CN103390199A - Photovoltaic power generation capacity/power prediction device - Google Patents
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Abstract
本发明公开了一种光伏发电量/发电功率预测装置，包括监测系统数据读取模块、数据库、数据处理模块、天气信息处理模块和预测模块；数据读取模块，读取监测系统实时监测到的光伏电站数据信息；数据库，存储预测所需的数据信息；数据处理模块，对数据库中的历史发电量/发电功率数据和各类天气数据利用模糊聚类分析法进行分类和分类结果评价，得到相似日样本集；天气预报信息处理模块，对天气预报数据进行处理；预测模块，包括BP预测子模块、灰色预测子模块以及支持向量机预测子模块；将各个预测子模块的预测结果进行组合得到最终预测结果。本发明将实时信息监测、预测功能结合在一起，得到预测结果，提供了完整的技术解决方案。
The invention discloses a photovoltaic power generation/power generation forecasting device, which includes a monitoring system data reading module, a database, a data processing module, a weather information processing module and a forecasting module; Photovoltaic power station data information; database, which stores the data information required for prediction; data processing module, uses fuzzy cluster analysis method to classify and classify the historical power generation/power data and various weather data in the database, and obtains similar Daily sample set; weather forecast information processing module, which processes weather forecast data; forecast module, including BP forecast sub-module, gray forecast sub-module and support vector machine forecast sub-module; combine the forecast results of each forecast sub-module to obtain the final forecast result. The invention combines real-time information monitoring and forecasting functions to obtain forecasting results and provides a complete technical solution.
Description
Technical field
The invention belongs to the photovoltaic power generation technology field, relate to a kind of photovoltaic power generation quantity/generated power forecasting device.
Background technology
Solar energy power generating is to utilize the photovoltaic effect of solar cell solar radiant energy directly to be converted to a kind of forms of electricity generation of electric energy.Sun power is the inexhaustible regenerative resources of the mankind, have sufficient spatter property, absolute security, relative popularity, certain long-life and non-maintaining property, the abundance of resource and potential advantages such as economy, have critical role in long-term energy strategy.Along with the problems such as global energy shortage and environmental pollution become increasingly conspicuous, solar energy power generating is because of its cleaning, safety, facility, the characteristics such as efficient, the new industry that has become the countries in the world common concern and given priority to.But due to photovoltaic generation diurnally generate electricity, be subjected to Changes in weather to affect very large, capacity confidence level and the time confidence level lower, so the output power of photovoltaic generation has strong variations and intermittent characteristics, and can not can freely control as thermoelectricity is the same with water power, must have a certain impact to operation of power networks and scheduling after entering electrical network, have a strong impact on the balance of electrical network.Starting late of solar energy power generating power prediction, also do not have ripe photovoltaic power generation quantity/generated power forecasting system at present.
Summary of the invention
The object of the invention is to: photovoltaic power generation quantity/generated power forecasting device, simple in structure, be easy to realize, accuracy is high.
Technical solution of the present invention is: this photovoltaic power generation quantity/generated power forecasting device comprises: the monitoring system data read module is used for reading the data message of the photovoltaic plant that monitoring system real-time monitors; Database, be used for the data message that storage monitoring system data read module reads; Data processing module,, for the historical generated energy to database/generated output data and all kinds of weather data, utilize method of fuzzy cluster analysis to classify and the classification results evaluation, obtains similar day sample set; The weather forecast information processing module, be used for the data of weather forecast that meteorological department provides is processed, and Weather information is described with numerical information; Prediction module, comprise BP predictor module, gray prediction submodule, SVM prediction submodule, above-mentioned submodule is predicted respectively photovoltaic power generation quantity/generated output, obtain photovoltaic power generation quantity/generated power forecasting result, then the photovoltaic power generation quantity of each prediction module/generated power forecasting result is made up, obtain the combined prediction value.
BP predictor module in apparatus of the present invention adopts the BP neural network, generated energy/the generated output that to transfer from data processing module, weather data information,, as input quantity, obtain generated energy/generated power forecasting result with the weather forecast data of having described with numerical information of transferring from the weather forecast processing module; BP neural network wherein is the artificial neural network based on error backpropagation algorithm; Weather data information is including, but not limited to weather pattern, intensity of illumination, temperature, humidity information.
