CN113176622B - Hourly solar radiation temporal downscaling method for cumulative exposure - Google Patents
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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
Technical Field
The invention belongs to the field of intersection of energy and weather prediction, and particularly relates to an hour-by-hour solar radiation time downscaling method aiming at accumulated exposure.
Background
Solar radiation is closely related to our life, refers to the total radiation quantity of solar radiation shortwaves received by the ground, is an important mark for measuring solar resources in one place, is a main influence factor for influencing the power supply of a solar power station, is a statistical downscaling technology through interpolation of a mathematical method or statistical experience relation, belongs to a pure physical statistical category, depends on observation data to a certain extent, and is widely applied in the weather forecast field, and a common time downscaling technology comprises a cubic spline interpolation method, an interpolation method according to a statistical relation and the like;
however, the prior art does not consider the superiority of different interpolation schemes in different regions and seasons, and part of interpolation schemes have limitations, and meanwhile, the prior art has limited space-time resolution, is difficult to make accurate prediction, has low prediction data accuracy and has poor prediction effect.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an hour-by-hour solar radiation time downscaling method aiming at accumulated exposure.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an hour-by-hour solar radiation time downscaling method for cumulative exposure comprising the steps of:
Acquiring initial forecast data;
And performing time downscaling processing on the initial forecast data through an astronomical radiation proportion interpolation method to obtain the hour-by-hour radiation forecast data 1.
Preferably, the acquiring initial 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 every 3 hours.
Preferably, 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.
An hour-by-hour solar radiation time downscaling method for cumulative exposure based on the method comprises the following steps:
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.
Preferably, the performing time downscaling on the initial forecast data includes:
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.
Preferably, the interpolation data integration includes: 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.
Preferably, the cubic spline interpolation processing calculation formula is:
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.
Preferably, the fixed ratio interpolation method includes:
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.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
According to the invention, based on the fact that the numerical evolution proportion of the astronomical radiation at adjacent moments is consistent with the actual solar radiation evolution proportion, the solar radiation at the three moments is calculated by adopting the magnitude proportions 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 prediction data obtained by the three interpolation schemes are averaged in consideration of the superiority of the different interpolation schemes in different regions and seasons, the limitation of a certain interpolation scheme is avoided, the method has a smoothing effect, the defect that the space-time resolution of the prior art is limited and accurate prediction is difficult to make is overcome, and therefore the accuracy of the prediction data is improved, and the prediction effect is improved.
Drawings
FIG. 1 is a flow chart one of the hour-by-hour solar radiation time downscaling method for cumulative exposure of the present invention;
FIG. 2 is a second flowchart of the hour-by-hour solar radiation time downscaling method of the present invention for cumulative exposure.
Detailed Description
Specific embodiments of the hour-by-hour solar radiation time downscaling method for cumulative exposure of the present invention are further described below in conjunction with FIGS. 1-2. The hour-by-hour solar radiation time downscaling method for cumulative exposure of the present invention is not limited to the description of the following examples.
Example 1:
this example gives a specific structure of an hour-by-hour solar radiation time downscaling method for cumulative exposure, as shown in fig. 1, comprising the steps of:
Acquiring initial forecast data;
And performing time downscaling processing on the initial forecast data through an astronomical radiation proportion interpolation method to obtain the hour-by-hour radiation forecast data 1.
Specifically, acquiring initial 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 every 3 hours.
Specifically, 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.
The specific structure of the hour-by-hour solar radiation time downscaling method for the accumulated exposure based on the method comprises the following steps as shown in fig. 2:
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.
Specifically, the time downscaling process is performed on the initial forecast data, including:
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.
Further, the interpolation data integration includes: 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.
Further, the cubic spline interpolation processing calculation formula is:
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.
Further, the fixed ratio interpolation method includes:
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.
Example 2:
In the embodiment, actual data is taken as an introduction of the specific implementation process of the invention, nationwide solar radiation mode data and observation data which are all four seasons are taken as examples from the year 2016, the month 9, the month 1, the year 2017, the month 8 and the month 31, the one-to-one correspondence between nationwide radiation stations and mode forecast data grid points is realized through bilinear interpolation, in the data processing process, abnormal data are removed in consideration of the data quality problem, and quality control processing is carried out; in the inspection process, indexes such as data absolute error AE, average absolute error MAE, correlation coefficient CO, root mean square error RMSE and the like are adopted to represent the forecast quality of numerical forecast data, and the data forecast effect obtained through a cubic spline interpolation scheme, a fixed proportion interpolation scheme and an astronomical radiation proportion interpolation scheme is analyzed, wherein the calculation formula of each forecast index is as follows:
absolute error: AE j＝|fj-oj |
Average absolute error:
root mean square error:
correlation coefficient:
where f and o represent the radiation values of the forecasted and live fields respectively, AndN is the number of samples, i is the number of samples for forecasting and observation.
