Method for predicting photovoltaic power generation power through particle swarm optimization gray model
The invention discloses a method for predicting photovoltaic power generation power through a particle swarm optimization grey model, and relates to the technical field of electric power systems, the influence of radiation intensity, air temperature and battery temperature is considered, a GM (1, N)...
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| creator | ZHANG CHEN YANG HUIHUA ZHANG YUE LUO XIAOMEI XIE YAO SU ZIFENG XIA YONGQIN LI FANGYU TAO WEIWEI HE YIXIANG YAN LIUJIN LI ZHENGBO HUANG WEIDONG XIAO YINGTAO CUI RONGMEI TANG YONGZHEN WU GANG PANG LUOBING JANG YEONG SHIN JIANG FENG JIN CUNFENG LIN JIACHENG ZHU JINRUN LI JUAN CHENG PENG FAN RONGQIN WANG ZIYU |
| description | The invention discloses a method for predicting photovoltaic power generation power through a particle swarm optimization grey model, and relates to the technical field of electric power systems, the influence of radiation intensity, air temperature and battery temperature is considered, a GM (1, N) model is established, and the optimal weight of the GM (1, N) model is searched through the particle swarm optimization. The influence of meteorological factors on the photovoltaic power is considered, the prediction precision is improved by adopting a combined prediction method, meanwhile, the requirement for the data sample size is not large, the condition that the photovoltaic power has the seasonal characteristic is considered, original data of one quarter can meet the requirements of the method, and the prediction precision is high.
本发明公开一种粒子群优化的灰色模型预测光伏发电功率的方法，涉及电力系统技术领域，考虑了辐射强度、气温、电池温度的影响，建立了GM(1，N)模型，并用粒子群算法寻找GM(1，N)模型的最优权值，该方法相较于传统的GM(1,1)模型，考虑了气象因素对于光伏功率的影响，采用组合预测的方法提高了预测精度，同时对数据样本量的要求不大，考虑光伏功率具有季节性特点，一个 |
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| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN114899814A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN114899814A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN114899814A3</originalsourceid><addsrcrecordid>eNqNjDEKwkAQANNYiPqH9QEWwRRJKSFio5V9OC6bu4VLdtmsBn29gnmA1cAwzDqLV7TIHfSsIIodeaMxgEQ2fnIyRx6EZ1QIOKI6Ix4XYVH5ESKIUyOfEKbZ6QAsRgO9f2VQ94KBO0zbbNW7NOFu4Sbbn5t7fTmgcIuTOP_9W1vf8rwoq6rMi9Pxn-YDvbNBxg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Method for predicting photovoltaic power generation power through particle swarm optimization gray model</title><source>esp@cenet</source><creator>ZHANG CHEN ; YANG HUIHUA ; ZHANG YUE ; LUO XIAOMEI ; XIE YAO ; SU ZIFENG ; XIA YONGQIN ; LI FANGYU ; TAO WEIWEI ; HE YIXIANG ; YAN LIUJIN ; LI ZHENGBO ; HUANG WEIDONG ; XIAO YINGTAO ; CUI RONGMEI ; TANG YONGZHEN ; WU GANG ; PANG LUOBING ; JANG YEONG SHIN ; JIANG FENG ; JIN CUNFENG ; LIN JIACHENG ; ZHU JINRUN ; LI JUAN ; CHENG PENG ; FAN RONGQIN ; WANG ZIYU</creator><creatorcontrib>ZHANG CHEN ; YANG HUIHUA ; ZHANG YUE ; LUO XIAOMEI ; XIE YAO ; SU ZIFENG ; XIA YONGQIN ; LI FANGYU ; TAO WEIWEI ; HE YIXIANG ; YAN LIUJIN ; LI ZHENGBO ; HUANG WEIDONG ; XIAO YINGTAO ; CUI RONGMEI ; TANG YONGZHEN ; WU GANG ; PANG LUOBING ; JANG YEONG SHIN ; JIANG FENG ; JIN CUNFENG ; LIN JIACHENG ; ZHU JINRUN ; LI JUAN ; CHENG PENG ; FAN RONGQIN ; WANG ZIYU</creatorcontrib><description>The invention discloses a method for predicting photovoltaic power generation power through a particle swarm optimization grey model, and relates to the technical field of electric power systems, the influence of radiation intensity, air temperature and battery temperature is considered, a GM (1, N) model is established, and the optimal weight of the GM (1, N) model is searched through the particle swarm optimization. The influence of meteorological factors on the photovoltaic power is considered, the prediction precision is improved by adopting a combined prediction method, meanwhile, the requirement for the data sample size is not large, the condition that the photovoltaic power has the seasonal characteristic is considered, original data of one quarter can meet the requirements of the method, and the prediction precision is high.
