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Case study of short-term electricity load forecasting with temperature dependency / Tai Hein Fong

By:

Tai, Hein Fong

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2009Description: xiv, 112 p. : ill. (some col.) ; 30 cm. + 1 computer discISBN:

THE0006681(Local)

Other title:

Case study of short-term electricity load forecasting with temperature dependency [computer file]

Subject(s):

Dissertation note: Project paper (Bachelor of Electrical Engineering (Power System)) -- Universiti Malaysia Pahang - 2009 Abstract: Load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This is a case study of short-term load forecasting using Artificial Neural Networks (ANNs). This load forecasting program gives load forecasts half an hour in advance. Historical load data obtained from the electricity generation company will be use. The main stages are the pre-processing of the data sets, network training, and forecasting. The inputs used for the neural network are one set of historical load demand data and five sets of temperature data. The neural network used has 3 layers: an input, a hidden, and an output layer. The input layer has 5 neurons, the number of hidden layer neurons can be varied for the different performance of the network, while the output layer has a single neuron.

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Holdings ( 2 )
Title notes ( 2 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB PEKAN	TK1005 .T35 2009 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000058382
Final Year Report	UMPLIB PEKAN	CD 5372 \| TK1005 .T35 2009 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000058383

Project paper (Bachelor of Electrical Engineering (Power System)) -- Universiti Malaysia Pahang - 2009

Load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This is a case study of short-term load forecasting using Artificial Neural Networks (ANNs). This load forecasting program gives load forecasts half an hour in advance. Historical load data obtained from the electricity generation company will be use. The main stages are the pre-processing of the data sets, network training, and forecasting. The inputs used for the neural network are one set of historical load demand data and five sets of temperature data. The neural network used has 3 layers: an input, a hidden, and an output layer. The input layer has 5 neurons, the number of hidden layer neurons can be varied for the different performance of the network, while the output layer has a single neuron.