Table 1 shows the global average correlation coefficient CO, root mean square error RMSE and mean absolute error MAE of the radiation data obtained based on the three interpolation schemes under the different forecast ages of spring, summer, autumn and winter, and A, B, C correspond to the cubic spline interpolation, fixed proportion interpolation and astronomical radiation proportion interpolation schemes respectively.
Compared with different processing schemes, the correlation results of the forecast data and the observed data obtained by the B, C scheme are not much different from the correlation point of view, and are obviously stronger than the results of the A scheme, and the correlation result of the C scheme is the best overall; in terms 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 other two schemes, the root mean square error of the data obtained by the three schemes except the autumn is not great, and the root mean square error of the data obtained by the scheme C is smaller as a whole; from the angle of absolute error, the data error obtained by the scheme C in spring and summer is smaller, and the data error obtained by the scheme B in autumn and winter is smaller.
By combining the three inspection indexes, the forecasting effect of the scheme C is obviously stronger than that of the scheme A, B in spring and summer, and the effect is good; for autumn and winter, the scheme B has good forecasting effect, and the scheme C has slightly inferior forecasting effect; thus, for three interpolation schemes as a whole, the C scheme, i.e. interpolation method according to the astronomical radiation proportion, is preferably employed.
Comparing the test results of different seasons, the correlation between the forecast data and the observed data is best in winter, the root mean square error and the absolute error of the forecast data are minimum, the correlation coefficient is as high as 0.88, 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 forecast effect of the data obtained by interpolation in winter is best, and the forecast effect in spring and autumn is worst in summer and possibly related to extreme weather such as heavy rain in summer.
The test results of different forecasting timelines are compared, and the forecasting effects of the first day, the second day and the third day are not different, but the forecasting effect of the first day is good as a whole, and the forecasting effect of the third day is relatively poor.
TABLE 1 time interpolation test results of different forecast timeliness in spring, summer, autumn and winter
Conclusion 1:
The forecast effect of the three treatment schemes on the national radiation station is discussed by comparing, checking and analyzing the forecast data and the observed data obtained by the three treatment schemes, and the main conclusion is as follows:
comparing the three time interpolation schemes, the astronomical radiation proportion interpolation method has the best overall forecasting effect, and further improves the radiation forecasting effect;
The radiation data obtained by the three interpolation schemes show that the forecasting effect is good in spring and winter, the forecasting effect is inferior in autumn and relatively poor in summer;
the radiation data obtained by the three interpolation schemes show that the forecasting effect of the first day is good, the forecasting effect of the second day is poor, and the forecasting effect of the third day is relatively poor.
Example 3:
In the embodiment, actual data is taken as an introduction of the specific implementation process of the invention, nationwide solar radiation mode data and observation data which are all four seasons are taken as examples from the year 2016, the month 9, the month 1, the year 2017, the month 8 and the month 31, the one-to-one correspondence between nationwide radiation stations and mode forecast data grid points is realized through bilinear interpolation, in the data processing process, abnormal data are removed in consideration of the data quality problem, and quality control processing is carried out; in the inspection process, indexes such as data absolute error AE, average absolute error MAE, correlation coefficient CO and root mean square error RMSE are adopted to represent the forecast quality of numerical forecast data, the data forecast effect obtained through three time interpolation schemes and integration schemes is analyzed, and the calculation formula of each forecast index is as follows:
absolute error: AE j＝|fj-oj |
Average absolute error:
root mean square error:
correlation coefficient:
where f and o represent the radiation values of the forecasted and live fields respectively, AndN is the number of samples, i is the number of samples for forecasting and observation.
