CN111815020A - South wall radiation prediction method based on solar radiation climate characteristic identification - Google Patents
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Abstract
The invention relates to a south wall radiation prediction method based on solar radiation climate characteristic identification, which comprises the following steps: 1) acquiring local historical data; 2) defining a weather type index SCF, and dividing the weather type of historical data based on the weather type index SCF; 3) and classifying the local measured data according to the weather type index SCF, and selecting a corresponding prediction model for radiation prediction according to different weather types. Compared with the prior art, the method has the advantages of considering weather type classification prediction, improving the accuracy of a prediction model, being simple and accurate in weather type classification, predicting the hourly scale and the like.
Description
Technical Field
The invention relates to the field of photovoltaic power generation evaluation and prediction, in particular to a south wall radiation prediction method based on solar radiation climate characteristic identification.
Background
In 2019, 24 th world energy meeting mentions that China will continue to promote energy structure reform, always adhere to the green low-carbon strategic direction, concentrate on improving the proportion of clean energy consumption, gradually realize the replacement of the existing main energy, namely fossil energy, the combustion of the traditional fossil fuel is the main source of greenhouse gas, and the development and application of renewable energy provides a feasible scheme for reducing the emission of the greenhouse gas. Under the age of green development of electric power, the electric power industry in China stands for the characteristic of rich energy resources of wind energy and solar energy resources, and the diversified development of renewable energy resources is greatly promoted, so that a way for the common development of wind power, photovoltaic power generation, hydropower and renewable energy resources is formed. Among them, solar energy resources have great development potential, and thus are receiving more and more attention. According to statistics of the national energy agency, in the first quarter of 2020, 395 thousands of kilowatts are newly added to the photovoltaic power generation installation nationwide, 223 thousands of kilowatts are newly added to the centralized photovoltaic installation, and 172 thousands of kilowatts are newly added to the distributed photovoltaic installation. The photovoltaic power generation capacity of the first quarter is 528 hundred million kilowatt-hours, and the year-on-year increase is 19.9 percent; the number of nationwide photovoltaic utilization hours is 248 hours, and the same time is increased by 8 hours.
At present, scholars at home and abroad have more researches on the solar radiation condition of building roofs and less researches on the relevant conditions of vertical wall surfaces. From the characteristics of solar energy utilization and the current development prospect, the vertical wall light-receiving area occupies an absolute advantage. Literature (Li Feng, Hu super, horse-year Jun, Chen Zhenghong, Lvwenhua, Yang Xingwu, Jing Hu Han area wall surface monthly mean solar Total radiation prediction model comparison [ J]2016,34(03): 324-. The method has the advantages that the prediction precision of the homology model is low under the sunny condition, and the prediction precision of the anisotropic model is high; the Liu homogeneity model is more suitable for predicting northTotal radiation of vertical wall surfaces in the Jing region; the prediction result of the anisotropic model tends to the prediction result of the isotropic model due to the poor air quality in the Beijing area. Literature (Long, ZHen; Li, Huashan; Bu, Xiaonbiao; Ma, Weibin; ZHao, Liang. solar radial vertical surfaces for building application in differential closure of semiconductors China [ J]Renewable and susteable Energy,2013) found that the peak of total solar radiation on each monthly vertical plane occurs at a different azimuth within a ± 90 ° range. Literature (M.D. I.D. ez-Mediavilla, M.C. Rodri i g ez-Amigo, M.I. Diest-Velasco, et al. the PV patent of vertical
Aclasic improvement using experimental data from Burgos, Spain.2019,177: 192-. The literature (M.Cucumo, A.De Rosa, V.Ferraro, et al.Experimental testing of modules for the evaluation of the radiation of the solar radiation on vertical surfaces Arcavacatea di Rende.2006,81(5): 692) 695.) compares the isotropic and anisotropic calculation models, comparing the radiation data in the south, west, north and east vertical planes for more than 55,000 hours per hour, respectively, and analysis of the results shows that there is no significant difference between the predictions for the various models. Literature (calculation of hourly solar radiation on south wall in Cheng, Luo Hui Long, Kunming region [ J)]Scientific technology and engineering 2011,11(13): 3063-.
