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  • 标题:PEMODELAN REGRESI NONPARAMETRIK MENGGUNAKAN PENDEKATAN POLINOMIAL LOKAL PADA BEBAN LISTRIK DI KOTA SEMARANG
  • 本地全文:下载
  • 作者:Suparti Suparti ; Alan Prahutama
  • 期刊名称:MEDIA STATISTIKA
  • 印刷版ISSN:1979-3693
  • 电子版ISSN:2477-0647
  • 出版年度:2017
  • 卷号:9
  • 期号:2
  • 页码:85-93
  • 出版社:MEDIA STATISTIKA
  • 摘要:Semarang is the provincial capital of Central Java, with infrastructure and economic’s growth was high. The phenomenon of power outages that occurred in Semarang, certainly disrupted economic development in Semarang. Large electrical energy consumed by industrial-scale consumers and households in the San Francisco area, monitored or recorded automatically and presented into a historical data load power consumption. Therefore, this study modeling the load power consumption at a time when not influenced by the use of electrical load (t-1)- th . Modeling using nonparametric regression approach with Local polynomial. In this study, the kernel used is a Gaussian kernel. In local polynomial modeling, determined optimum bandwidth. One of the optimum bandwidth determination using the Generalized Cross Validation (GCV). GCV values obtained amounted to 1425.726 with a minimum bandwidth of 394. Modelling generate local polynomial of order 2 with MSE value of 1408.672. Keywords : e lectrical l oad, l o c al p olinomial, g aussian k ernel, GCV.
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