DocumentCode :
2040102
Title :
Adaptive short-term load forecasting using artificial neural networks
Author :
Gooi, H.B. ; Teo, C.Y. ; Chin, L. ; Ang, S.Y. ; Khor, E.K.
Author_Institution :
Nanyang Technol. Univ., Singapore
Volume :
2
fYear :
1993
fDate :
19-21 Oct. 1993
Firstpage :
787
Abstract :
A multi-layer artificial neural network (ANN) with an adaptive learning algorithm is used to forecast system hourly loads up to 168 hours for the Public Utilities Board (PUB) of Singapore. The ANN-based load models are trained using hourly historical load data and daily historical maximum/minimum temperature data supplied by the PUB and Meteorological Service Singapore respectively. The models are trained by day types to predict daily peak and valley loads. The hourly forecast loads are computed from the predicted peak and valley loads and average normalized loads for each day type. The average absolute error for a 24-hour ahead forecast using the actual load and temperature data is shown to be 2.32% for Mondays through Sundays and 5.98% for ten special day types in a year.<>
Keywords :
adaptive systems; electricity supply industry; feedforward neural nets; power engineering computing; 24-hour ahead forecast; ANN-based load models; Public Utilities Board; Singapore; adaptive learning algorithm; adaptive short-term load forecasting; artificial neural networks; average normalized loads; daily historical maximum/minimum temperature data; hourly forecast loads; hourly historical load data; multi-layer artificial neural network; special day types; system hourly loads; valley loads; Artificial neural networks; Databases; Load forecasting; Load modeling; Multi-layer neural network; Neural networks; Predictive models; Processor scheduling; Temperature; Weather forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
Conference_Location :
Beijing, China
Print_ISBN :
0-7803-1233-3
Type :
conf
DOI :
10.1109/TENCON.1993.320131
Filename :
320131
Link To Document :
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