Table 2 shows the overall average correlation coefficient CO, root mean square error RMSE and mean absolute error MAE of the radiation data obtained based on the three interpolation schemes and the integration scheme under the different forecasting timelines of spring, summer, autumn and winter, A, B, C respectively correspond to the cubic spline interpolation, the fixed proportion interpolation and the astronomical radiation proportion interpolation scheme, and D corresponds to the integration scheme of the three interpolation methods.
Compared with different processing schemes, the correlation results of the forecast data and the observed data obtained by the B, C, D scheme are not much different from the correlation point of view, and are obviously stronger than the results of the A scheme, and the correlation result of the D scheme is the best as a whole; in terms 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 other three schemes, the root mean square error of the data obtained by the four schemes except the autumn is not great, and the root mean square error of the data obtained by the scheme C, D is smaller as a whole; from the absolute error perspective, the data error obtained by the C, D scheme in spring and summer is smaller, and the data error obtained by the B, D scheme in autumn and winter is smaller. By combining the three inspection indexes, the forecast effect of C, D scheme is obviously stronger than A, B scheme in spring and summer, and the effect is good; for autumn and winter, B, D scheme has better forecasting effect, and C scheme has slightly inferior forecasting effect. Therefore, for three interpolation schemes, a C scheme is preferably adopted, namely, an interpolation method according to astronomical radiation proportion is adopted; if the integration scheme is combined, namely, for four processing schemes, a D scheme is preferably adopted, namely, the forecasting method is integrated according to three interpolation schemes.
Comparing the test results of different seasons, the correlation between the forecast data and the observed data is best in winter, the root mean square error and the absolute error of the forecast data are minimum, the correlation coefficient is as high as 0.88, 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 forecast effect of the data obtained by interpolation in winter is best, and the forecast effect in spring and autumn is worst in summer and possibly related to extreme weather such as heavy rain in summer.
The test results of different forecasting timelines are compared, and the forecasting effects of the first day, the second day and the third day are not different, but the forecasting effect of the first day is good as a whole, and the forecasting effect of the third day is relatively poor.
TABLE 2 time interpolation test results of different forecast timeliness in spring, summer, autumn and winter
Conclusion:
The forecasting effect of the four treatment schemes on the national radiation station is discussed by comparing, checking and analyzing the forecasting data and the observed data obtained by the four treatment schemes, and the 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 treatment schemes show that the forecasting effect is good in spring and winter, the forecasting effect is inferior in autumn and relatively poor in summer;
The radiation data obtained by the four treatment schemes show that the forecasting effect of the first day is good, the forecasting effect of the second day is poor, and the forecasting effect of the third day is relatively poor.
Working principle:
astronomical radiation proportion interpolation process:
Firstly, selecting a training period with a certain length through an accumulated exposure forecast data acquisition unit, acquiring accumulated exposure forecast data, inputting the accumulated exposure forecast data into a data decoding and extracting unit to decode the accumulated exposure forecast data, and outputting initial forecast data;
secondly, inputting initial forecast data into an astronomical radiation proportion interpolation module, and outputting hour-by-hour radiation forecast data 1;
The integration scheme comprises the following steps:
Firstly, selecting a training period with a certain length through an accumulated exposure forecast data acquisition unit, acquiring accumulated exposure forecast data, inputting the accumulated exposure forecast data into a data decoding and extracting unit to decode the accumulated exposure forecast data, and outputting initial forecast data;
Secondly, respectively inputting initial forecast data into an astronomical radiation proportion interpolation module, a cubic spline interpolation module and a fixed proportion interpolation module, wherein the three modules respectively output hour-by-hour radiation forecast data 1, hour-by-hour radiation forecast data 2 and hour-by-hour radiation forecast data 3;
Finally, inputting 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 into an integrated module, and outputting final forecast data;
According to the invention, based on the fact that the numerical evolution proportion of the astronomical radiation at adjacent moments is consistent with the actual solar radiation evolution proportion, the solar radiation at the three moments is calculated by adopting the magnitude proportions 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 integrates the forecast data obtained by three interpolation schemes in consideration of the superiority of different interpolation schemes in different regions and seasons, avoids the limitation of certain interpolation schemes, has a smoothing effect, solves the defects of limited space-time resolution and difficult precise forecast in the prior art, and improves the accuracy of radiation forecast.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the 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.
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| CN106203713A (en) * | 2016-07-14 | 2016-12-07 | 国网湖南省电力公司 | Consider the northern area electrical network icing numerical forecast modification method of solar radiation |
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