Sunshine duration forecasting method based on multiple modes

Abstract

The invention relates to the technical field of agricultural information processing, in particular to a sunshine duration forecasting method based on multiple modes. The cloud cover and solar radiation forecasting data of EC and CMA-MESO modes are combined, two different calculation algorithms are adopted for contrastive analysis, the optimal forecasting method is selected, the sunshine duration forecasting precision is effectively improved, the method can accurately forecast the sunshine duration according to the cloud cover and the radiation condition, the method can adapt to different seasonal changes, and the method has the advantages of being high in practicability and wide in application range. And the practicability and the reliability are relatively high.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural information processing, and particularly to a method for forecasting sunshine duration based on multiple models. Background Technique

[0002] Sunshine duration is an important parameter in meteorological forecasting, directly affecting multiple fields such as climate change prediction, agricultural production, and energy management. Accurate forecasting of sunshine duration contributes to the prevention of meteorological disasters and the implementation of energy conservation and emission reduction measures. Traditional methods for forecasting sunshine duration have problems such as low prediction accuracy and complex calculations.

[0003] In view of this situation, the present invention proposes a method for forecasting sunshine duration based on the cloud amount and solar radiation forecasts of EC and CMA-MESO. The forecast value of sunshine duration is calculated through two algorithms, and the optimal algorithm is selected through comparative verification. Summary of the Invention

[0004] In view of the problems in the related art, the present invention proposes a method for forecasting sunshine duration based on multiple models to overcome the above-mentioned technical problems existing in the existing related technologies. The purpose of the present invention is to combine the cloud amount and solar radiation forecast data of the EC and CMA-MESO models, and conduct comparative analysis using two different calculation algorithms to select the optimal forecasting method, effectively improving the prediction accuracy of sunshine duration. This method can not only accurately forecast the sunshine duration according to the cloud amount and radiation conditions, but also adapt to different seasonal changes, and has strong practicability and reliability.

[0005] To achieve the above object, the present invention provides the following technical solution: A method for forecasting sunshine duration based on multiple models, including the following steps:

[0006] (1) Data acquisition: Obtain cloud amount and solar radiation forecast data from EC and CMA-MESO;

[0007] (2) Application of Algorithm 1:

[0008] ① Determine the start time and end time of sunshine duration according to the preset time period division;

[0009] ② Convert the low cloud amount forecast data to a 1-hour resolution through 3-hour linear interpolation;

[0010] ③ Calculate the cumulative value of the times when the low cloud amount ≤ 60% from the start time to the end time as the forecast value of sunshine duration;

[0011] (3) Application of Algorithm 2:

[0012] ① Calculate the solar radiation (H0) in the upper atmosphere and the solar radiation (H L ) under clear sky conditions on the ground;

[0013] ②Using the measured total solar radiation (H0) and the total solar radiation on sunny days (H L ), the sunshine duration (S) is calculated through the formula S = S L ·(a + b·(H / H L ))), where a = 0.248 and b = 0.752;

[0014] (4) Inspection and comparison:

[0015] ①Compare the predicted values of the sunshine duration for the past three days calculated by Algorithm 1 and Algorithm 2 with the actual data to evaluate the error;

[0016] ②Select the algorithm with a smaller error as the optimal algorithm;

[0017] (5) Forecast output: Use the optimal algorithm to calculate the predicted values of the sunshine duration for the next 9 days and generate the forecast results.

[0018] Preferably, the time period division of the said Algorithm 1 includes:

[0019] (1) From the Winter Solstice to the Winter Solstice + 30 days, from the Winter Solstice + 30 days to the Spring Equinox + 2 days, from the Spring Equinox + 2 days to the Spring Equinox + 30 days, from the Spring Equinox + 30 days to the Autumnal Equinox - 50 days, from the Autumnal Equinox - 50 days to the Autumnal Equinox + 40 days, from the Autumnal Equinox + 40 days to the Winter Solstice;

[0020] (2) From the Winter Solstice + 25 days to the Spring Equinox - 10 days, from the Spring Equinox - 10 days to the Summer Solstice, from the Summer Solstice to the Summer Solstice + 30 days, from the Summer Solstice + 30 days to the Autumnal Equinox - 20 days, from the Autumnal Equinox - 20 days to the Autumnal Equinox + 35 days to the Winter Solstice + 25 days.

[0021] Preferably, the solar radiation calculation formula in the said Algorithm 2 includes:

[0022] (1) The calculation formula for the solar radiation H0 above the atmosphere is as follows:

[0023]

[0024] In the formula: G SC is the solar constant, E0 is the correction factor for the eccentricity of the Earth's orbit, Φ is the latitude, δ is the solar declination, W S is the hour angle.

[0025] (2) The calculation formula for the solar radiation (H L ) under clear sky conditions on the ground is as follows:

[0026] H L = H0·τa·τb

[0027] In the formula: τa and τb are the atmospheric transmittance coefficients respectively.

