{"title":"基于并行网元的短期集合日照预报方法","authors":"Shoji Kawasaki, Koshi Ishibe","doi":"10.1002/eej.23487","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>In this paper, the authors propose an ensemble forecasting method using multiple individuals for short-time-ahead (1 h ahead) insolation forecasting by using neuroevolution (NE), in which a genetic algorithm is applied to the learning algorithm of a neural network for insolation. Although the method improves the accuracy compared to a single forecast, NE has a problem that the training time is long. In order to solve this problem, the authors propose a parallelization method of GPU processing for short-time-ahead forecasting and try to solve the problem by parallelizing the GPU.</p>\n </div>","PeriodicalId":50550,"journal":{"name":"Electrical Engineering in Japan","volume":"218 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Short-Term Ensemble Insolation Forecasting Method Using Parallelized NE\",\"authors\":\"Shoji Kawasaki, Koshi Ishibe\",\"doi\":\"10.1002/eej.23487\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>In this paper, the authors propose an ensemble forecasting method using multiple individuals for short-time-ahead (1 h ahead) insolation forecasting by using neuroevolution (NE), in which a genetic algorithm is applied to the learning algorithm of a neural network for insolation. Although the method improves the accuracy compared to a single forecast, NE has a problem that the training time is long. In order to solve this problem, the authors propose a parallelization method of GPU processing for short-time-ahead forecasting and try to solve the problem by parallelizing the GPU.</p>\\n </div>\",\"PeriodicalId\":50550,\"journal\":{\"name\":\"Electrical Engineering in Japan\",\"volume\":\"218 1\",\"pages\":\"\"},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2025-01-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electrical Engineering in Japan\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/eej.23487\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electrical Engineering in Japan","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/eej.23487","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Short-Term Ensemble Insolation Forecasting Method Using Parallelized NE
In this paper, the authors propose an ensemble forecasting method using multiple individuals for short-time-ahead (1 h ahead) insolation forecasting by using neuroevolution (NE), in which a genetic algorithm is applied to the learning algorithm of a neural network for insolation. Although the method improves the accuracy compared to a single forecast, NE has a problem that the training time is long. In order to solve this problem, the authors propose a parallelization method of GPU processing for short-time-ahead forecasting and try to solve the problem by parallelizing the GPU.
期刊介绍:
Electrical Engineering in Japan (EEJ) is an official journal of the Institute of Electrical Engineers of Japan (IEEJ). This authoritative journal is a translation of the Transactions of the Institute of Electrical Engineers of Japan. It publishes 16 issues a year on original research findings in Electrical Engineering with special focus on the science, technology and applications of electric power, such as power generation, transmission and conversion, electric railways (including magnetic levitation devices), motors, switching, power economics.