{"title":"使用支持向量机 (SVM) 方法预测家庭令牌电量的电力负荷 (W)","authors":"Andika Dwi cahyo, Sri Anardani, Yoga Prisma Yuda","doi":"10.47233/jsit.v4i1.1543","DOIUrl":null,"url":null,"abstract":"Electricity is an essential need that cannot be separated from daily human life, especially in the modern era where many devices require electrical energy. The demand for electrical energy is increasing over time, thus the electricity providers must have sufficient capacity to meet the demand. However, there is still a lot of wasted energy due to the mismatch between the demand and the supplied amount of electricity, leading to significant electricity wastage. In this regard, appropriate calculations are needed to determine the amount of electrical power required, hence the need for a prediction method to determine the required power in real-time. The Support Vector Machine method is expected to predict the electrical load accurately, enabling users to determine the required load for the next period. It is hoped that this prediction will achieve an accuracy rate above 85%.","PeriodicalId":477238,"journal":{"name":"Jurnal Sains dan Teknologi (JSIT)","volume":"26 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediksi Beban Daya Listrik (W) Menggunakan Metode Support Vector Machine (SVM) Pada Listrik Token Rumah Tangga\",\"authors\":\"Andika Dwi cahyo, Sri Anardani, Yoga Prisma Yuda\",\"doi\":\"10.47233/jsit.v4i1.1543\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electricity is an essential need that cannot be separated from daily human life, especially in the modern era where many devices require electrical energy. The demand for electrical energy is increasing over time, thus the electricity providers must have sufficient capacity to meet the demand. However, there is still a lot of wasted energy due to the mismatch between the demand and the supplied amount of electricity, leading to significant electricity wastage. In this regard, appropriate calculations are needed to determine the amount of electrical power required, hence the need for a prediction method to determine the required power in real-time. The Support Vector Machine method is expected to predict the electrical load accurately, enabling users to determine the required load for the next period. It is hoped that this prediction will achieve an accuracy rate above 85%.\",\"PeriodicalId\":477238,\"journal\":{\"name\":\"Jurnal Sains dan Teknologi (JSIT)\",\"volume\":\"26 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jurnal Sains dan Teknologi (JSIT)\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.47233/jsit.v4i1.1543\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Sains dan Teknologi (JSIT)","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.47233/jsit.v4i1.1543","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediksi Beban Daya Listrik (W) Menggunakan Metode Support Vector Machine (SVM) Pada Listrik Token Rumah Tangga
Electricity is an essential need that cannot be separated from daily human life, especially in the modern era where many devices require electrical energy. The demand for electrical energy is increasing over time, thus the electricity providers must have sufficient capacity to meet the demand. However, there is still a lot of wasted energy due to the mismatch between the demand and the supplied amount of electricity, leading to significant electricity wastage. In this regard, appropriate calculations are needed to determine the amount of electrical power required, hence the need for a prediction method to determine the required power in real-time. The Support Vector Machine method is expected to predict the electrical load accurately, enabling users to determine the required load for the next period. It is hoped that this prediction will achieve an accuracy rate above 85%.