CN103984991A - Distributed type solar radiation prediction method and system based on micro-meteorology data - Google Patents
Distributed type solar radiation prediction method and system based on micro-meteorology data Download PDFInfo
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Abstract
The invention relates to a distributed type solar radiation prediction method based on micro-meteorology data. The distributed type solar radiation prediction method comprises the following steps that S100, a micro-meteorology collection device collects, processes and stores real-time weather data and sends the real-time weather data to a data service center regularly, and historical weather data are formed in the data service center; S200, the data service center collects and processes future weather forecast data; S300, the historical weather data obtained in the S100 are summed up and sorted up, multiple regression data analysis is carried out on the historical weather data, the atmospheric transmissivity of each period of ten days within the next year and the sunshine durations corresponding to the sunny days, the cloudy days and the rainy days of every month are obtained and are combined with the future weather forecast data obtained in the S200, and the sun radiation intensity of the future days can be obtained. The distributed type solar radiation prediction method based on the micro-meteorology data can accurately predict the solar radiation intensity of the future days.
Description
Technical field
The present invention relates to a kind of solar radiation Forecasting Methodology and system, be specifically related to a kind of distributed solar energy radiation Forecasting Methodology and system based on microclimate data.
Background technology
Global distributed photo-voltaic power generation station is built like a raging fire, domesticly advances triumphantly especially.Follow distributed photovoltaic power generation system day by day to promote power distribution network permeability, its negative effect that power distribution network economical operation and the quality of power supply are produced is increasing, has become problem in the urgent need to address.Because solar energy power generating output power has non-scheduling, intermittence, stochastic volatility feature, at present, photovoltaic generation power prediction technology still in exploration, conceptual phase, does not have generally acknowledged mature technology and product at home and abroad, can not accurately predict the intensity of solar radiation.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of distributed solar energy radiation Forecasting Methodology and system based on microclimate data, can dope more accurately the not solar radiation intensity in the future.
The technical scheme that the present invention solves the problems of the technologies described above is as follows: a kind of distributed solar energy radiation Forecasting Methodology based on microclimate data, comprises the following steps:
Step S100, store by the real-time weather data of microclimate harvester acquisition and processing and to the real-time weather data gathering, and by real-time weather data timed sending to data service center, and in data service center, form the weather history data of a year in the past;
Step S200, collects and processes long-range weather forecast data by data service center;
Step S300, weather history data in step S100 are carried out to induction-arrangement and multiple regression data analysis, draw the atmospheric transmittance in each ten days and sun percentage of possible sunshine corresponding to the fine day of every month, cloudy day and rainy day in following a year, the long-range weather forecast data that obtain in integrating step S200 again, draw the intensity of solar radiation of default day in following 1 year.
On the basis of technique scheme, the present invention can also do following improvement.
Further, in described step S300, weather history data in step S100 are carried out to induction-arrangement and multiple regression data analysis, show that the method for the sun percentage of possible sunshine that in following a year, the atmospheric transmittance in each ten days and the fine day of every month, cloudy day and rainy day are corresponding comprises the following steps
Step S301, classifies the weather history data that obtain by fine day, rainy day, these 3 kinds of weather conditions of cloudy day;
Step S302, carries out arrangement from small to large by the percentage of possible sunshine in the weather history data through classification according to the atmospheric humidity in weather history data;
Step S303, respectively to fine day, rainy day, in cloudy day, the percentage of possible sunshine carrying out according to the atmospheric humidity in weather history data in the weather history data of arrangement from small to large carries out multiple regression processing by least square method Computing Principle, obtain the computing formula of sun percentage of possible sunshine, the computing formula of described sun percentage of possible sunshine is y=a1*x^3+a2*x^2+a3*x+a4, wherein, a1, a2, a3 and a4 are the coefficient of the sun percentage of possible sunshine computing formula that draws by multiple regression algorithm, x is that in long-range weather forecast data, the atmospheric humidity of default day deducts after the minimum humidity in weather history data divided by the value after 5, y is sun percentage of possible sunshine.
Further, in described step S300, the long-range weather forecast data in integrating step S200, show that the method for the intensity of solar radiation of default day in following a year is,
Step S304, by quilt shine upon the setting angle of object, highly, orientation and irradiation time, and atmospheric transmittance calculates corresponding sun sunshine amount in the unit interval;
Step S305, the corresponding sun percentage of possible sunshine of obtaining by the atmospheric humidity in the long-range weather forecast data in step S200 and weather condition and step S300 calculates in following 1 year the intensity of solar radiation of default day, intensity of solar radiation=dip plane sunshine amount * percentage of possible sunshine+surface level sunshine amount of default day in described following a year.
