Bright Sunshine Duration Index-Based Prediction of Solar PV Power Using ANN Approach

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI:10.1109/ICICCSP53532.2022.9862452

D. V. S. K. Rao, B. Prusty, Hareesh Myneni

{"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":null,"pages":null},"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}

引用次数: 1

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

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

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

近年来，太阳能光伏系统的并网问题引起了人们的广泛关注。同时，由于光伏发电的可变性、不确定性和间歇性，电网受到干扰;因此，准确估计与天气相关的光伏发电是必要的。一个地点的日全球太阳辐射、温度和日照时数可以反映该地点的天气状况;因此，使用人工神经网络(ANN)来估计光伏输出功率。日照时间指数“k”被引入，用于对一个地区的天气状况进行分类。因此，基于“k”考虑了两种天气条件，并针对这两种情况(条件i: 0

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)

自引率

0.00%

发文量