Title :
Using artificial neural network for providing hourly load update and next day load profile
Author :
Wu, Hsien-Tsai ; Lu, Chan-Nan
Author_Institution :
Dept. of Electr. Eng., Kung-Shan Inst. of Technol., Tainan, Taiwan
Abstract :
An artificial neural network technique is used to provide real time hourly load update, next day load profile, and daily peak load forecast. Weather and time factors are included in the proposed method by using them as the network´s inputs. Results from the neural network are compared with those from autoregressive moving average model with exogenous input (ARMAX). Experiments are conducted on two Utilities historical data, each contains a time period longer than one year. Forecasting accuracy is evaluated throughout a whole year in order to determine the effect of seasonal load variation on the accuracy of the proposed forecasting models
Keywords :
load forecasting; neural nets; power engineering computing; artificial neural network technique; autoregressive moving average model; daily peak load forecast; exogenous input; next day load profile; real time hourly load update; seasonal load variation; time factors; weather factors;
Conference_Titel :
Advances in Power System Control, Operation and Management, 1991. APSCOM-91., 1991 International Conference on
Conference_Location :
IET
Print_ISBN :
0-86341-246-7