{"title":"Day-Ahead Prediction of PV Power Output: A One-Year Case Study at Changwon in South Korea","authors":"Wanbin Son, Ye-Rim Lee","doi":"10.1007/s42835-024-01974-w","DOIUrl":null,"url":null,"abstract":"<p>This paper is a one-year case study of day-ahead prediction of PV output at Changwon in South Korea. We are focused on day-ahead hourly PV power forecasting and long-term experiments in this paper. We introduce three machine learning based forecasting methods that predict hourly PV power for the next day at midnight, and show performance of them for a 51 kW PV system located at Changwon for a year. Our methods learn relationship of historical meteorological factors, and then predict 24 h PV power considering the trained relationship and weather forecasts from weather forecasting organizations. We show monthly performance of all the proposed methods and a persistence model for a year. Since South Korea is located in a temperate zone with four distinct seasons, and has complex climate characteristics, it is difficult to show actual performance of PV forecasting methods by short-term experimental results. We believe that long term experimental results in this paper are valuable data for the next studies.</p>","PeriodicalId":15577,"journal":{"name":"Journal of Electrical Engineering & Technology","volume":"34 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrical Engineering & Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42835-024-01974-w","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}
引用次数: 0
Abstract
This paper is a one-year case study of day-ahead prediction of PV output at Changwon in South Korea. We are focused on day-ahead hourly PV power forecasting and long-term experiments in this paper. We introduce three machine learning based forecasting methods that predict hourly PV power for the next day at midnight, and show performance of them for a 51 kW PV system located at Changwon for a year. Our methods learn relationship of historical meteorological factors, and then predict 24 h PV power considering the trained relationship and weather forecasts from weather forecasting organizations. We show monthly performance of all the proposed methods and a persistence model for a year. Since South Korea is located in a temperate zone with four distinct seasons, and has complex climate characteristics, it is difficult to show actual performance of PV forecasting methods by short-term experimental results. We believe that long term experimental results in this paper are valuable data for the next studies.
期刊介绍:
ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies.
The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.