01915nam a2200253 a 4500 vtls000055190 KUKTEM 20251114204452.0 110804t2009 my a f m 000 0 eng d THE0006681(Local) 201905171506 amirul 201108041004 Fida UMP TK1005 .T35 2009 rs Bc. Tai, Hein Fong Case study of short-term electricity load forecasting with temperature dependency / Tai Hein Fong Case study of short-term electricity load forecasting with temperature dependency [computer file] Kuantan, Pahang : UMP, 2009 xiv, 112 p. : ill. (some col.) ; 30 cm. + 1 computer disc 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. Electric power-plants Load Forecasting Electric power systems 0 0 0 1 20000 20000 2019-09-04 0 TK1005 .T35 2009 rs Bc. 0000058382 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 CD 5372 | TK1005 .T35 2009 rs Bc. 0000058383 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 2569 2575 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*6501*9992