Please use this identifier to cite or link to this item:
|Title:||Forecasting daily load curves using a hybrid fuzzy-neural approach||Authors:||Srinivasan, D.
|Issue Date:||Nov-1994||Citation:||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/10635/80441||ISSN:||13502360||DOI:||10.1049/ip-gtd:19941288|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 16, 2019
WEB OF SCIENCETM
checked on May 8, 2019
checked on May 14, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.