Further, in described step S100, if the timed sending of described microclimate harvester is correct data to the real-time weather data of data service center, described data service center is to the real-time weather data of the service centre's timing extraction that clears data from the database of microclimate harvester after microclimate harvester returns to correct data receiver instruction.
Further, in described step S100, if the timed sending of described microclimate harvester is empty data to the real-time weather data of data service center, described data service center sends microclimate operating performance of plant confirmation signal to described microclimate harvester, and judges ruuning situation and the data communication situation of microclimate harvester.
Further, in described step S100, if the timed sending of described microclimate harvester is misdata to the real-time weather data of data service center, described data service center sends self-inspection request to described microclimate harvester, described microclimate harvester receives after self-inspection request, first self is checked, then check result is turned back to described data service center.
Further, the process of processing real-time weather data by microclimate harvester comprises analysis, arranges real-time weather data and rejects wrong and unwanted real-time weather data, and the process of processing following data of weather forecast by data service center comprises analysis, arrange following data of weather forecast and reject the data of weather forecast in unwanted future.
Further, also comprise step S400, described step S400 is that the solar radiation of presetting day in described following 1 year predicts the outcome and in following 1 year, presets the actual weather data of day and carry out degree of accuracy contrast, and carries out shining solar day the fixed value coefficient adjustment in the computing formula of rate.
The invention has the beneficial effects as follows: one of the present invention is based on the distributed solar radiation Forecasting Methodology of microclimate data, can dope more accurately the not solar radiation intensity in the future, for effective reduction energy storage cost, the raising photovoltaic generation quality of power supply and schedulability, the friendly of electrical network is all had to Great significance.
Based on above-mentioned one, based on the distributed solar radiation Forecasting Methodology of microclimate data, it is a kind of based on the distributed solar radiation prognoses system of microclimate data that the present invention also provides.
A distributed solar energy radiation prognoses system based on microclimate data, comprises microclimate harvester and data service center, and microclimate harvester is connected with described data service center,
Described microclimate harvester for the real-time weather data of acquisition and processing and to gather real-time weather data store, and by real-time weather data timed sending to data service center;
The real-time weather data that described data service center passes over microclimate harvester forms the weather history data of a year in the past, described data service center is also for collecting and process long-range weather forecast data from observatory, described data service center is also for carrying out induction-arrangement and multiple regression data analysis to weather history data, draw the atmospheric transmittance in each ten days and the fine day of every month in following a year, cloudy day and sun percentage of possible sunshine corresponding to rainy day, and in conjunction with the long-range weather forecast data that obtain, draw the intensity of solar radiation of default day in following 1 year.
Further, the computing formula of described sun percentage of possible sunshine is y=a1*x^3+a2*x^2+a3*x+a4, wherein, a1, a2, a3 and a4 are the coefficient of the sun percentage of possible sunshine computing formula that draws by multiple regression algorithm, x is that the atmospheric humidity in default moment in long-range weather forecast data deducts after the minimum humidity in weather history data divided by the value after 5, and y is sun percentage of possible sunshine;
Described intensity of solar radiation=dip plane sunshine amount * percentage of possible sunshine+surface level sunshine amount.
Brief description of the drawings
Fig. 1 is a kind of distributed solar energy radiation Forecasting Methodology process flow diagram based on microclimate data of the present invention.
Fig. 2 is the structured flowchart of a kind of distributed solar energy radiation prognoses system based on microclimate data of the present invention.
Embodiment
Below in conjunction with accompanying drawing, principle of the present invention and feature are described, example, only for explaining the present invention, is not intended to limit scope of the present invention.
As shown in Figure 1, the present embodiment is a kind of distributed solar energy radiation Forecasting Methodology based on microclimate data, comprises following content,
S100, gathers real-time weather data, carries out data analysis arrangement, is stored in microclimate harvester after rejecting wrong and unwanted real-time weather data, and timed sending is to data service center, and in data service center history of forming weather data; Described weather history data are the weather data in past 1 year; The data of rejecting from the real-time weather data the inside of obtaining are for removing date, time, weather condition, temperature, rainfall amount, temperature, humidity, sunshine-duration, the data beyond insolation amount, then revise the small misdata of these data the insides that remain, such as weather condition is rain, the still rainfall such as snow, the data that snowfall is 0, this Data induction is cloudy data; The data of collecting that judge of just omiting are rejected the misdata causing because of microclimate harvester problem, and if most of data of collecting in this time point are 0, or the sunshine-duration exceedes maximal value etc.; Finally the data that collect are divided three classes, and are stored in database, three class data are respectively, and one, fine: refering in particular to weather condition is at that time fine day; Two, the cloudy day: how to comprise cloudy day, the moon; Three, rain: be the weather condition (comprising shower, heavy rain, moderate rain, light rain, all rainfalls such as snow, the data that snowfall is greater than 0) beyond, two.
