Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72829
Title: One-day ahead electric load forecasting with hybrid fuzzy-neural networks
Authors: Srinivasan, Dipti 
Chang, C.S. 
Tan, Swee Sien
Issue Date: 1996
Source: Srinivasan, Dipti,Chang, C.S.,Tan, Swee Sien (1996). One-day ahead electric load forecasting with hybrid fuzzy-neural networks. Biennial Conference of the North American Fuzzy Information Processing Society - NAFIPS : 160-163. ScholarBank@NUS Repository.
Abstract: Short-term electrical load forecasting is essential to maintain economic operation of electric power systems. Although several techniques have surfaced in the field of load forecasting, efforts are still being made to develop a model that can achieve a reliable forecast with accurate results. This paper describes the development and implementation of a one-day ahead load forecaster based on a hybrid fuzzy-neural approach. Kohonen's self-organizing feature map with unsupervised learning is used for the classification of daily load patterns. Supervised back-propagation neural networks are then used for learning the temperature-related corrections of the load curves. A post-processing fuzzy controller is employed for fuzzy corrections for unusual load conditions, making the fuzzy-neural model robust in generating accurate predictions on all days of the week.
Source Title: Biennial Conference of the North American Fuzzy Information Processing Society - NAFIPS
URI: http://scholarbank.nus.edu.sg/handle/10635/72829
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

18
checked on Dec 16, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.