本发明公开一种粒子群优化的灰色模型预测光伏发电功率的方法，涉及电力系统技术领域，考虑了辐射强度、气温、电池温度的影响，建立了GM(1，N)模型，并用粒子群算法寻找GM(1，N)模型的最优权值，该方法相较于传统的GM(1,1)模型，考虑了气象因素对于光伏功率的影响，采用组合预测的方法提高了预测精度，同时对数据样本量的要求不大，考虑光伏功率具有季节性特点，一个</description><language>chi ; eng</language><subject>CALCULATING ; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRICITY ; GENERATION ; PHYSICS ; SYSTEMS FOR STORING ELECTRIC ENERGY ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220812&DB=EPODOC&CC=CN&NR=114899814A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220812&DB=EPODOC&CC=CN&NR=114899814A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHANG CHEN</creatorcontrib><creatorcontrib>YANG HUIHUA</creatorcontrib><creatorcontrib>ZHANG YUE</creatorcontrib><creatorcontrib>LUO XIAOMEI</creatorcontrib><creatorcontrib>XIE YAO</creatorcontrib><creatorcontrib>SU ZIFENG</creatorcontrib><creatorcontrib>XIA YONGQIN</creatorcontrib><creatorcontrib>LI FANGYU</creatorcontrib><creatorcontrib>TAO WEIWEI</creatorcontrib><creatorcontrib>HE YIXIANG</creatorcontrib><creatorcontrib>YAN LIUJIN</creatorcontrib><creatorcontrib>LI ZHENGBO</creatorcontrib><creatorcontrib>HUANG WEIDONG</creatorcontrib><creatorcontrib>XIAO YINGTAO</creatorcontrib><creatorcontrib>CUI RONGMEI</creatorcontrib><creatorcontrib>TANG YONGZHEN</creatorcontrib><creatorcontrib>WU GANG</creatorcontrib><creatorcontrib>PANG LUOBING</creatorcontrib><creatorcontrib>JANG YEONG SHIN</creatorcontrib><creatorcontrib>JIANG FENG</creatorcontrib><creatorcontrib>JIN CUNFENG</creatorcontrib><creatorcontrib>LIN JIACHENG</creatorcontrib><creatorcontrib>ZHU JINRUN</creatorcontrib><creatorcontrib>LI JUAN</creatorcontrib><creatorcontrib>CHENG PENG</creatorcontrib><creatorcontrib>FAN RONGQIN</creatorcontrib><creatorcontrib>WANG ZIYU</creatorcontrib><title>Method for predicting photovoltaic power generation power through particle swarm optimization gray model</title><description>The invention discloses a method for predicting photovoltaic power generation power through a particle swarm optimization grey model, and relates to the technical field of electric power systems, the influence of radiation intensity, air temperature and battery temperature is considered, a GM (1, N) model is established, and the optimal weight of the GM (1, N) model is searched through the particle swarm optimization. The influence of meteorological factors on the photovoltaic power is considered, the prediction precision is improved by adopting a combined prediction method, meanwhile, the requirement for the data sample size is not large, the condition that the photovoltaic power has the seasonal characteristic is considered, original data of one quarter can meet the requirements of the method, and the prediction precision is high.
本发明公开一种粒子群优化的灰色模型预测光伏发电功率的方法，涉及电力系统技术领域，考虑了辐射强度、气温、电池温度的影响，建立了GM(1，N)模型，并用粒子群算法寻找GM(1，N)模型的最优权值，该方法相较于传统的GM(1,1)模型，考虑了气象因素对于光伏功率的影响，采用组合预测的方法提高了预测精度，同时对数据样本量的要求不大，考虑光伏功率具有季节性特点，一个</description><subject>CALCULATING</subject><subject>CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>CONVERSION OR DISTRIBUTION OF ELECTRIC POWER</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRICITY</subject><subject>GENERATION</subject><subject>PHYSICS</subject><subject>SYSTEMS FOR STORING ELECTRIC ENERGY</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjDEKwkAQANNYiPqH9QEWwRRJKSFio5V9OC6bu4VLdtmsBn29gnmA1cAwzDqLV7TIHfSsIIodeaMxgEQ2fnIyRx6EZ1QIOKI6Ix4XYVH5ESKIUyOfEKbZ6QAsRgO9f2VQ94KBO0zbbNW7NOFu4Sbbn5t7fTmgcIuTOP_9W1vf8rwoq6rMi9Pxn-YDvbNBxg</recordid><startdate>20220812</startdate><enddate>20220812</enddate><creator>ZHANG CHEN</creator><creator>YANG HUIHUA</creator><creator>ZHANG YUE</creator><creator>LUO XIAOMEI</creator><creator>XIE YAO</creator><creator>SU ZIFENG</creator><creator>XIA YONGQIN</creator><creator>LI FANGYU</creator><creator>TAO WEIWEI</creator><creator>HE YIXIANG</creator><creator>YAN LIUJIN</creator><creator>LI ZHENGBO</creator><creator>HUANG WEIDONG</creator><creator>XIAO YINGTAO</creator><creator>CUI RONGMEI</creator><creator>TANG YONGZHEN</creator><creator>WU GANG</creator><creator>PANG LUOBING</creator><creator>JANG YEONG SHIN</creator><creator>JIANG FENG</creator><creator>JIN CUNFENG</creator><creator>LIN JIACHENG</creator><creator>ZHU JINRUN</creator><creator>LI JUAN</creator><creator>CHENG PENG</creator><creator>FAN RONGQIN</creator><creator>WANG ZIYU</creator><scope>EVB</scope></search><sort><creationdate>20220812</creationdate><title>Method for predicting photovoltaic power generation power through particle swarm optimization gray model</title><author>ZHANG CHEN ; 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The influence of meteorological factors on the photovoltaic power is considered, the prediction precision is improved by adopting a combined prediction method, meanwhile, the requirement for the data sample size is not large, the condition that the photovoltaic power has the seasonal characteristic is considered, original data of one quarter can meet the requirements of the method, and the prediction precision is high.
本发明公开一种粒子群优化的灰色模型预测光伏发电功率的方法，涉及电力系统技术领域，考虑了辐射强度、气温、电池温度的影响，建立了GM(1，N)模型，并用粒子群算法寻找GM(1，N)模型的最优权值，该方法相较于传统的GM(1,1)模型，考虑了气象因素对于光伏功率的影响，采用组合预测的方法提高了预测精度，同时对数据样本量的要求不大，考虑光伏功率具有季节性特点，一个</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONVERSION OR DISTRIBUTION OF ELECTRIC POWER COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRICITY GENERATION PHYSICS SYSTEMS FOR STORING ELECTRIC ENERGY SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Method for predicting photovoltaic power generation power through particle swarm optimization gray model |
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