{"title":"印度某太阳能发电厂短期全球水平辐照度预测模型的混合深度学习框架","authors":"S.V.S. Rajaprasad, Rambabu Mukkamala","doi":"10.33223/epj/168115","DOIUrl":null,"url":null,"abstract":": The rapid development of grid integration of solar energy in developing countries like India has created vital concerns such as fluctuations and interruptions affecting grid operations. Improving the consistency and accuracy of solar energy forecasts can increase the reliability of the power grid. Although solar energy is available in abundance around the world, it is viewed as an unpredictable source due to uncertain fluctuations in climate conditions. Global horizontal irradiance (GHI) pre - diction is critical to efficiently manage and forecast the power output of solar power plants. Howe - ver, developing an accurate GHI forecasting model is challenging due to the variability of weather conditions over time. This research aims to develop and compare univariate LSTM models capable of predicting GHI in a solar power plant in India over the short term. The present study introduces a deep neural network-based (DNN) hybrid model with a combination of convolutional neural","PeriodicalId":52299,"journal":{"name":"Polityka Energetyczna","volume":"168 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A hybrid deep learning framework for modeling the short term global horizontal irradiance prediction of a solar power plant in India\",\"authors\":\"S.V.S. Rajaprasad, Rambabu Mukkamala\",\"doi\":\"10.33223/epj/168115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": The rapid development of grid integration of solar energy in developing countries like India has created vital concerns such as fluctuations and interruptions affecting grid operations. Improving the consistency and accuracy of solar energy forecasts can increase the reliability of the power grid. Although solar energy is available in abundance around the world, it is viewed as an unpredictable source due to uncertain fluctuations in climate conditions. Global horizontal irradiance (GHI) pre - diction is critical to efficiently manage and forecast the power output of solar power plants. Howe - ver, developing an accurate GHI forecasting model is challenging due to the variability of weather conditions over time. This research aims to develop and compare univariate LSTM models capable of predicting GHI in a solar power plant in India over the short term. The present study introduces a deep neural network-based (DNN) hybrid model with a combination of convolutional neural\",\"PeriodicalId\":52299,\"journal\":{\"name\":\"Polityka Energetyczna\",\"volume\":\"168 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Polityka Energetyczna\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33223/epj/168115\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Energy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Polityka Energetyczna","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33223/epj/168115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Energy","Score":null,"Total":0}
A hybrid deep learning framework for modeling the short term global horizontal irradiance prediction of a solar power plant in India
: The rapid development of grid integration of solar energy in developing countries like India has created vital concerns such as fluctuations and interruptions affecting grid operations. Improving the consistency and accuracy of solar energy forecasts can increase the reliability of the power grid. Although solar energy is available in abundance around the world, it is viewed as an unpredictable source due to uncertain fluctuations in climate conditions. Global horizontal irradiance (GHI) pre - diction is critical to efficiently manage and forecast the power output of solar power plants. Howe - ver, developing an accurate GHI forecasting model is challenging due to the variability of weather conditions over time. This research aims to develop and compare univariate LSTM models capable of predicting GHI in a solar power plant in India over the short term. The present study introduces a deep neural network-based (DNN) hybrid model with a combination of convolutional neural