The impact of influence range fuzzy subtractive clustering modification to accuracy anomalous load forecasting

F.A. Respati; A.G. Abdullah; Y. Mulyadi

doi:10.1088/1757-899X/180/1/012292

← Back to search

The impact of influence range fuzzy subtractive clustering modification to accuracy anomalous load forecasting

Open

F.A. Respati, A.G. Abdullah, Y. Mulyadi

2017 IOP Conference Series: Materials Science and Engineering Vol. 180 Issue 1 Conference paper Cited by 0 SDG 7

Abstract

Short term load forecasting (STLF) has an important role for reliability and economic operation of electrical power system. In this paper, fuzzy subtractive clustering (FSC) method is used in STLF of electrical power system for special days in anomalous load conditions. These anomalous loads occur during national holidays. This method is applied on dataset of Region 2 Java-Bali to forecast the load demand on half-hour in national holidays (anomalous load). The proposed methodology has been to decrease the forecasted error value. Finally, the result shows that FSC implementation for STLF of regional load have more accuracy and better outcomes. © Published under licence by IOP Publishing Ltd.

Affiliations

Program Studi Teknik Elektro, FPTK Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi No. 207, Bandung, Indonesia

View full text (DOI) →

Research at a Glance

Research at a Glance

Topics & SDG Alignment

Collaboration

Author Profile (Selected)

References Overview

Journal & Source

Metadata & Integrity