Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/81008
Title: Practical implementation of a hybrid fuzzy neural network for one-day-ahead load forecasting
Authors: Srinivasan, D. 
Tan, S.S.
Chang, C.S. 
Chan, E.K.
Keywords: Fuzzy neural networks
Load forecasting
Issue Date: 1998
Citation: Srinivasan, D.,Tan, S.S.,Chang, C.S.,Chan, E.K. (1998). Practical implementation of a hybrid fuzzy neural network for one-day-ahead load forecasting. IEE Proceedings: Generation, Transmission and Distribution 145 (6) : 687-692. ScholarBank@NUS Repository.
Abstract: The paper presents the development and practical implementation of a hybrid short-term electrical load forecasting model for a power system control centre. This hybrid architecture incorporates a Kohonen self-organising feature map with unsupervised learning for classification of daily load patterns, a supervised backpropagation neural network for mapping the temperature/load relationship, and a fuzzy expert system for postprocessing of neural network outputs. This load forecaster requires minimum operator intervention and can be trained adaptively on-line. The developed model has been tested extensively in the actual operating environment and has been shown to outperform the existing regression-based model. © IEE, 1998.
Source Title: IEE Proceedings: Generation, Transmission and Distribution
URI: http://scholarbank.nus.edu.sg/handle/10635/81008
ISSN: 13502360
Appears in Collections:Staff Publications

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