S200, collects following data of weather forecast, and the line number of going forward side by side arranges, rejects unnecessary long-range weather forecast data according to one's analysis.
Long-range weather forecast data are arranged, reject temperature, humidity, weather condition is (fine, rain, many cloudy daies etc.) data in addition, and the data of collecting are divided three classes, and be stored in database, three class data are respectively, and one, fine: refering in particular to weather condition is at that time fine day; Two, the cloudy day: how to comprise cloudy day, the moon; Three, rain: be the weather condition (comprising shower, heavy rain, moderate rain, light rain, all rainfalls such as snow, the data that snowfall is greater than 0) beyond, two.
S300, weather history data in S100 are carried out to induction-arrangement and multiple regression data analysis, draw the atmospheric transmittance in each ten days and sun percentage of possible sunshine corresponding to the fine day of every month, cloudy day and rainy day in following a year, in conjunction with the long-range weather forecast data that obtain in S200, draw the intensity of solar radiation of default day in following 1 year again; Weather history data in step S100 are carried out to induction-arrangement and multiple regression data analysis, show that the method for the sun percentage of possible sunshine that in following a year, the atmospheric transmittance in each ten days and the fine day of every month, cloudy day and rainy day are corresponding comprises the following steps,
Step S301, by the weather history data that obtain by fine day, rainy day, cloudy day this in 3 weather condition classify;
Step S302, carries out arrangement from small to large by the percentage of possible sunshine in the weather history data through classification according to the atmospheric humidity in weather history data;
Step S303, respectively in fine day, rainy day, cloudy day, carry out percentage of possible sunshine in the weather history data of arrangement from small to large (least square method is a kind of mathematical optimization technology by least square method Computing Principle according to the atmospheric humidity in weather history data, find the optimal function coupling of data by the quadratic sum of minimum error, utilize least square method can try to achieve easily unknown data, and make the quadratic sum of error between these data of trying to achieve and real data for minimum.) carry out multiple regression processing (operational data is atmospheric humidity and the percentage of possible sunshine of weather history data), obtain the computing formula y=a1*x^3+a2*x^2+a3*x+a4 of 3 order polynomials, this computing formula is sun percentage of possible sunshine computing formula, wherein, a1, a2, a3 and a4 are the coefficient of the sun percentage of possible sunshine computing formula that draws by multiple regression algorithm, x is that in long-range weather forecast data, the atmospheric humidity of default day deducts after the minimum humidity in weather history data divided by the value after 5, and y is sun percentage of possible sunshine.The establishing method of minimum humidity is: because in the process by multiple regression procedure reckoning computing formula, first the data of collecting are classified, be divided into fine day data, cloudy day data and rainy day data, then sort from big to small according to the atmospheric humidity of data the inside, start to search from first data, getting first can be by 5 atmospheric humidity that divide exactly, if in all atmospheric humidity data, be less than or equal to first by the number of the data of 5 atmospheric humiditys that divide exactly more than 10 or 10, setting this atmospheric humidity is minimum humidity, for example, first can be 35 by 5 atmospheric humiditys that divide exactly data, so in all atmospheric humidity data, atmospheric humidity data are less than or equal to 35 data more than 10 or 10, setting this atmospheric humidity 35 is minimum humidity, otherwise continue down to search, until first data that meet above-mentioned condition occur, the atmospheric humidity of setting these data is minimum humidity.X set former because: because carrying out in the process of multivariate linear equation, we are that one 5 humidity units are a scale for the setting of humidity, getting 5 humidity unit's the inside percentage of possible sunshine mean values is that the corresponding percentage of possible sunshine of this unit maximal humidity carries out reasoning, as: the percentage of possible sunshine mean value of 40 humidity unit-45 humidity units is 0.725, sets percentage of possible sunshine mean value corresponding to 45 humidity units and is 0.725 and then carry out multivariate linear equation.
