Further, the fixed ratio interpolation method includes:
the time evolution of the accumulated radiation amount at the middle three moments along with the forecast aging is obtained by subtracting the adjacent moments of the accumulated radiation amount;
interpolating the cumulative radiation dose at three moments to hourly according to a fixed proportion, wherein the fixed proportion is as follows: the interpolation ratio before 11 was 1/6:1/3:1/2, the interpolation ratio from 11 to 13 was 0.31:0.35:0.34, and the interpolation ratio after 13 was 1/2:1/3: 1/6;
the radiation calculation formula at three moments is as follows:
0 hour-11 hours: Y21-Y/6, Y22-Y/3, Y23-Y/2,
time 11-13: Y21-0.31Y, Y22-0.31Y, Y23-0.31Y,
13 th-23 th: Y21-Y/2, Y22-Y/3, Y23-Y/6,
wherein Y21, Y22 and Y23 are the radiation amount at three moments, and Y is the total radiation amount at three moments.
Example 2:
in this embodiment, a specific implementation process of the present invention is described by actual data, taking national solar radiation pattern data and observation data of 2016, 9, 1, 2017, 8, 31, all the year round as an example, one-to-one correspondence between national radiation stations and pattern forecast data grid points is realized by bilinear interpolation, and in a data processing process, in consideration of a data quality problem, abnormal data are removed, and quality control processing is performed; in the inspection process, indexes such as data absolute error AE, mean absolute error MAE, correlation coefficient CO, root mean square error RMSE and the like are adopted to represent the prediction quality of numerical prediction data, the data prediction effect obtained by a cubic spline interpolation scheme, a fixed proportion interpolation scheme and an astronomical radiation proportion interpolation scheme is analyzed, and the calculation formula of each prediction index is as follows:
absolute error: AEj＝|fj-oj|
Mean absolute error:
root mean square error:
correlation coefficient:
where f and o represent the radiance values of the forecast field and the live field respectively,
and
the average value is N, the number of samples is N, and i represents the sample number of prediction and observation.
Table 1 shows the overall average correlation coefficient CO, the root mean square error RMSE, and the average absolute error MAE of the radiation data obtained based on the three interpolation schemes at different forecast ages in spring, summer, autumn, and winter, where a, B, and C correspond to the cubic spline interpolation, the fixed ratio interpolation, and the astronomical radiation ratio interpolation schemes, respectively.
Comparing different processing schemes, and regarding to different seasons, the correlation results of the forecast data obtained by the B, C scheme and the observation data are not greatly different and are obviously stronger than the results of the A scheme from the perspective of correlation, and the correlation result of the C scheme is optimal on the whole; from the aspect of root mean square error, in autumn, the root mean square error of the data obtained by the scheme A is obviously higher than that of the data obtained by the other two schemes, the root mean square error of the data obtained by the three schemes except autumn has little difference, and the root mean square error of the data obtained by the scheme C is smaller on the whole; from the angle of absolute errors, the data errors obtained by the scheme C in spring and summer are smaller, and the data errors obtained by the scheme B in autumn and winter are smaller.
By combining the three inspection indexes, the forecasting effect of the C scheme is obviously better than that of the A, B scheme in spring and summer on the whole, and the effect is good; for autumn and winter, the forecast effect of the B scheme is better, and the forecast effect of the C scheme is slightly inferior; therefore, for all three interpolation schemes, the C scheme, i.e., the interpolation method according to the astronomical radiance ratio, is preferably adopted.
Comparing the test results of different seasons, the correlation between the forecast data and the observation data is best, the root mean square error and the absolute error of the forecast data are minimum in winter, the correlation coefficient is as high as 0.88, and the root mean square error and the absolute error are respectively as low as 120(w.m-2) and 105(w.m-2), namely, the data forecast effect obtained through interpolation in winter is best, and then the forecast effect in spring, autumn and summer is the worst, and may be related to extreme weather such as rainstorm in summer.
The comparison of the test results of different forecast timeliness shows that the forecast effects of the first day, the second day and the third day are not very different, but the forecast effect of the first day is better on the whole, and the forecast effect of the third day is relatively poorer.
TABLE 1 time interpolation test results of different forecast aging in spring, summer, autumn and winter
Conclusion 1:
through comparison, inspection and analysis of forecast data and observation data obtained by the three processing schemes, the forecast effects of the three processing schemes on the national radiation stations are discussed, and the obtained main conclusions are as follows:
comparing the three time interpolation schemes, the astronomical radiation proportional interpolation method has the best overall prediction effect, and the radiation prediction effect is further improved;
the radiation data obtained by the three interpolation schemes show that the forecasting effect is good in spring and winter, and the forecasting effect is poor in autumn and summer;
the radiation data obtained by the three interpolation schemes show that the prediction effect on the first day is good, and the prediction effect on the second day and the third day is relatively poor.
