Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Connectionism-based CBR method for distribution short-term nodal load forecasting

Wu, Jianzhong ORCID: and Yu, Y 2005. Connectionism-based CBR method for distribution short-term nodal load forecasting. Presented at: TENCON 2005, Melbourne, Australia, 21-24 November 2005. Tencon 2005 - 2005 IEEE Region 10 Conference, Melbourne, Australia, 21-24 November 2005. Piscataway, NJ: IEEE, pp. 1302-137. 10.1109/TENCON.2005.301217

Full text not available from this repository.


Short-term nodal load forecasting is very important for operation, planning and management of distribution networks, which is characterized by lack and imprecision of historical data, inconspicuous trend for load variation, and changeful pattern of nodal load. It is very hard for conventional methods to solve such problem. A connectionism-based case-based-reasoning method (CBCBR) is proposed based on parallel distributed processing (PDP) model. The principle of CBCBR is analyzed, the elementary architecture of CBCBR is defined, and a hybrid supervised/unsupervised learning algorithm, which equips CBCBR with a good generalization capability, is also proposed. CBCBR can build nodes and connections dynamically by the rapid and incremental learning procedure and can withstand the effect of bad data effectively through network self-organizing. The proposed method is tested using load data of a practical system and the test results is compared with that of BP network, RBF network and AR model.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords: Power systems , case-based reasoning , connectionism , load forecasting , power distribution
Publisher: IEEE
ISBN: 0780393112
Last Modified: 21 Oct 2022 10:35

Citation Data

Actions (repository staff only)

Edit Item Edit Item