In conjunction with the long-range weather forecast data in data service center, the method that draws the intensity of solar radiation of default day in following a year is: the setting angle that first shines upon object by quilt, highly, orientation and irradiation time (taking minute as unit), and atmospheric transmittance calculates corresponding sun sunshine amount (dip plane sunshine amount+surface level sunshine amount in the unit interval, percentage of possible sunshine corresponding to result of calculation is 1, this module is called sunshine amount computing module in computing method the inside), then by the atmospheric humidity in the data of weather forecast in data service center, and weather condition is (fine, rain, cloudy day) and step S300 in the corresponding sun percentage of possible sunshine obtained calculate the intensity of solar radiation of default day in following 1 year, intensity of solar radiation=dip plane insolation amount * percentage of possible sunshine+surface level insolation amount of default day in described following 1 year.
Step S400, the solar radiation of default day in described following a year is predicted the outcome and in following 1 year the actual weather data of default day carry out degree of accuracy contrast, and carry out solar day according to the fixed value coefficient adjustment in the computing formula of rate.
In process in the timed sending of microclimate harvester to the real-time weather data of data service center, if when the timed sending of described microclimate harvester is correct data to the real-time weather data of data service center, described data service center is to the real-time weather data of the service centre's timing extraction that clears data from the database of microclimate harvester after microclimate harvester returns to correct data receiver instruction.If when the timed sending of described microclimate harvester is empty data to the real-time weather data of data service center, described data service center sends microclimate operating performance of plant confirmation signal to described microclimate harvester, and judges ruuning situation and the data communication situation of microclimate harvester.If when the timed sending of described microclimate harvester is misdata to the real-time weather data of data service center, described data service center sends self-inspection request to described microclimate harvester, described microclimate harvester receives after self-inspection request, first self is checked, then check result is turned back to described data service center.
A kind of distributed solar energy radiation Forecasting Methodology based on microclimate data of the present embodiment can dope the not solar radiation intensity in the future more accurately, thereby for distributed photovoltaic power generation control system provides decision-making foundation, realizing adaptive power regulates and dynamic energy management, for effective reduction energy storage cost, the raising photovoltaic generation quality of power supply and schedulability, the friendly of electrical network is all had to Great significance.
Fig. 2 is the structured flowchart of a kind of distributed solar energy radiation prognoses system based on microclimate data of the present embodiment.A kind of distributed solar energy radiation prognoses system based on microclimate data, comprise microclimate harvester and data service center, in described microclimate harvester, be provided with microclimate harvester database, in described data service center, be provided with data service center database, microclimate harvester is connected with described data service center
Described microclimate harvester is used for gathering real-time weather data, carry out being stored in microclimate harvester database after data analysis arrangement, rejecting mistake and unwanted real-time weather data, and regularly from microclimate harvester database, extracting real-time weather data sends to data service center;
The real-time weather data that described data service center passes over microclimate harvester is stored in the weather history data that form past 1 year in data service center database, described data service center is also for collecting following data of weather forecast from observatory, the line number of going forward side by side arranges according to one's analysis, reject unnecessary long-range weather forecast data, described data service center also carries out induction-arrangement and multiple regression data analysis for the weather history data to data service center database, draw the atmospheric transmittance in each ten days and the fine day of every month in following a year, cloudy day and sun percentage of possible sunshine corresponding to rainy day, and in conjunction with the long-range weather forecast data that obtain, draw the intensity of solar radiation of default day in following 1 year.The computing formula of described sun percentage of possible sunshine is y=a1*x^3+a2*x^2+a3*x+a4, wherein, a1, a2, a3 and a4 are the coefficient of the sun percentage of possible sunshine computing formula that draws by multiple regression algorithm, x is that in long-range weather forecast data, the atmospheric humidity of default day deducts after the minimum humidity in weather history data divided by the value after 5, and y is sun percentage of possible sunshine; Intensity of solar radiation=dip plane sunshine amount * percentage of possible sunshine+surface level sunshine amount of default day in described following 1 year.
In data service center, weather history data in the database of data service center (weather history data are the weather data in past 1 year) are carried out to induction-arrangement and multiple regression data analysis, draw the atmospheric transmittance in each ten days and the fine day of every month in following a year, the computing formula of cloudy day and sun percentage of possible sunshine corresponding to rainy day, and the atmospheric transmittance in each ten days and the fine day of every month in following a year will be drawn, the computing formula cloudy and sun percentage of possible sunshine that the rainy day is corresponding is stored in the database of data service center, described data service center extracts historical data and the atmospheric transmittance in each ten days and the fine day of every month in following 1 year again from the database of data service center, the computing formula of cloudy day and sun percentage of possible sunshine corresponding to rainy day, again in conjunction with the long-range weather forecast data that obtain in data service center, draw the not intensity of solar radiation in the future, data service center predicts the outcome sky sun energy radiation to send to user.