SVM prediction submodule in apparatus of the present invention adopts support vector machine, to transfer generated energy/generated output, weather data information from data processing module,, as input quantity, obtain generated energy/generated power forecasting result with the weather forecast data of having described with numerical information of transferring from the weather forecast processing module.
Gray prediction submodule in apparatus of the present invention adopts gray theory, will transfer generated energy/generated output as input quantity from data processing module, obtains generated energy/generated power forecasting result.
In the prediction module of apparatus of the present invention, each submodule carries out the photovoltaic generation power prediction based on similar day sample set; Data processing module is classified and the classification results evaluation, and the concrete steps that obtain similar day sample set are:
1) choose temperature, humidity and weather pattern as similar day proper vector, then temperature profile vector sum Humidity Features vector is carried out respectively normalized, simultaneously the weather pattern proper vector is mapped as numerical value;
2) the temperature profile vector sum Humidity Features vector that normalized is good, and the weather pattern proper vector historical data that is mapped as numerical value is carried out cluster analysis, namely utilize method of fuzzy cluster analysis to try to achieve Data classification result under different clusters number, the sample with identical weather characteristics poly-be a class;
3) try to achieve the Xie-Beni Validity Index Vxb of all Data classifications according to the Cluster Assessment target function, then with the Data classification of Validity Index Vxb minimum as cluster result, form similar day sample set.
In the prediction module of apparatus of the present invention, predicting the outcome of each prediction module made up, and is to make up according to least variance method, and namely adopting minimum variance is optimization aim, obtain the weight coefficient between various Forecasting Methodologies, then make up and obtain the combined prediction value.
Prediction unit of the present invention, choose generated energy in each predictor module and generated energy predicted as input quantity; Choosing generated output can realize generated output is predicted as input quantity.
The present invention has the following advantages: photovoltaic generation is subjected to weather effect to have more greatly randomness and intermittence, a certain fixing forecast model can not adapt to all situations fully, technology adopts the Individual forecast method can not guarantee precision at present, and its usable range has certain restriction; The data that the present invention will real-time monitor and data of weather forecast combine as input, multiple Forecasting Methodology combines, getting the combined value that predicts the outcome of various Forecasting Methodologies for finally predicting the outcome, have advance and opening, is a very strong system of cover practicality.
Description of drawings
Fig. 1 is workflow diagram of the present invention.
Embodiment
Photovoltaic power generation quantity of the present invention/generated power forecasting device comprises: the monitoring system data read module is used for reading the data message of the photovoltaic plant that monitoring system real-time monitors; Database, be used for the data message that storage monitoring system data read module reads; Data processing module,, for the historical generated energy to database/generated output data and all kinds of weather data, utilize method of fuzzy cluster analysis to classify and the classification results evaluation, obtains similar day sample set; The weather forecast information processing module, be used for the data of weather forecast that meteorological department provides is processed, and Weather information is described with numerical information; Prediction module, comprise BP predictor module, gray prediction submodule, SVM prediction submodule; Above-mentioned submodule is predicted respectively photovoltaic power generation quantity/generated output, obtains photovoltaic power generation quantity/generated power forecasting result, and the photovoltaic power generation quantity that then each predictor module is obtained/generated power forecasting result combination, obtain final combined prediction value.
BP predictor module in apparatus of the present invention adopts the BP neural network, generated energy/the generated output that to transfer from data processing module, weather data information,, as input quantity, obtain generated energy/generated power forecasting result with the weather forecast data of having described with numerical information of transferring from the weather forecast processing module.Weather data information wherein is including, but not limited to weather pattern, intensity of illumination, temperature, humidity information.
In the prediction module of apparatus of the present invention, predicting the outcome of each predictor module made up, and is to make up according to least variance method, and namely adopting minimum variance is optimization aim, obtain the weight coefficient between various predicted values, then make up and obtain the combined prediction value; Its expression is:
In formula:
pFor the combined prediction value; M is the Forecasting Methodology number, and the present invention adopts three kinds of Forecasting Methodologies therefore the m value is 3;
p i Be
iThe predicted value of kind method;
w i It is the weighting coefficient of i kind Forecasting Methodology;
e i And Var(
e i ) be respectively
iPlant predicated error and the variance of Forecasting Methodology.
In conjunction with Fig. 1, the job step of apparatus of the present invention is described further.
