01687nam a2200205 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100001900109245010300128246010300231260003400334300006500368502010700433520086900540650004501409650002701454vtls000055190KUKTEM20251114204452.0110804t2009    my a   f m    000 0 eng d  aTHE0006681(Local)  aUMP1 aTai, Hein Fong10aCase study of short-term electricity load forecasting with temperature dependency /cTai Hein Fong3 aCase study of short-term electricity load forecasting with temperature dependencyh[computer file]  aKuantan, Pahang :bUMP,c2009  axiv, 112 p. :bill. (some col.) ;c30 cm. +e1 computer disc  aProject paper (Bachelor of Electrical Engineering (Power System)) -- Universiti Malaysia Pahang - 20093 aLoad 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. 0aElectric power-plantsxLoadxForecasting 0aElectric power systems