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 / Case study of short-term electricity load forecasting with temperature dependency [computer file] Tai Hein Fong - 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.
THE0006681(Local)
Electric power-plants--Load--Forecasting
Electric power systems
Case study of short-term electricity load forecasting with temperature dependency / Case study of short-term electricity load forecasting with temperature dependency [computer file] Tai Hein Fong - 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.
THE0006681(Local)
Electric power-plants--Load--Forecasting
Electric power systems