NDLI: Adaptive short-term load forecasting using artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	Gooi, H.B. Teo, C.Y. Chin, L. Ang, S.Y. Khor, E.K.
Copyright Year	1993
Description	Author affiliation: Nanyang Technol. Univ., Singapore (Gooi, H.B.; Teo, C.Y.; Chin, L.; Ang, S.Y.; Khor, E.K.)
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.
Starting Page	787
Ending Page	790
File Size	274817
Page Count	4
File Format	PDF
ISBN	0780312333
DOI	10.1109/TENCON.1993.320131
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Artificial neural networks Weather forecasting Load modeling Predictive models Neural networks Databases Temperature Processor scheduling Multi-layer neural network
Content Type	Text
Resource Type	Article

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