[0028] Preferably, in the inspection and comparison step, the root mean square error and the mean absolute error are used as evaluation indicators, and the algorithm with a smaller error is selected as the optimal algorithm.

[0029] Preferably, in the prediction output step, the generated prediction results include the daily sunshine hour values and their corresponding confidence intervals, and are displayed through a graphical interface.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] The present invention is a sunshine hour prediction method based on multiple models. By combining the cloud amount and solar radiation prediction data of the EC and CMA-MESO models, two different calculation algorithms are used for comparative analysis, and the optimal prediction method is selected, effectively improving the prediction accuracy of sunshine hours. This method can not only accurately predict sunshine hours according to cloud amount and radiation conditions, but also adapt to different seasonal changes, and has strong practicability and reliability. Description of the Drawings

[0032] Figure 1 It is a flowchart of an embodiment of the present invention. Detailed Embodiments

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention.

[0034] Embodiment

[0035] The present invention proposes a technical solution for a sunshine hour prediction method based on multiple models: A sunshine hour prediction method based on multiple models includes the following steps:

[0036] (1) Data acquisition: Obtain cloud amount and solar radiation prediction data from EC and CMA-MESO;

[0037] (2) Application of Algorithm 1:

[0038] ① Determine the start time and end time of sunshine hours according to the preset time period division;

[0039] ② Convert the low cloud amount prediction data to a 1-hour resolution through 3-hour linear interpolation; specifically, for example, if the original is 50% at 08:00 and 20% at 11:00, linearly interpolate to 40% at 09:00 and 30% at 10:00;

[0040] ③ Calculate the cumulative value of the times when the low cloud amount ≤ 60% from the start time to the end time as the sunshine hour prediction value; specifically, the unit is hours;

[0041] (3) Application of Algorithm 2:

[0042] ① Calculate the solar radiation (H0) above the atmosphere and the solar radiation (H L ) under clear sky conditions on the ground;

[0043] ② Using the measured total radiation (H0) and the total radiation on sunny days (H L ), calculate the sunshine duration (S) through the formula S = S L ·(a + b·(H / H L )) where a = 0.248 and b = 0.752;

[0044] (4) Verification and comparison:

[0045] ① Compare the predicted values of the sunshine duration for the past three days calculated by Algorithm 1 and Algorithm 2 with the actual data to evaluate the error;

[0046] ② Select the algorithm with a smaller error as the optimal algorithm;

[0047] (5) Forecast output: Use the optimal algorithm to calculate the predicted values of the sunshine duration for the next 9 days and generate the forecast results.

[0048] Further, the time period division of Algorithm 1 includes:

[0049] (1) From the winter solstice to the winter solstice + 30 days, from the winter solstice + 30 days to the vernal equinox + 2 days, from the vernal equinox + 2 days to the vernal equinox + 30 days, from the vernal equinox + 30 days to the autumnal equinox - 50 days, from the autumnal equinox - 50 days to the autumnal equinox + 40 days, from the autumnal equinox + 40 days to the winter solstice;

[0050] (2) From the winter solstice + 25 days to the vernal equinox - 10 days, from the vernal equinox - 10 days to the summer solstice, from the summer solstice to the summer solstice + 30 days, from the summer solstice + 30 days to the autumnal equinox - 20 days, from the autumnal equinox - 20 days to the autumnal equinox + 35 days to the winter solstice + 25 days.

[0051] Preferably, the solar radiation calculation formula in Algorithm 2 includes:

[0052] (1) The calculation formula for the solar radiation H0 above the atmosphere is as follows:

[0053]

[0054] In the formula: G SC is the solar constant, E0 is the correction factor for the eccentricity of the Earth's orbit, Φ is the latitude, δ is the solar declination, W S is the hour angle.

[0055] In this embodiment, E0 = 1.00011 + 0.034221cosτ + 0.00128sinτ + 0.00719cos2τ + 0.000077sin2τ, δ = (180 / π)·(0.006918 - 0.399912cosτ + 0.070257sinτ - 0.006758cos2τ + 0.000907sin2τ - 0.002697cos3τ +

[0056] 0.00148sin3τ), where: the annual angle τ = 2π·(n - 1) / 365, in radians, and n is the day number in a year.

[0057]

[0058] The time interval between sunrise and sunset is the day length (SL). Assuming the solar altitude angle is 0 at sunrise and sunset, then:

[0059] S L = (2 / 15)·W S .

[0060] (2) The calculation formula for solar radiation (H L ) under clear sky conditions on the ground is as follows:

[0061] H L = H0·τa·τb

[0062] Where: τa and τb are the atmospheric transmittance coefficients respectively.

[0063] In this embodiment, H L = 0.8×H0, HL is the total ground radiation under clear sky conditions; the daily solar radiation calculation formula is: H = H L ×(a + b×S / S L ), where: H is the measured daily total radiation, HL is the daily total radiation under clear sky conditions. S and S L are the sunshine hours and day length respectively, a is 0.248, b is 0.752; therefore, the formula for calculating sunshine hours is: S = ((H / H L - a) / b) / S L .

