O. Savchenko, O. Miroshnyk, O. Moroz, I. Trunova, Anatolii Sereda, Sergii Dudnikov, O. Kozlovskyi, R. Buinyi, S. Halko
{"title":"基于人工神经网络预测太阳辐射强度提高太阳能发电厂效率","authors":"O. Savchenko, O. Miroshnyk, O. Moroz, I. Trunova, Anatolii Sereda, Sergii Dudnikov, O. Kozlovskyi, R. Buinyi, S. Halko","doi":"10.1109/KhPIWeek53812.2021.9570009","DOIUrl":null,"url":null,"abstract":"The method of forecasting of the generation of solar power plants on the basis of the theory of artificial neural networks is proposed. Creating a predictive model has been carried out in the STATISTICA software package. To predict the energy of solar radiation for 24 hours in advance, the so-called “time window” method was used. To predict the intensity of solar radiation, measurements of hydro meteorological station were used. During simulation, the width of the input “time window” was set to 24 hours, the width of the output “time window” was set to 1 hours. Thus, the “time window” was shifted 24 times. The practical results of the article are offered to the use of enterprises operating solar power plants.","PeriodicalId":365896,"journal":{"name":"2021 IEEE 2nd KhPI Week on Advanced Technology (KhPIWeek)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Improving the Efficiency of Solar Power Plants Based on Forecasting the Intensity of Solar Radiation Using Artificial Neural Networks\",\"authors\":\"O. Savchenko, O. Miroshnyk, O. Moroz, I. Trunova, Anatolii Sereda, Sergii Dudnikov, O. Kozlovskyi, R. Buinyi, S. Halko\",\"doi\":\"10.1109/KhPIWeek53812.2021.9570009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The method of forecasting of the generation of solar power plants on the basis of the theory of artificial neural networks is proposed. Creating a predictive model has been carried out in the STATISTICA software package. To predict the energy of solar radiation for 24 hours in advance, the so-called “time window” method was used. To predict the intensity of solar radiation, measurements of hydro meteorological station were used. During simulation, the width of the input “time window” was set to 24 hours, the width of the output “time window” was set to 1 hours. Thus, the “time window” was shifted 24 times. The practical results of the article are offered to the use of enterprises operating solar power plants.\",\"PeriodicalId\":365896,\"journal\":{\"name\":\"2021 IEEE 2nd KhPI Week on Advanced Technology (KhPIWeek)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 2nd KhPI Week on Advanced Technology (KhPIWeek)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KhPIWeek53812.2021.9570009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 2nd KhPI Week on Advanced Technology (KhPIWeek)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KhPIWeek53812.2021.9570009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving the Efficiency of Solar Power Plants Based on Forecasting the Intensity of Solar Radiation Using Artificial Neural Networks
The method of forecasting of the generation of solar power plants on the basis of the theory of artificial neural networks is proposed. Creating a predictive model has been carried out in the STATISTICA software package. To predict the energy of solar radiation for 24 hours in advance, the so-called “time window” method was used. To predict the intensity of solar radiation, measurements of hydro meteorological station were used. During simulation, the width of the input “time window” was set to 24 hours, the width of the output “time window” was set to 1 hours. Thus, the “time window” was shifted 24 times. The practical results of the article are offered to the use of enterprises operating solar power plants.