Case study of short term load forecasting for weekends

Musa, Mohd Fadhil (2009) Case study of short term load forecasting for weekends. [Student Project] (Unpublished)

Abstract

This project report presents the short term load forecasting (STLF) to predict load demand in future. Short term load forecast (STLF) is a method to predict day ahead 24 hour load demand and two factors were considered which is the time and temperature. The objectives of this project are analyzing the profile or pattern and predict load demand during weekends. This project is use Artificial Neural Network (ANN) method to solve the problem using MATLAB software. The average error by using mean absolute percentage error (MAPE) for Friday, Saturday and Sunday are 1.25%, 1.39% and 2.04% respectively.

Metadata

Item Type:	Student Project
Creators:	Creators Email / ID Num. Musa, Mohd Fadhil UNSPECIFIED
Contributors:	Contribution Name Email / ID Num. Advisor Salim, Nur Ashida UNSPECIFIED Advisor Abdul Rahman, Titik Khawa UNSPECIFIED
Subjects:	T Technology > TK Electrical engineering. Electronics. Nuclear engineering T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electric power distribution. Electric power transmission
Divisions:	Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Programme:	Bachelor of Electrical Engineering (Hons.)
Keywords:	Artificial Neural Network (ANN), Multiple Linear Regression (MLR), Autoregressive moving average (ARMA)
Date:	2009
URI:	https://ir.uitm.edu.my/id/eprint/125462