Fuzzy inference systems applied to LV substation load estimation

Author

Konjic, Tatjana ; Miranda, Vladimiro ; Kapetanovic, Izudin

Volume

20

Issue

2

fYear

2005

fDate

5/1/2005 12:00:00 AM

Firstpage

742

Lastpage

749

Abstract

This paper describes a system for estimating load curves at low-voltage (LV) substations. The system is built by the aggregation of individual fuzzy inference systems of the Takagi-Sugeno type. The model was developed from actual measurements forming a base of raw data of consumer behavior. This database allowed one to build large test and training sets of simulated LV substations, which led to the development of the fuzzy system. The results are compared in terms of accuracy with the ones obtained with a previous artificial neural network approach, with better performance.

Keywords

distribution networks; fuzzy set theory; fuzzy systems; inference mechanisms; load forecasting; neural nets; power engineering computing; substations; LV substation load estimation; Takagi-Sugeno type; artificial neural network; fuzzy inference system; load forecasting; low-voltage substation; power distribution; Artificial neural networks; Economic forecasting; Energy consumption; Fuzzy systems; Load forecasting; Monitoring; Predictive models; State estimation; Substations; Voltage; Fuzzy systems; load forecasting; power distribution;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/TPWRS.2005.846210

Filename

1425568