However, the existing south wall (south facing vertical wall) radiation prediction method has the following disadvantages:
1. most of the existing south wall radiation prediction methods are average daily scales of months and representative daily and hourly scales of months, and research on the hourly scales is less.
2. The existing weather type division mostly adopts a single index, for example: the definition index kt, the corrected definition index kt', the total cloud cover and the like neglect the influence of other factors on the weather type.
3. The existing solar radiation models are mainly proposed by foreigners, the data for establishing the models mainly come from the United states and European regions, and the models have larger errors when being directly used in China.
4. Different radiation models are suitable for different types of weather, and the accuracy is necessarily reduced if the model is used singly.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a south wall radiation prediction method based on solar radiation climate characteristic identification.
The purpose of the invention can be realized by the following technical scheme:
a south wall radiation prediction method based on solar radiation climate feature recognition comprises the following steps:
1) acquiring local historical data;
2) defining a weather type index SCF, and dividing the weather type of historical data based on the weather type index SCF;
3) and classifying the local measured data according to the weather type index SCF, and selecting a corresponding prediction model for radiation prediction according to different weather types.
In the step 1), the historical data includes radiation data, meteorological data, total radiation of the ground level and a definition index, the radiation data includes total radiation of the ground level, scattered radiation, direct radiation and reflected radiation data, and the meteorological data includes total cloud cover and visibility data.
In the step 2), the weather type index SCF and the corrected definition index k'TThe horizontal plane direct incidence ratio Bd is related to the total cloud cover C, and the expression is as follows:
SCF＝w1Bd+w2k′T+w3(1-C)
wherein, w1、w2、w3Are weights and the sum is 1.
In order to reduce the influence of the solar altitude on the definition index, the definition index is corrected, and then the definition index is correctedDegree index k'TThe expression of (a) is:
wherein k isTFor clarity index, m is the mass of the atmosphere, I is the total radiation in the horizontal plane, IoIs the solar radiation quantity on the horizontal plane outside the atmosphere.
In the step 3), when the solar altitude is smaller than 10 degrees, the process returns to the step S2.
In the step 2), the weather types are classified into 4 types according to the richness of the direct radiation component of the solar radiation by adopting a K-means clustering algorithm.
The weather types are specifically as follows:
type I: SCF value of 1-0.70, best condition, representing weather condition: sunny;
type II: SCF values of 0.70-0.48, with less favorable conditions, representing weather conditions: turning the sunny side to the cloudy side;
type III: SCF values of 0.48-0.24, poor conditions, representing weather conditions: cloudy, cloudy-cloudy;
type IV: SCF value of 0.24-0, worst condition, representing weather conditions: rain, rain gusts, snow, light fog, haze, rain and above, and/or snow and above.
In the step 3), the prediction model specifically comprises an isotropic Liu & Jordan model, an anisotropic Temps & Clouson model, a Perez model, a Kulcher model, a Hay model and a Reindl model.
When the weather type of the local measured data is type I, a Perez model is adopted, when the weather type of the local measured data is type II, a Liu & Jordan model is adopted, and when the weather type of the local measured data is type III and type IV, a Hay model is adopted.
And in the step 3), selecting a corresponding prediction model according to different weather types to perform radiation prediction according to an hourly scale.
Compared with the prior art, the invention has the following advantages:
the weather type index calculation formula adopted by the invention is simple, easy to quantify and easy to distinguish;
compared with a single model, the comprehensive model avoids the precision reduction phenomenon of the single model under a specific weather type through calculation, and selects the most suitable weather conditions of each model, so that the precision of the prediction model is remarkably improved.
And thirdly, the weather types can be accurately and effectively classified, and compared with the existing mode of singly adopting the total cloud amount or adopting the combination of the definition index and the cloud amount, the method is more accurate.
And fourthly, the hourly scale is adopted for prediction, so that the result is more accurate.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a comparison of models under different weather conditions, where fig. 2a is a comparison result of weather type I, fig. 2b is a comparison result of weather type II, fig. 2c is a comparison result of weather type III, and fig. 2d is a comparison result of weather type IV.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
The invention provides a comprehensive weather type index and effectively divides the weather types into four categories according to horizontal plane radiation data and meteorological environment data observed by a national-level service weather station Beijing station located in the suburb of the south of Beijing, and meteorological environment factors such as a corrected definition index, a direct incidence ratio and total cloud amount, and analyzes corresponding optimal models aiming at different weather types, thereby providing a south wall radiation prediction method with higher precision.