[0064] Furthermore, in the inspection and comparison step, the root mean square error and mean absolute error are used as evaluation indicators, and the algorithm with smaller error is selected as the optimal algorithm.

[0065] Furthermore, in the forecast output step, the generated forecast results include daily sunshine hour values and their corresponding confidence intervals, and are displayed through a graphical interface.

[0066]

[0067]

[0068] Table 1

[0069] Table 1 is a calculation method of sunshine duration based on the EC mode, specifically including the setting rules for the start time and end time of sunshine duration in different time periods.

[0070] For a certain region (e.g., a certain city), through training and verification with historical meteorological data and model data, the calculation results of sunshine duration in different seasons are obtained and compared with the actual data, so as to obtain the optimal forecasting algorithm. According to this method, the sunshine duration forecast for the next 10 days is output daily for the decision-making reference of industries such as agriculture and energy.

[0071] In the present invention, through the calculation and comparison of Algorithm 1 and Algorithm 2, the optimal algorithm is selected as the forecasting conclusion to improve the forecasting accuracy and practicability. By combining the cloud amount and solar radiation forecast data of the EC and CMA-MESO models, comparative analysis is carried out using two different calculation algorithms, and the optimal forecasting method is selected, effectively improving the forecasting accuracy of sunshine duration. This method can not only accurately forecast the sunshine duration according to the cloud amount and radiation conditions, but also adapt to different seasonal changes, and has strong practicability and reliability.

[0072] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "coaxial", "bottom", "one end", "top", "middle", "the other end", "upper", "one side", "top", "inner", "front", "center", "both ends", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0073] In the present invention, unless otherwise clearly specified and defined, the terms "installed", "set", "connected", "fixed", "swivelly connected", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two elements or the interaction relationship between two elements. Unless otherwise clearly defined, for those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0074] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A sunshine duration forecasting method based on multiple modes, characterized in that: The following steps are involved: (1) Data acquisition: Obtain cloud cover and solar radiation forecast data from EC and CMA-MESO; (2) Application of Algorithm 1: ①Determine the start and end time of sunshine hours according to the preset time period; ② Convert the low cloud forecast data to 1-hour resolution through 3-hour linear interpolation; ③ Calculate the cumulative value of the number of times when the low cloud cover is ≤ 60% from the start time to the end time as the sunshine hours forecast value; (3) Application of Algorithm 2: ① Calculate the solar radiation above the atmosphere (H0) and the solar radiation on the ground in clear sky conditions (H L ); ② Using the measured total radiation (H0) and the sunny total radiation (H L ), through the formula S = S L ·(a+b·(H / H L )) Calculate the sunshine hours (S), where a = 0.248, b = 0.752; (4) Inspection and comparison: ① Compare the sunshine hours forecast values ​​for the past three days calculated by Algorithm 1 and Algorithm 2 with the actual data to evaluate the error; ② Select the algorithm with smaller error as the optimal algorithm; (5) Forecast output: Use the optimal algorithm to calculate the forecast value of sunshine hours for the next 9 days and generate the forecast results.

2. The sunshine duration forecasting method based on multiple modes according to claim 1, characterized in that: The time period division of Algorithm 1 includes: (1) Winter Solstice to Winter Solstice + 30 days, Winter Solstice + 30 days to Spring Equinox + 2 days, Spring Equinox + 2 days to Spring Equinox + 30 days, Spring Equinox + 30 days to Autumnal Equinox - 50 days, Autumnal Equinox - 50 days to Autumnal Equinox + 40 days, Autumnal Equinox + 40 days to Winter Solstice; (2) Winter Solstice + 25 days to Vernal Equinox - 10 days, Vernal Equinox - 10 days to Summer Solstice, Summer Solstice to Summer Solstice + 30 days, Summer Solstice + 30 days to Autumnal Equinox - 20 days, Autumnal Equinox - 20 days, Autumnal Equinox + 35 days to Winter Solstice + 25 days.

3. The sunshine duration forecasting method based on multiple modes according to claim 1, characterized in that: The solar radiation calculation formula in Algorithm 2 includes: (1) The calculation formula of solar radiation H0 above the atmosphere is as follows: Where: G SC is the solar constant, E0 is the correction factor for the Earth's orbital eccentricity, Φ is the latitude, δ is the solar declination, W S The hour angle. (2) Solar radiation under clear sky conditions (H L ) is calculated as follows: H L =H0·τa·τb Where: τa and τb are the atmospheric transmittance coefficients respectively.

4. The sunshine duration forecasting method based on multiple modes according to claim 1 is characterized in that: In the inspection and comparison step, the root mean square error and the mean absolute error are used as evaluation indicators, and the algorithm with the smaller error is selected as the optimal algorithm.

5. The sunshine duration forecasting method based on multiple modes according to claim 1, characterized in that: In the forecast output step, the generated forecast results include daily sunshine hours and their corresponding confidence intervals, and are displayed through a graphical interface.

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