{"title":"利用机器学习技术在2050年底前规划一个完全可再生能源系统:来自耶路撒冷地区电力公司的经验证据","authors":"Ammar Almasri, Diaa Salman","doi":"10.1109/ACCC54619.2021.00013","DOIUrl":null,"url":null,"abstract":"Machine Learning (ML) approach facilitates solving many current issues, such as marketing, telecommunication, health care, sale, and energy issues. ML solves the issue of burning fossil fuels that caused environmental pollution by replacing conventional resources with renewable resources. This study aims to advance the knowledge of transitioning the Palestinian energy system to 100% renewable energy towards 2050 and considering the mostly expected load demand in the period of 2020–2050. The load performance and the share of renewable energy sources for the existed pattern of a given data for the status in Palestine are forecasted by using the Artificial Neural Network method (RNN). Then by using the linear programming method, the needed share of renewables into the grid is designed. The results of analyses show that 100% renewable energy is thinkable to be achieved.","PeriodicalId":215546,"journal":{"name":"2021 2nd Asia Conference on Computers and Communications (ACCC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Using Machine Learning Techniques To Plan A Fully Renewable Energy Systems By The End of 2050: Empirical Evidence From Jerusalem District Electricity Company\",\"authors\":\"Ammar Almasri, Diaa Salman\",\"doi\":\"10.1109/ACCC54619.2021.00013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine Learning (ML) approach facilitates solving many current issues, such as marketing, telecommunication, health care, sale, and energy issues. ML solves the issue of burning fossil fuels that caused environmental pollution by replacing conventional resources with renewable resources. This study aims to advance the knowledge of transitioning the Palestinian energy system to 100% renewable energy towards 2050 and considering the mostly expected load demand in the period of 2020–2050. The load performance and the share of renewable energy sources for the existed pattern of a given data for the status in Palestine are forecasted by using the Artificial Neural Network method (RNN). Then by using the linear programming method, the needed share of renewables into the grid is designed. The results of analyses show that 100% renewable energy is thinkable to be achieved.\",\"PeriodicalId\":215546,\"journal\":{\"name\":\"2021 2nd Asia Conference on Computers and Communications (ACCC)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd Asia Conference on Computers and Communications (ACCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACCC54619.2021.00013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd Asia Conference on Computers and Communications (ACCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACCC54619.2021.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Machine Learning Techniques To Plan A Fully Renewable Energy Systems By The End of 2050: Empirical Evidence From Jerusalem District Electricity Company
Machine Learning (ML) approach facilitates solving many current issues, such as marketing, telecommunication, health care, sale, and energy issues. ML solves the issue of burning fossil fuels that caused environmental pollution by replacing conventional resources with renewable resources. This study aims to advance the knowledge of transitioning the Palestinian energy system to 100% renewable energy towards 2050 and considering the mostly expected load demand in the period of 2020–2050. The load performance and the share of renewable energy sources for the existed pattern of a given data for the status in Palestine are forecasted by using the Artificial Neural Network method (RNN). Then by using the linear programming method, the needed share of renewables into the grid is designed. The results of analyses show that 100% renewable energy is thinkable to be achieved.
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