A novel approach to short-term load forecasting using fuzzy neural networks

Author

Papadakis, S.E. ; Theocharis, J.B. ; Kiartzis, S.J. ; Bakirtzis, A.G.

Author_Institution

Dept. of Electr. & Comput. Eng., Aristotelian Univ. of Thessaloniki, Greece

Volume

13

Issue

2

fYear

1998

fDate

5/1/1998 12:00:00 AM

Firstpage

480

Lastpage

492

Abstract

An efficient modeling technique based on the fuzzy curve notion is developed in this paper to generate fuzzy models for short-term load forecasting. The suggested forecasting approach proceeds on the following steps: (a) prediction of the load curve extremals (peak and valley loads) using separate fuzzy models; (b) formulation of the representative day based on historical load data; and (c) mapping of the representative day load curve to the forecasted peak values to obtain the predicted day load curves. Very good prediction performance is attained as shown in the simulation results which verify the effectiveness of the modeling technique

Keywords

fuzzy neural nets; load forecasting; power system analysis computing; fuzzy curve notion; fuzzy models generation; fuzzy neural networks; historical load data; load curve extremals prediction; power systems; prediction performance; representative day load curve; short-term load forecasting; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Input variables; Least squares approximation; Load forecasting; Power engineering and energy; Power systems; Printing; Systems engineering and theory;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.667372

Filename

667372