A STLF in distribution systems - A short comparative study between ANFIS Neuro-Fuzzy and ANN approaches - part I

Author

Santos, P.J. ; Rafael, S. ; Lobato, P. ; Pires, A.J.

Author_Institution

Dept. of Electr. Eng. at EST Setubal, Polytech. Inst. of Setubal, Setubal

fYear

2009

fDate

18-20 March 2009

Firstpage

661

Lastpage

665

Abstract

The STLF algorithms belong to the set of methodologies which aim to furnish more effectiveness in planning, operation and conduction in electric energy systems. Actions like, maintenance issues, network management, and eventual power purchase decisions within liberalized electricity markets require, among others, reliable next-hour load forecasts. Regressive methods are widely used. Artificial neural networks (ANN), models based on fuzzy inference (ANFIS) and neuro-fuzzy (NF) are used in short-term problems (one hour ahead). In this paper it´s made a short comparative study in order to compare these three approaches for the same case study. These methodological approaches are discussed in a real life case study.

Keywords

fuzzy reasoning; load forecasting; neural nets; power distribution planning; power engineering computing; power markets; artificial neural networks; distribution systems; electric energy system planning; fuzzy inference; liberalized electricity markets; load forecasting; network management; neuro-fuzzy approach; power purchase decisions; Artificial neural networks; Electricity supply industry; Energy management; Fuzzy neural networks; Load forecasting; Maintenance; Power system management; Power system modeling; Power system planning; Power system reliability; ANFIS; Artificial neural network; Consumption trend; Electrical distribution network; Short-term load forecast;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering, Energy and Electrical Drives, 2009. POWERENG '09. International Conference on

Conference_Location

Lisbon

Print_ISBN

978-1-4244-4611-7

Electronic_ISBN

978-1-4244-2291-3

Type

conf

DOI

10.1109/POWERENG.2009.4915196

Filename

4915196