{"title":"基于物理模型和长短期记忆的光伏发电综合预测","authors":"Yaoyu Wu, Jing Liu, Suhuan Li, Mingyue Jin","doi":"10.1007/s43236-024-00782-9","DOIUrl":null,"url":null,"abstract":"<p>Solar energy is clean and pollution free. However, the evident intermittency and volatility of illumination make power systems uncertain. Therefore, establishing a photovoltaic prediction model to enhance prediction precision is conducive to lessening the uncertainty of photovoltaic (PV) power generation and to ensuring the safe and stable operation of power grid scheduling. The radiation from the sun to the Earth has a certain regularity, which can be estimated under ideal weather conditions. However, the radiation is affected by climate, cloud cover, and other reasons. Therefore, this paper puts forward a PV prediction model combining a physical model and a neural network that can modify solar radiation in complex weather through the neural network to enhance the accuracy of PV power prediction. First, a solar radiation model (SRM) is established by using the solar radiation mechanism to estimate the sum radiation value on the horizontal plane. Then the slope radiation value received by the PV panel is calculated by the slope irradiance conversion method. Second, the main factors that greatly influence PV power are screened out by the Pearson method. The calculated slope radiation and the main influencing factors are taken as inputs. The long short-term memory network (LSTM) is selected to set up the SRM-LSTM PV power prediction method. The significance of the suggested method is verified by the true data from Alice Springs, Australia. The results show that when compared with the backpropagation (BP) prediction method, the MAE and RMSE were reduced by 22.18% and 33.89% under complex weather conditions, respectively. When compared with the LSTM prediction method, the MAE and RMSE were reduced by 15.99% and 21.73%, respectively. These results demonstrate high accuracy.</p>","PeriodicalId":50081,"journal":{"name":"Journal of Power Electronics","volume":"36 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Physical model and long short-term memory-based combined prediction of photovoltaic power generation\",\"authors\":\"Yaoyu Wu, Jing Liu, Suhuan Li, Mingyue Jin\",\"doi\":\"10.1007/s43236-024-00782-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Solar energy is clean and pollution free. However, the evident intermittency and volatility of illumination make power systems uncertain. Therefore, establishing a photovoltaic prediction model to enhance prediction precision is conducive to lessening the uncertainty of photovoltaic (PV) power generation and to ensuring the safe and stable operation of power grid scheduling. The radiation from the sun to the Earth has a certain regularity, which can be estimated under ideal weather conditions. However, the radiation is affected by climate, cloud cover, and other reasons. Therefore, this paper puts forward a PV prediction model combining a physical model and a neural network that can modify solar radiation in complex weather through the neural network to enhance the accuracy of PV power prediction. First, a solar radiation model (SRM) is established by using the solar radiation mechanism to estimate the sum radiation value on the horizontal plane. Then the slope radiation value received by the PV panel is calculated by the slope irradiance conversion method. Second, the main factors that greatly influence PV power are screened out by the Pearson method. The calculated slope radiation and the main influencing factors are taken as inputs. The long short-term memory network (LSTM) is selected to set up the SRM-LSTM PV power prediction method. The significance of the suggested method is verified by the true data from Alice Springs, Australia. The results show that when compared with the backpropagation (BP) prediction method, the MAE and RMSE were reduced by 22.18% and 33.89% under complex weather conditions, respectively. When compared with the LSTM prediction method, the MAE and RMSE were reduced by 15.99% and 21.73%, respectively. These results demonstrate high accuracy.</p>\",\"PeriodicalId\":50081,\"journal\":{\"name\":\"Journal of Power Electronics\",\"volume\":\"36 1\",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Power Electronics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s43236-024-00782-9\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Power Electronics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s43236-024-00782-9","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Physical model and long short-term memory-based combined prediction of photovoltaic power generation
Solar energy is clean and pollution free. However, the evident intermittency and volatility of illumination make power systems uncertain. Therefore, establishing a photovoltaic prediction model to enhance prediction precision is conducive to lessening the uncertainty of photovoltaic (PV) power generation and to ensuring the safe and stable operation of power grid scheduling. The radiation from the sun to the Earth has a certain regularity, which can be estimated under ideal weather conditions. However, the radiation is affected by climate, cloud cover, and other reasons. Therefore, this paper puts forward a PV prediction model combining a physical model and a neural network that can modify solar radiation in complex weather through the neural network to enhance the accuracy of PV power prediction. First, a solar radiation model (SRM) is established by using the solar radiation mechanism to estimate the sum radiation value on the horizontal plane. Then the slope radiation value received by the PV panel is calculated by the slope irradiance conversion method. Second, the main factors that greatly influence PV power are screened out by the Pearson method. The calculated slope radiation and the main influencing factors are taken as inputs. The long short-term memory network (LSTM) is selected to set up the SRM-LSTM PV power prediction method. The significance of the suggested method is verified by the true data from Alice Springs, Australia. The results show that when compared with the backpropagation (BP) prediction method, the MAE and RMSE were reduced by 22.18% and 33.89% under complex weather conditions, respectively. When compared with the LSTM prediction method, the MAE and RMSE were reduced by 15.99% and 21.73%, respectively. These results demonstrate high accuracy.
期刊介绍:
The scope of Journal of Power Electronics includes all issues in the field of Power Electronics. Included are techniques for power converters, adjustable speed drives, renewable energy, power quality and utility applications, analysis, modeling and control, power devices and components, power electronics education, and other application.