{"title":"基于日照时数指数的人工神经网络预测太阳能光伏发电能力","authors":"D. V. S. K. Rao, B. Prusty, Hareesh Myneni","doi":"10.1109/ICICCSP53532.2022.9862452","DOIUrl":null,"url":null,"abstract":"The grid integration of solar photovoltaic (PV) systems has recently grabbed considerable research attention. Simultaneously, the grid has been subjected to disturbances due to PV generations' variability, uncertainty, and intermittency; therefore, accurately estimating the weather-dependent PV power is imperative. The daily global solar radiation, temperature, and sunshine duration of a location can reflect its weather condition; hence, they are used to estimate PV power output using artificial neural network (ANN). A sunshine duration index, “k,” has been introduced to classify a location's weather condition. Accordingly, two weather conditions are considered based on “k,” and solar PV power estimation models are developed for both cases (Condition-I: 0<k<0.8 and condition-II: 0.8k1). The performance of the proposed ANN-based models is evaluated using error metrics, namely, mean absolute percentage error (MAPE) and relative root mean square error (RRMSE). The temperature of the considered location has resulted in a minimum error in estimating PV power output. The ANN model for 0.8k1 has resulted in a MAPE of 1.888 % with temperature as input. The ANN model for 0<k<0.8 has resulted in a MAPE of 10.599 % with temperature as input. Excellent performance is noticed with the developed forecasting models in estimating PV power. These models are helpful for feasibility studies of PV power plant installations and economic scheduling.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Bright Sunshine Duration Index-Based Prediction of Solar PV Power Using ANN Approach\",\"authors\":\"D. V. S. K. Rao, B. Prusty, Hareesh Myneni\",\"doi\":\"10.1109/ICICCSP53532.2022.9862452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The grid integration of solar photovoltaic (PV) systems has recently grabbed considerable research attention. Simultaneously, the grid has been subjected to disturbances due to PV generations' variability, uncertainty, and intermittency; therefore, accurately estimating the weather-dependent PV power is imperative. The daily global solar radiation, temperature, and sunshine duration of a location can reflect its weather condition; hence, they are used to estimate PV power output using artificial neural network (ANN). A sunshine duration index, “k,” has been introduced to classify a location's weather condition. Accordingly, two weather conditions are considered based on “k,” and solar PV power estimation models are developed for both cases (Condition-I: 0<k<0.8 and condition-II: 0.8k1). The performance of the proposed ANN-based models is evaluated using error metrics, namely, mean absolute percentage error (MAPE) and relative root mean square error (RRMSE). The temperature of the considered location has resulted in a minimum error in estimating PV power output. The ANN model for 0.8k1 has resulted in a MAPE of 1.888 % with temperature as input. The ANN model for 0<k<0.8 has resulted in a MAPE of 10.599 % with temperature as input. Excellent performance is noticed with the developed forecasting models in estimating PV power. These models are helpful for feasibility studies of PV power plant installations and economic scheduling.\",\"PeriodicalId\":326163,\"journal\":{\"name\":\"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICCSP53532.2022.9862452\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICCSP53532.2022.9862452","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bright Sunshine Duration Index-Based Prediction of Solar PV Power Using ANN Approach
The grid integration of solar photovoltaic (PV) systems has recently grabbed considerable research attention. Simultaneously, the grid has been subjected to disturbances due to PV generations' variability, uncertainty, and intermittency; therefore, accurately estimating the weather-dependent PV power is imperative. The daily global solar radiation, temperature, and sunshine duration of a location can reflect its weather condition; hence, they are used to estimate PV power output using artificial neural network (ANN). A sunshine duration index, “k,” has been introduced to classify a location's weather condition. Accordingly, two weather conditions are considered based on “k,” and solar PV power estimation models are developed for both cases (Condition-I: 0
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