As shown in fig. 1, the specific process of the present invention is as follows:
s1: acquiring historical data, wherein the data time is one year, and the radiation data comprises total horizontal radiation, scattered radiation, direct radiation, reflected radiation and the like; the meteorological data comprise total cloud cover, visibility and the like, the data are screened and subjected to quality control, the data are removed, and the total extraterrestrial horizontal radiation, the definition index and the like are calculated according to the horizontal radiation observation value and the astronomical geographic factor;
s2: calculating a direct incidence ratio, correcting a definition index and total cloud volume data, calculating a weather type index SCF, and dividing the weather type index SCF into 4 types through a K-means clustering algorithm, wherein the 4 types are respectively a type I (SCF value is 1-0.70), a type II (SCF value is 0.70-0.48), a type III (SCF value is 0.48-0.24) and a type IV (SCF value is 0.24-0), and the richness of the direct solar radiation component of the solar radiation is reduced in sequence to obtain the solar altitude;
s3: judging whether the solar altitude is greater than 10 degrees or not, returning to the previous step for data screening when the solar altitude is too low to be less than 10 degrees because the accuracy of the model is reduced due to the excessively low solar altitude, and entering the next step until the solar altitude is greater than 10 degrees;
s4: obtaining a local division range based on the weather type index;
s5: acquiring actually measured data and dividing weather types;
s6: according to different weather types, different models are selected for prediction:
s6.1) if the current weather type is I type, selecting a Perez model for calculation;
s6.2) if the current weather type is II type, selecting a Liu & Jordan model;
s6.3) if the current weather type is III or IV, selecting a Hay model;
the weather type classification of step S5 specifically includes the following steps:
the clarity index is the ratio of the total solar radiation on the horizontal plane to the solar radiation on the horizontal plane outside the atmosphere, namely:
the radiation of the interface on the atmosphere is determined by the astronomical position of the earth, can be calculated, the definition index can be used for representing the attenuation of the atmosphere to the solar radiation and is a weather type classification index which is considered preferentially, and the larger the definition index is, the higher the atmosphere transparency is, the less the attenuation of the atmosphere to the solar radiation is, and the larger the solar radiation reaching the ground is.
However, the clarity index is not only related to meteorological conditions but also to the position of the sun in the sky, and is modified as follows in order to reduce the influence of the solar altitude on the clarity index:
wherein, k'TFor the corrected sharpness index, m is the atmospheric mass.
The corrected three data of the definition index, the direct radiation ratio (the proportion of the horizontal plane direct radiation exposure in the total radiation exposure) and the total cloud amount (the number of cloud-shielded sky vision) are adopted, normalized and weighted respectively to obtain a comprehensive index factor which is named as SCF (sky condition factor), namely a weather type index, and the specific calculation formula is as follows:
SCF＝w1Bd+w2k'T+w3(1-C)
wherein, w1，w2And w3The sum of (1) is a specific numerical value calculated according to the local geographic position and the radiation data, C represents the total cloud cover, and the total cloud cover is defined as the number of cloud cover sky in the example, and represents the percentage of the range covered by the cloud cover in the sky to the total sky range.
The weather type index contains three key information influencing photovoltaic power generation, and weather type division can be performed according to the parameter. Dividing the weather into four categories by a K-Means clustering algorithm, wherein the weather type I has the best condition and is sunny; weather type II is from sunny to cloudy; the weather type III mainly comprises cloudy, cloudy-cloudy and the like; weather type IV belongs to inclement weather, including weather conditions of light rain, gust rain, light snow, light fog, haze, medium rain and above, medium snow and above, and the like.
According to the classification result, a weather division standard based on the weather type index can be obtained, and the weather type can be determined in real time by comparing the standard with an actual measurement value, so that a proper radiation model is adopted.
