Please use this identifier to cite or link to this item: https://doi.org/10.1049/ip-gtd:19941288
Title: Forecasting daily load curves using a hybrid fuzzy-neural approach
Authors: Srinivasan, D. 
Liew, A.C. 
Chang, C.S. 
Issue Date: Nov-1994
Source: Srinivasan, D., Liew, A.C., Chang, C.S. (1994-11). Forecasting daily load curves using a hybrid fuzzy-neural approach. IEE Proceedings: Generation, Transmission and Distribution 141 (6) : 561-567. ScholarBank@NUS Repository. https://doi.org/10.1049/ip-gtd:19941288
Abstract: A new approach to electric load forecasting which combines the powers of neural network and fuzzy logic techniques is proposed. Expert knowledge represented by fuzzy rules is used for preprocessing input data fed to a neural network. The method effectively deals with trends and special events that occur annually. The fuzzy-neural network is trained on real data from a power system and evaluated for forecasting next-day load profiles based on forecast weather data and other parameters. Simulation results are presented to illustrate the performance and applicability of this approach. A comparison of results with other forecasting techniques establishes its superiority.
Source Title: IEE Proceedings: Generation, Transmission and Distribution
URI: http://scholarbank.nus.edu.sg/handle/10635/62208
ISSN: 13502360
DOI: 10.1049/ip-gtd:19941288
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