{"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}
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.
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