Cover Image

Short-term load forecasting using artificial neural network / Suhana Shaari

This thesis presents a neural network based approach for short-term load forecasting that uses the most correlated weather data for training and testing the neural network of weather data determines the input parameters of the neural networks. Inputs to the ANN are past loads and the output of the A...

Full description

Saved in:
Bibliographic Details
Main Author: Shaari, Suhana
Format: Thesis
Language:English
Published: 2012
Online Access:https://ir.uitm.edu.my/id/eprint/84792/1/84792.pdf
https://ir.uitm.edu.my/id/eprint/84792/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uitm.ir.84792
record_format eprints
spelling my.uitm.ir.847922024-01-15T09:00:20Z https://ir.uitm.edu.my/id/eprint/84792/ Short-term load forecasting using artificial neural network / Suhana Shaari Shaari, Suhana This thesis presents a neural network based approach for short-term load forecasting that uses the most correlated weather data for training and testing the neural network of weather data determines the input parameters of the neural networks. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers is tested with various combinations of neurons, and the results are compared in term of forecasting error. Historical load data and temperature observations for the year 2006 - 2010 obtained from the Australian Energy Market Operator (AEMO) & Bereau of Meteordology (BOM) for Sydney/NSW. The inputs used were the hourly load demand for the full day (24 hours), the weather, humidity and holiday for the state.-The network trained over 4 year's data. A mean average percent error (MAPE) of 1.99% was achieved when the trained network was tested on one year data. 2012 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/84792/1/84792.pdf Short-term load forecasting using artificial neural network / Suhana Shaari. (2012) Degree thesis, thesis, Universiti Teknologi MARA (UiTM).
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
description This thesis presents a neural network based approach for short-term load forecasting that uses the most correlated weather data for training and testing the neural network of weather data determines the input parameters of the neural networks. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers is tested with various combinations of neurons, and the results are compared in term of forecasting error. Historical load data and temperature observations for the year 2006 - 2010 obtained from the Australian Energy Market Operator (AEMO) & Bereau of Meteordology (BOM) for Sydney/NSW. The inputs used were the hourly load demand for the full day (24 hours), the weather, humidity and holiday for the state.-The network trained over 4 year's data. A mean average percent error (MAPE) of 1.99% was achieved when the trained network was tested on one year data.
format Thesis
author Shaari, Suhana
spellingShingle Shaari, Suhana
Short-term load forecasting using artificial neural network / Suhana Shaari
author_facet Shaari, Suhana
author_sort Shaari, Suhana
title Short-term load forecasting using artificial neural network / Suhana Shaari
title_short Short-term load forecasting using artificial neural network / Suhana Shaari
title_full Short-term load forecasting using artificial neural network / Suhana Shaari
title_fullStr Short-term load forecasting using artificial neural network / Suhana Shaari
title_full_unstemmed Short-term load forecasting using artificial neural network / Suhana Shaari
title_sort short-term load forecasting using artificial neural network / suhana shaari
publishDate 2012
url https://ir.uitm.edu.my/id/eprint/84792/1/84792.pdf
https://ir.uitm.edu.my/id/eprint/84792/
_version_ 1789429217442660352
score 13.214268

Similar Items