Short Term Load Forecasting Using Data Mining Technique

Intan Azmira , Wan Abdul Razak (2008) Short Term Load Forecasting Using Data Mining Technique. Masters thesis, Universiti Teknologi Malaysia.

[img] PDF (24 Pages)
Short_Term_Load_Forecasting_Using_Data_Mining_Technique.pdf - Submitted Version
Restricted to Registered users only

Download (1MB)

Abstract

Accurate load and price forecasting are very essential in power system planning. These will increase the efficiency of electricity generation and distribution while maintaining sufficient security of operation. Short Term Load Forecasting is important for several operational decisions such as economic scheduling of generating capacity, scheduling of fuel purchases, security assessment and planning for transmission. This thesis proposes method for Short Term Load Forecasting using data mining technique. The load data provided by utility of Malaysia has been analyzed to see its behavior or load pattern in a day during weekday and weekend in Peninsular Malaysia. By considering day-type in a week, five model of SAR.IMA (Seasonal ARIMA) has been created using Minitab. The forecasting is implemented based on the similar repeating trend of patterns from historical load data. The half hourly load data for six weeks had been plotted according to day-type to forecast the load demand for a day ahead. The MAPEs (Mean Absolute Percentage Errors) obtained ranging from 1.07% to 3.26%. Hence this modeling had improved the accuracy of forecasting rather than using only one model for all day in a week.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Electric power-plants -- Load -- Forecasting, Electric power consumption -- Forecasting, Electric utilities -- Planning
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Library > Tesis > FKE
Depositing User: Siti Syahirah Ab Rahim
Date Deposited: 02 Oct 2014 17:25
Last Modified: 28 May 2015 04:30
URI: http://eprints.utem.edu.my/id/eprint/13294
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item