Example 3:
in this embodiment, a specific implementation process of the present invention is described by actual data, taking national solar radiation pattern data and observation data of 2016, 9, 1, 2017, 8, 31, all the year round as an example, one-to-one correspondence between national radiation stations and pattern forecast data grid points is realized by bilinear interpolation, and in a data processing process, in consideration of a data quality problem, abnormal data are removed, and quality control processing is performed; in the inspection process, indexes such as data absolute error AE, mean absolute error MAE, correlation coefficient CO, root mean square error RMSE and the like are adopted to represent the prediction quality of numerical prediction data, the data prediction effect obtained by three time interpolation schemes and integration schemes is analyzed, and the calculation formula of each prediction index is as follows:
absolute error: AEj＝|fj-oj|
Mean absolute error:
root mean square error:
correlation coefficient:
where f and o represent the radiance values of the forecast field and the live field respectively,
and
the average value is N, the number of samples is N, and i represents the sample number of prediction and observation.
Table 2 shows the overall average correlation coefficient CO, the root mean square error RMSE, and the average absolute error MAE of the radiation data obtained based on the three interpolation schemes and the integration scheme in the four seasons of spring, summer, autumn, and winter, where a, B, and C correspond to the cubic spline interpolation, the fixed ratio interpolation, and the astronomical radiation ratio interpolation scheme, respectively, and D corresponds to the integration scheme of the three interpolation methods.
Comparing different processing schemes, and regarding to different seasons, the correlation results of the forecast data obtained by the B, C, D scheme and the observation data are not greatly different and are obviously stronger than the results of the A scheme from the perspective of correlation, and the correlation result of the D scheme is optimal as a whole; from the aspect of root mean square error, in autumn, the root mean square error of the data obtained by the scheme A is obviously higher than that of the data obtained by the other three schemes, the root mean square error of the data obtained by the four schemes except autumn has little difference, and the root mean square error of the data obtained by the C, D scheme is smaller on the whole; from the perspective of absolute errors, the C, D scheme in spring and summer has smaller data errors, and the B, D scheme in autumn and winter has smaller data errors. By combining the three inspection indexes, the prediction effect of the C, D scheme is obviously better than that of the A, B scheme in spring and summer on the whole, and the effect is good; for autumn and winter, the B, D scheme has better forecast effect, and the C scheme has slightly less forecast effect. Therefore, in general, for the three interpolation schemes, the C scheme, that is, the interpolation method according to the astronomical radiation ratio, is preferably adopted; if the integration scheme is combined, namely for four processing schemes, a D scheme, namely an integrated forecasting method according to three interpolation schemes, is preferably adopted.
Comparing the test results of different seasons, the correlation between the forecast data and the observation data is best, the root mean square error and the absolute error of the forecast data are minimum in winter, the correlation coefficient is as high as 0.88, and the root mean square error and the absolute error are respectively as low as 120(w.m-2) and 105(w.m-2), namely, the data forecast effect obtained through interpolation in winter is best, and then the forecast effect in spring, autumn and summer is the worst, and may be related to extreme weather such as rainstorm in summer.
The comparison of the test results of different forecast timeliness shows that the forecast effects of the first day, the second day and the third day are not very different, but the forecast effect of the first day is better on the whole, and the forecast effect of the third day is relatively poorer.
TABLE 2 time interpolation test results of different forecast aging in spring, summer, autumn and winter
And (4) conclusion:
through comparison, inspection and analysis of forecast data and observation data obtained by the four processing schemes, the forecast effects of the four processing schemes on national radiation stations are discussed, and the obtained main conclusion is as follows:
comparing the three time interpolation schemes and the integrated processing scheme, the integrated processing scheme has the best overall forecasting effect, and the radiation forecasting effect is further improved;
the radiation data obtained by the four processing schemes show that the forecasting effect is good in spring and winter, and the forecasting effect is poor in autumn and summer;
the radiation data obtained by the four processing schemes show that the forecasting effect on the first day is good, and the forecasting effect on the second day and the third day is relatively poor.