[0018] (1) reads the various information in the photovoltaic plant monitoring system, stores in database; The monitoring information of monitoring system generally comprises: photovoltaic power station power generation amount, generated output, Weather information, each components and parts duty etc.; The Weather information that monitoring system is monitored has weather pattern, temperature, humidity, wind speed, wind direction, build-up radiation amount, radiation intensity;
(2) information in database is processed, utilized method of fuzzy cluster analysis to classify and the classification results evaluation, obtain similar day sample set; Concrete steps are:
21) choose temperature, humidity and the weather pattern proper vector as similar day, then temperature profile vector sum Humidity Features vector is carried out respectively normalized, generally temperature profile vector sum Humidity Features vector is normalized to data between [0,1] or [1,1]; Simultaneously the weather pattern proper vector is mapped as numerical value, for example fine day is mapped as 1.5, clear to cloudyly is mapped as 1.3, and cloudy turn to fine is mapped as 1.2, and the rainy day is mapped as 0.2 etc.;
22) the temperature profile vector sum Humidity Features vector that normalized is good, and the weather pattern proper vector historical data that is mapped as numerical value is carried out cluster analysis, namely utilize method of fuzzy cluster analysis to try to achieve Data classification result under different clusters number, the sample with identical weather characteristics poly-be a class;
23) generally be divided into the 2-7 class according to the actual conditions data, be the Validity Index Vxb that Xie-Beni Validity Index is tried to achieve all Data classifications according to the Cluster Assessment index, Xie-Beni refers to Xie X .L and the Beni G. A two people geometry according to data set, the Validity Index that proposed in 1991; Then with the Data classification of Validity Index Vxb minimum as cluster result, form similar day sample set; Validity Index V
xbCan weigh the interior compactness of class and the degree of separation between class, found an equilibrium point between compactness and class between degree of separation in class, its value is less, and the cluster result of acquisition is better; Xie-Beni Validity Index Vxb expression is:
In formula, N is number of samples,
x j For sample j, U is that the degree of membership matrix is the degree of membership of each sample to every class, and c is the classification number, and V is the cluster centre of every class, is used for weighing the compactness in class in following formula, be worth littlely, and the interior data similarity of class is more large compacter; Be used for weighing the separation degree between class and class, the dissimilarity between larger class and class is larger, and between class, degree of separation is better;
(3) respectively temperature, humidity are carried out normalized by the weather forecast information processing module, weather pattern is mapped as numerical value; Optimal classification result resulting according to step (2), the belonging kinds that utilizes fuzzy clustering algorithm to obtain predicting day, similar day sample set of other compositions of sample of the class at its place; Sample set generally comprises the information such as the every daily generation of photovoltaic plant, generated output, temperature, humidity;
(4) will predict day information and similar day sample set be input to prediction module, obtain predicting the outcome of each predictor module, make up after the predicting the outcome of each predictor module, array mode is for to make up according to least variance method, namely adopting minimum variance is optimization aim, obtains the weight coefficient between various predicted values and make up obtaining the combined prediction value.
Claims (5)
1. photovoltaic power generation quantity/generated power forecasting device is characterized in that this device comprises: the monitoring system data read module is used for reading the data message of the photovoltaic plant that monitoring system real-time monitors; Database, be used for the data message that storage monitoring system data read module reads; Data processing module,, for the historical generated energy to database/generated output data and weather data, utilize method of fuzzy cluster analysis to classify and the classification results evaluation, obtains similar day sample set; The weather forecast information processing module, be used for the data of weather forecast that meteorological department provides is processed, and Weather information is described with numerical information;
Prediction module, comprise BP predictor module, gray prediction submodule, SVM prediction submodule, above-mentioned submodule is predicted respectively photovoltaic power generation quantity/generated output, obtain photovoltaic power generation quantity/generated power forecasting result, then the photovoltaic power generation quantity of each prediction module/generated power forecasting result is made up, obtain the combined prediction value.
2. photovoltaic power generation quantity according to claim 1/generated power forecasting device, it is characterized in that: described BP predictor module adopts the BP neural network, generated energy/the generated output that to transfer from data processing module, weather data information,, as input quantity, obtain generated energy/generated power forecasting result with the weather forecast data of having described with numerical information of transferring from the weather forecast processing module.
3. photovoltaic power generation quantity according to claim 1/generated power forecasting device, it is characterized in that: described SVM prediction submodule adopts support vector machine, to transfer generated energy/generated output, weather data information from data processing module,, as input quantity, obtain generated energy/generated power forecasting result with the weather forecast data of having described with numerical information of transferring from the weather forecast processing module.