In process in the timed sending of microclimate harvester to the real-time weather data of data service center, if when the timed sending of described microclimate harvester is correct data to the real-time weather data of data service center, described data service center is to the real-time weather data of the service centre's timing extraction that clears data from the database of microclimate harvester after microclimate harvester returns to correct data receiver instruction.If when the timed sending of described microclimate harvester is empty data to the real-time weather data of data service center, described data service center sends microclimate operating performance of plant confirmation signal to described microclimate harvester, and judges ruuning situation and the data communication situation of microclimate harvester.If when the timed sending of described microclimate harvester is misdata to the real-time weather data of data service center, described data service center sends self-inspection request to described microclimate harvester, described microclimate harvester receives after self-inspection request, first self is checked, then check result is turned back to described data service center.After data service center judges that microclimate harvester goes wrong, can send the error message of the microclimate device of collecting to data monitoring management personnel, allow maintenance personal that microclimate harvester is keeped in repair or to be changed.
The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any amendment of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (10)
1. the distributed solar energy radiation Forecasting Methodology based on microclimate data, is characterized in that: comprise the following steps:
Step S100, by the real-time weather data of microclimate harvester acquisition and processing, and the real-time weather data gathering is stored, real-time weather data timed sending, to data service center, and is formed to the weather history data of a year in the past in data service center;
Step S200, collects and processes long-range weather forecast data by data service center;
Step S300, weather history data in step S100 are carried out to induction-arrangement and multiple regression data analysis, draw the atmospheric transmittance in each ten days and sun percentage of possible sunshine corresponding to the fine day of every month, cloudy day and rainy day in following a year, the long-range weather forecast data that obtain in integrating step S200 again, draw the intensity of solar radiation of default day in following 1 year.
2. a kind of distributed solar energy radiation Forecasting Methodology based on microclimate data according to claim 1, it is characterized in that: in described step S300, weather history data in step S100 are carried out to induction-arrangement and multiple regression data analysis, the method that draws the sun percentage of possible sunshine that in following a year, the atmospheric transmittance in each ten days and the fine day of every month, cloudy day and rainy day are corresponding comprises the following steps
Step S301, classifies the weather history data that obtain by fine day, rainy day, these 3 kinds of weather conditions of cloudy day;
Step S302, carries out arrangement from small to large by the percentage of possible sunshine in the weather history data through classification according to the atmospheric humidity in weather history data;
Step S303, respectively to fine day, rainy day, in cloudy day, the percentage of possible sunshine carrying out according to the atmospheric humidity in weather history data in the weather history data of arrangement from small to large carries out multiple regression processing by least square method Computing Principle, obtain the computing formula of sun percentage of possible sunshine, the computing formula of described sun percentage of possible sunshine is y=a1*x^3+a2*x^2+a3*x+a4, wherein, a1, a2, a3 and a4 are the coefficient of the sun percentage of possible sunshine computing formula that draws by multiple regression algorithm, x is that in long-range weather forecast data, the atmospheric humidity of default day deducts after the minimum humidity in weather history data divided by the value after 5, y is sun percentage of possible sunshine.
3. a kind of distributed solar energy radiation Forecasting Methodology based on microclimate data according to claim 1, it is characterized in that: in described step S300, long-range weather forecast data in integrating step S200, show that the method for the intensity of solar radiation of default day in following a year is
Step S304, by quilt shine upon the setting angle of object, highly, orientation and irradiation time, and atmospheric transmittance calculates corresponding sun sunshine amount in the unit interval;
Step S305, the corresponding sun percentage of possible sunshine of obtaining by the atmospheric humidity in the long-range weather forecast data in step S200 and weather condition and step S300 calculates in following 1 year the intensity of solar radiation of default day, intensity of solar radiation=dip plane sunshine amount * percentage of possible sunshine+surface level sunshine amount of default day in described following a year.
4. according to a kind of distributed solar energy radiation Forecasting Methodology based on microclimate data described in claims 1 to 3 any one, it is characterized in that: in described step S100, if the timed sending of described microclimate harvester is correct data to the real-time weather data of data service center, described data service center is to the real-time weather data of the service centre's timing extraction that clears data from the database of microclimate harvester after microclimate harvester returns to correct data receiver instruction.
