Please use this identifier to cite or link to this item:
|Title:||Practical implementation of a hybrid fuzzy neural network for one-day-ahead load forecasting|
|Authors:||Srinivasan, D. |
|Keywords:||Fuzzy neural networks|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 12, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.