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...

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Main Author:	Shaari, Suhana
Format:	Thesis
Language:	English
Published:	2012
Online Access:	https://ir.uitm.edu.my/id/eprint/84792/1/84792.pdf
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id	my-uitm-ir.84792
record_format	uketd_dc
spelling	my-uitm-ir.847922024-01-15T09:00:20Z Short-term load forecasting using artificial neural network / Suhana Shaari 2012 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 https://ir.uitm.edu.my/id/eprint/84792/ https://ir.uitm.edu.my/id/eprint/84792/1/84792.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Zakaria, Zuhaina
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Zakaria, Zuhaina
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
qualification_level	Bachelor degree
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
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2012
url	https://ir.uitm.edu.my/id/eprint/84792/1/84792.pdf
_version_	1794192051111198720

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

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