5. according to a kind of distributed solar energy radiation Forecasting Methodology based on microclimate data described in claims 1 to 3 any one, it is characterized in that: in described step S100, if the timed sending of described microclimate harvester is empty data to the real-time weather data of data service center, described data service center sends microclimate operating performance of plant confirmation signal to described microclimate harvester, and judges ruuning situation and the data communication situation of microclimate harvester.
6. according to a kind of distributed solar energy radiation Forecasting Methodology based on microclimate data described in claims 1 to 3 any one, it is characterized in that: in described step S100, if the timed sending of described microclimate harvester is misdata to the real-time weather data of data service center, described data service center sends self-inspection request to described microclimate harvester, described microclimate harvester receives after self-inspection request, first self is checked, then check result is turned back to described data service center.
7. according to a kind of distributed solar energy radiation Forecasting Methodology based on microclimate data described in claims 1 to 3 any one, it is characterized in that: the process of processing real-time weather data by microclimate harvester comprises analysis, arranges real-time weather data and reject wrong and unwanted real-time weather data, and the process of processing following data of weather forecast by data service center comprises analysis, arranges following data of weather forecast and rejects unwanted long-range weather forecast data.
8. according to a kind of distributed solar energy radiation Forecasting Methodology based on microclimate data described in claims 1 to 3 any one, it is characterized in that: also comprise step S400, described step S400 is that the solar radiation of presetting day in described following 1 year predicts the outcome and in following 1 year, presets the actual weather data of day and carry out degree of accuracy contrast, and carries out shining solar day the fixed value coefficient adjustment in the computing formula of rate.
9. the distributed solar energy radiation prognoses system based on microclimate data, is characterized in that: comprise microclimate harvester and data service center, microclimate harvester is connected with described data service center,
Described microclimate harvester for the real-time weather data of acquisition and processing and to gather real-time weather data store, and by real-time weather data timed sending to data service center;
The real-time weather data that described data service center passes over microclimate harvester forms the weather history data of a year in the past, described data service center is also for collecting and process long-range weather forecast data from observatory, described data service center is also for carrying out induction-arrangement and multiple regression data analysis to weather history data, draw the atmospheric transmittance in each ten days and the fine day of every month in following a year, cloudy day and sun percentage of possible sunshine corresponding to rainy day, and in conjunction with the long-range weather forecast data that obtain, draw the intensity of solar radiation of default day in following 1 year.
10. a kind of distributed solar energy radiation prognoses system based on microclimate data according to claim 9, it is characterized in that: the computing formula of described sun percentage of possible sunshine is y=a1*x^3+a2*x^2+a3*x+a4, wherein, a1, a2, a3 and a4 are the coefficient of the sun percentage of possible sunshine computing formula that draws by multiple regression algorithm, x is that in long-range weather forecast data, the atmospheric humidity of default day deducts after the minimum humidity in weather history data divided by the value after 5, and y is sun percentage of possible sunshine;
Described intensity of solar radiation=dip plane sunshine amount * percentage of possible sunshine+surface level sunshine amount.
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| CN106849096A (en) * | 2017-03-15 | 2017-06-13 | 云南电网有限责任公司电力科学研究院 | A kind of user side smart micro-grid system |
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| CN107884067A (en) * | 2017-11-03 | 2018-04-06 | 东莞理工学院 | Solar irradiance fluctuation identification and forecasting procedure based on the analysis of polynary Characteristics of micrometeorology |
| CN114900124A (en) * | 2022-04-12 | 2022-08-12 | 广东绿建联能源环境科技有限公司 | Photovoltaic power generation detection system and detection method thereof |
| CN114900124B (en) * | 2022-04-12 | 2023-01-24 | 广东绿建联能源环境科技有限公司 | Photovoltaic power generation detection system and detection method thereof |
| CN115423200A (en) * | 2022-09-16 | 2022-12-02 | 南通沃太新能源有限公司 | Method for predicting photovoltaic power by complementing solar irradiation in off-line state |
| CN115423200B (en) * | 2022-09-16 | 2023-12-29 | 南通沃太新能源有限公司 | Method for predicting photovoltaic power by supplementing solar irradiation in off-line state |
| CN116070335A (en) * | 2023-04-03 | 2023-05-05 | 中通服建设有限公司 | Integrated photovoltaic building design method |
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