基于地理信息系统的电力负荷预测

2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST) Pub Date : 2019-07-01 DOI:10.1109/ICEAST.2019.8802591

Sooppasek Katruksa, S. Jiriwibhakorn

{"title":"基于地理信息系统的电力负荷预测","authors":"Sooppasek Katruksa, S. Jiriwibhakorn","doi":"10.1109/ICEAST.2019.8802591","DOIUrl":null,"url":null,"abstract":"This paper continues to develop the previous paper on Application Data for Electricity Load Forecasting Models by applying the method to the geographic information system (GIS) technology in medium-term energy forecasting for the Metropolitan Electricity Authority (MEA) area of Bangkok, Thailand. This method can be employed to improve the electricity load efficiency of the MEA. The spatial prediction plays a key role in the expansion of the areas of electricity distribution, such as the decision-making regarding investment in new substations and power system planning for maintenance and operations. The results appear to indicate that the prediction of the point density of the MEA areas was proportional to the electricity demand in the MEA areas.","PeriodicalId":188498,"journal":{"name":"2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Electricity Load Forecasting Based on a Geographic Information System\",\"authors\":\"Sooppasek Katruksa, S. Jiriwibhakorn\",\"doi\":\"10.1109/ICEAST.2019.8802591\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper continues to develop the previous paper on Application Data for Electricity Load Forecasting Models by applying the method to the geographic information system (GIS) technology in medium-term energy forecasting for the Metropolitan Electricity Authority (MEA) area of Bangkok, Thailand. This method can be employed to improve the electricity load efficiency of the MEA. The spatial prediction plays a key role in the expansion of the areas of electricity distribution, such as the decision-making regarding investment in new substations and power system planning for maintenance and operations. The results appear to indicate that the prediction of the point density of the MEA areas was proportional to the electricity demand in the MEA areas.\",\"PeriodicalId\":188498,\"journal\":{\"name\":\"2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEAST.2019.8802591\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEAST.2019.8802591","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

本文在泰国曼谷大都会电力局(MEA)地区中期能源预测的地理信息系统(GIS)技术的基础上，继续发展了前一篇关于电力负荷预测模型应用数据的论文。该方法可以提高MEA的负载效率。空间预测在配电区域的扩展中起着关键作用，如新建变电站的投资决策和电力系统的维护和运行规划。结果表明，对多边环境协定地区点密度的预测与多边环境协定地区的电力需求成正比。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Electricity Load Forecasting Based on a Geographic Information System

This paper continues to develop the previous paper on Application Data for Electricity Load Forecasting Models by applying the method to the geographic information system (GIS) technology in medium-term energy forecasting for the Metropolitan Electricity Authority (MEA) area of Bangkok, Thailand. This method can be employed to improve the electricity load efficiency of the MEA. The spatial prediction plays a key role in the expansion of the areas of electricity distribution, such as the decision-making regarding investment in new substations and power system planning for maintenance and operations. The results appear to indicate that the prediction of the point density of the MEA areas was proportional to the electricity demand in the MEA areas.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)

自引率

0.00%

发文量