4. photovoltaic power generation quantity according to claim 1/generated power forecasting device, it is characterized in that: described gray prediction submodule adopts gray theory, to transfer generated energy/generated output as input quantity from data processing module, obtain generated energy/generated power forecasting result.
5. according to claim 1,2,3 or 4 described photovoltaic power generation quantity/generated power forecasting devices, it is characterized in that: in described prediction module, each submodule carries out the photovoltaic generation power prediction based on similar day sample set; Described data processing module is classified and the classification results evaluation, and the concrete steps that obtain similar day sample set are:
1) choose temperature, humidity and weather pattern as similar day proper vector, then temperature profile vector sum Humidity Features vector is carried out respectively normalized, simultaneously the weather pattern proper vector is mapped as numerical value;
2) the temperature profile vector sum Humidity Features vector that normalized is good, and the weather pattern proper vector historical data that is mapped as numerical value is carried out cluster analysis, namely utilize method of fuzzy cluster analysis to try to achieve Data classification result under different clusters number, the sample with identical weather characteristics poly-be a class;
3) be the Validity Index Vxb that the Xie-Beni Validity Index is tried to achieve all Data classifications according to the Cluster Assessment index, then with the Data classification of Validity Index Vxb minimum as cluster result, form similar day sample set.
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|---|---|---|---|---|
| CN103810536A (en) * | 2014-01-10 | 2014-05-21 | 贵州电网公司 | Wind-solar power joint prediction system |
| CN103886616A (en) * | 2014-02-25 | 2014-06-25 | 国家电网公司 | Method for short-term prediction of photovoltaic power generation power based on whole-sky imaging data |
| CN103984991A (en) * | 2014-05-12 | 2014-08-13 | 武汉东日科技有限公司 | Distributed type solar radiation prediction method and system based on micro-meteorology data |
| CN103984988A (en) * | 2014-05-06 | 2014-08-13 | 国家电网公司 | Method for correcting super-short-term prediction of photovoltaic power of ARMA module in real time through light metering network |
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| CN114298370A (en) * | 2021-11-23 | 2022-04-08 | 中国华能集团清洁能源技术研究院有限公司 | Photovoltaic power generation capacity prediction method and device, electronic equipment and storage medium |
| CN116109007A (en) * | 2023-03-23 | 2023-05-12 | 阿里巴巴达摩院(杭州)科技有限公司 | Power generation power determination method, server and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101769788A (en) * | 2009-12-29 | 2010-07-07 | 青海国泰节能技术研究院 | Method for forecasting optical output power and electric energy production of photovoltaic power station |
| CN102663513A (en) * | 2012-03-13 | 2012-09-12 | 华北电力大学 | Combination forecast modeling method of wind farm power by using gray correlation analysis |
| US20120230821A1 (en) * | 2011-03-10 | 2012-09-13 | Sinovel Wind Group Co., Ltd. | Wind power prediction method of single wind turbine generator |
-
2013
- 2013-07-18 CN CN2013103011499A patent/CN103390199A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101769788A (en) * | 2009-12-29 | 2010-07-07 | 青海国泰节能技术研究院 | Method for forecasting optical output power and electric energy production of photovoltaic power station |
| US20120230821A1 (en) * | 2011-03-10 | 2012-09-13 | Sinovel Wind Group Co., Ltd. | Wind power prediction method of single wind turbine generator |
| CN102663513A (en) * | 2012-03-13 | 2012-09-12 | 华北电力大学 | Combination forecast modeling method of wind farm power by using gray correlation analysis |
Non-Patent Citations (1)
| Title |
|---|
| 孟洋洋 等: "基于相似日和人工神经网络的风电功率短期预测", 《电网技术》, vol. 34, no. 12, 5 December 2010 (2010-12-05), pages 163 - 167 * |
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|---|---|---|---|---|
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| CN104182564B (en) * | 2014-06-26 | 2017-09-12 | 内蒙古电力勘测设计院有限责任公司 | Photo-voltaic power generation station design specialist's system |
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| WO2020228568A1 (en) * | 2019-05-14 | 2020-11-19 | 京东方科技集团股份有限公司 | Method for training power generation amount prediction model of photovoltaic power station, power generation amount prediction method and device of photovoltaic power station, training system, prediction system and storage medium |
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