{"title":"基于机器学习的全球水平辐照度预测应用","authors":"B. Manning","doi":"10.1109/SECON.2017.7925302","DOIUrl":null,"url":null,"abstract":"The adoption of residential solar energy solutions has become a popular alternative for many families as the need for alternative energy resources begins to increase and this is causing a fluctuation in the estimated electrical energy needs forecasted by energy companies. This is a problem because electric companies base their production amounts on regional estimated energy needs and might easily overproduce or underproduce energy unless they can better estimate the availability of solar energy in each region and create a solution for monitoring its residential adoption across the same region.","PeriodicalId":368197,"journal":{"name":"SoutheastCon 2017","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A machine learning based application for predicting Global Horizontal Irradiance\",\"authors\":\"B. Manning\",\"doi\":\"10.1109/SECON.2017.7925302\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The adoption of residential solar energy solutions has become a popular alternative for many families as the need for alternative energy resources begins to increase and this is causing a fluctuation in the estimated electrical energy needs forecasted by energy companies. This is a problem because electric companies base their production amounts on regional estimated energy needs and might easily overproduce or underproduce energy unless they can better estimate the availability of solar energy in each region and create a solution for monitoring its residential adoption across the same region.\",\"PeriodicalId\":368197,\"journal\":{\"name\":\"SoutheastCon 2017\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SoutheastCon 2017\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SECON.2017.7925302\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoutheastCon 2017","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SECON.2017.7925302","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A machine learning based application for predicting Global Horizontal Irradiance
The adoption of residential solar energy solutions has become a popular alternative for many families as the need for alternative energy resources begins to increase and this is causing a fluctuation in the estimated electrical energy needs forecasted by energy companies. This is a problem because electric companies base their production amounts on regional estimated energy needs and might easily overproduce or underproduce energy unless they can better estimate the availability of solar energy in each region and create a solution for monitoring its residential adoption across the same region.
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