Electrical load forecasting using support vector machines

Belgin Emre Türkay*, Dilara Demren

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Citations (Scopus)

Abstract

In this study, an application with electrical load forecastingan important topic in the electrical industry - has been carried out by a machine learning method which has recently become popular: Support Vector Machines (SVM). Load forecasting with SVM can model the nonlinear relations with the factors that affect the load in addition to the accurate modelling of the load curve at the weekends and on important calendar days. The data gathered from the Istanbul European Side are used as a sample for the application. In addition to the past load data, daily average temperature, calendar days, holidays and electricity price are considered as an attribute in forecasting. The programme LibSVM is used for modelling the system. It is noted that SVM gave satisfactory results.

Original languageEnglish
Title of host publicationELECO 2011 - 7th International Conference on Electrical and Electronics Engineering
PagesI49-I53
Publication statusPublished - 2011
Event7th International Conference on Electrical and Electronics Engineering, ELECO 2011 - Bursa, Turkey
Duration: 1 Dec 20114 Dec 2011

Publication series

NameELECO 2011 - 7th International Conference on Electrical and Electronics Engineering

Conference

Conference7th International Conference on Electrical and Electronics Engineering, ELECO 2011
Country/TerritoryTurkey
CityBursa
Period1/12/114/12/11

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