The working principle is as follows:
astronomical radiation proportion interpolation process:
firstly, selecting a training period with a certain length by an accumulated exposure forecast data acquisition unit, acquiring accumulated exposure forecast data, inputting the accumulated exposure forecast data into a data decoding extraction unit to decode the accumulated exposure forecast data, and outputting initial forecast data;
secondly, inputting the initial forecast data into an astronomical radiation proportion interpolation module, and outputting hourly radiation forecast data 1;
an integration scheme process:
firstly, selecting a training period with a certain length by an accumulated exposure forecast data acquisition unit, acquiring accumulated exposure forecast data, inputting the accumulated exposure forecast data into a data decoding extraction unit to decode the accumulated exposure forecast data, and outputting initial forecast data;
secondly, inputting the initial forecast data into an astronomical radiation proportion interpolation module, a cubic spline interpolation module and a fixed proportion interpolation module respectively, and outputting hourly radiation forecast data 1, hourly radiation forecast data 2 and hourly radiation forecast data 3 by the three modules respectively;
finally, inputting hourly radiation forecast data 1, hourly radiation forecast data 2 and hourly radiation forecast data 3 into the integration module, and outputting final forecast data;
according to the method, based on the fact that the numerical evolution proportion of the astronomical radiation at the adjacent moments is consistent with the actual solar radiation evolution proportion, the solar radiation at the three moments is calculated by adopting the magnitude proportion of the astronomical radiation at the three adjacent moments, so that the accuracy of forecast data is improved, and the forecast effect is improved;
according to the method, the advantages of different interpolation schemes in different regions and seasons are considered, the prediction data obtained by the three interpolation schemes are integrated, the limitation of a certain interpolation scheme is avoided, the method has a smoothing effect, the defects that in the prior art, the space-time resolution is limited and accurate prediction is difficult to make are overcome, and the accuracy of radiation prediction is improved.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (8)
1. An hourly solar radiation time downscaling method for cumulative exposure, comprising the steps of:
acquiring initial forecast data;
and (3) carrying out time scale reduction processing on the initial forecast data by an astronomical radiation proportion interpolation method to obtain hourly radiation forecast data 1.
2. The hourly solar radiation timescale method for cumulative exposure as recited in claim 1, wherein said obtaining 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 by 3 h.
3. The hourly solar radiation timescale method for cumulative exposure of claim 1, wherein said astronomical radiation scale interpolation method comprises: acquiring astronomical radiation at different moments every day by using an astronomical radiation calculation program, and obtaining the total radiation amount, the radiant quantity and the astronomical radiant quantity at three moments by subtracting adjacent moments of the cumulative total radiation amount based on the time-by-time astronomical radiation distribution condition, wherein the radiant quantity calculation formula at the three moments is as follows:
wherein X1, X2 and X3 are astronomical radiation amount at three moments, Y31、Y32、Y33The radiation amount at three moments, and Y is the total radiation amount at three moments.
4. An hourly solar radiation time downscaling method for cumulative exposure based on the method of claims 1-3, characterized in that it comprises the following steps:
acquiring initial forecast data;
based on three time downscaling methods for accumulated exposure, time downscaling processing is carried out on the initial forecast data to obtain three hourly radiation forecast data, wherein the three time downscaling methods for 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 hourly radiation forecast data to obtain final forecast data.
5. The hourly solar radiation timescale method for cumulative exposure as recited in claim 4, wherein said timescale processing initial forecast data comprises:
processing the initial forecast data by an astronomical radiation proportion interpolation method to obtain hourly radiation forecast data 1;
processing the initial forecast data by a cubic spline interpolation method to obtain hourly radiation forecast data 2;
and processing the initial forecast data by a fixed proportion interpolation method to obtain hourly radiation forecast data 3.
6. The hourly solar radiation timescale method for cumulative exposure of claim 5, wherein said interpolating data integration comprises: and 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.
7. The hourly solar radiation timescale method for cumulative exposure of claim 5, wherein: the cubic spline interpolation processing calculation formula is as follows:
Y0＝aX3+bX2+cX+d
Y1＝3aX2+2bX+c，
where Y0 is the cumulative total solar radiation and Y1 is the solar radiation at each time.
8. The hourly solar radiation timescale method for cumulative exposure of claim 5, wherein said fixed ratio interpolation method comprises:
the time evolution of the accumulated radiation amount at the middle three moments along with the forecast aging is obtained by subtracting the adjacent moments of the accumulated radiation amount;
interpolating the cumulative radiation dose at three moments to hourly according to a fixed proportion, wherein the fixed proportion is as follows: the interpolation ratio before 11 was 1/6:1/3:1/2, the interpolation ratio from 11 to 13 was 0.31:0.35:0.34, and the interpolation ratio after 13 was 1/2:1/3: 1/6;
the radiation calculation formula at three moments is as follows:
0 hour-11 hours: Y21-Y/6, Y22-Y/3, Y23-Y/2,
time 11-13: Y21-0.31Y, Y22-0.31Y, Y23-0.31Y,
13 th-23 th: Y21-Y/2, Y22-Y/3, Y23-Y/6,
wherein Y21, Y22 and Y23 are the radiation amount at three moments, and Y is the total radiation amount at three moments.
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