In step S6, the comparison between the different models selected according to different weather types is specifically:
in the scheme, representative models are selected, namely an isotropic Liu & Jordan model, an anisotropic Temps & Clouson model, a Perez model, a Kulcher model, a Hay model and a Reindl model, and are calculated and analyzed through actual measurement data of the south wall in the suburb of Beijing south China. Under the weather type I, the calculated values of the isotropic model and most of the anisotropic models are lower than the actual measured values, and the Perez model is the closest to the weather type I, mainly because the radiation component of the weather type I is mainly direct radiation; in weather type II, the error between the Liu & Jordan model and the measured value is minimum, and the calculated values of other models are all low; in weather types III and IV, the Hay model has the smallest error with the measured value, and the calculated values of other models are all lower.
As shown in FIG. 2, the ideal values of MBE, MAPE and NRMSE are 0, and the smaller the value is, the higher the precision is, wherein MBE >0, the more the predicted value is, and the less the observed value is. NRMSE is often used to measure the deviation between an observed value and a true value, is very sensitive to the response of extra-large or extra-small errors in a set of measured data, and indicates that the model is more accurate when the value is smaller. CORR is a statistical index for representing the closeness of correlation between variables, and the closer to 1, the higher the correlation between the calculated value and the measured value.
Claims (10)
1. A south wall radiation prediction method based on solar radiation climate feature recognition is characterized by comprising the following steps:
1) acquiring local historical data;
2) defining a weather type index SCF, and dividing the weather type of historical data based on the weather type index SCF;
3) and classifying the local measured data according to the weather type index SCF, and selecting a corresponding prediction model for radiation prediction according to different weather types.
2. The method as claimed in claim 1, wherein in step 1), the historical data includes radiation data, meteorological data, and extraterrestrial level total radiation and clarity index, the radiation data includes level total radiation, scattered radiation, direct radiation, and reflected radiation data, and the meteorological data includes total cloud cover and visibility data.
3. The south wall radiation prediction method based on solar radiation climate feature recognition according to claim 1, wherein in the step 2), the weather type index SCF and the corrected definition index k'TThe horizontal plane direct incidence ratio Bd is related to the total cloud cover C, and the expression is as follows:
SCF＝w1Bd+w2k′T+w3(1-C)
wherein, w1、w2、w3Are weights and the sum is 1.
4. The method as claimed in claim 3, wherein the definition index is corrected according to the method for predicting the photovoltaic power based on the solar radiation climate feature identification, wherein the definition index k 'is corrected according to the method for predicting the photovoltaic power based on the solar radiation climate feature identification, so as to reduce the influence of the solar altitude on the definition index'TThe expression of (a) is:
wherein k isTFor clarity index, m is the mass of the atmosphere, total radiation in the horizontal plane, IoIs the amount of solar radiation on the horizontal plane outside the atmosphere。
5. The method for predicting photovoltaic power based on solar radiation climate characteristic identification as claimed in claim 1, wherein in step 3), when the solar altitude is less than 10 degrees, the method returns to step S2.
6. The photovoltaic power prediction method based on solar radiation climate characteristic identification as claimed in claim 1, wherein in the step 2), a K-means clustering algorithm is adopted to classify the weather types into 4 categories according to the richness of the direct radiation component of the solar radiation.
7. The photovoltaic power prediction method based on solar radiation climate feature identification as claimed in claim 6, wherein the weather types are specifically:
type I: SCF value of 1-0.70, best condition, representing weather condition: sunny;
type II: SCF values of 0.70-0.48, with less favorable conditions, representing weather conditions: turning the sunny side to the cloudy side;
type III: SCF values of 0.48-0.24, poor conditions, representing weather conditions: cloudy, cloudy-cloudy;
type IV: SCF value of 0.24-0, worst condition, representing weather conditions: rain, rain gusts, snow, light fog, haze, rain and above, and/or snow and above.
8. The method according to claim 7, wherein in the step 3), the prediction models specifically include an isotropic Liu & Jordan model, an anisotropic Temps & Clouson model, a Perez model, a Kulcher model, a Hay model, and a Reindl model.
9. The method as claimed in claim 8, wherein when the type of the locally measured data is type I, a Perez model is used, when the type of the locally measured data is type II, a Liu & Jordan model is used, and when the type of the locally measured data is type III or type IV, a Hay model is used.
10. The photovoltaic power prediction method based on solar radiation climate feature identification as claimed in claim 1, wherein in the step 3), the radiation prediction is performed according to hourly scale by selecting corresponding prediction models according to different weather types.
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