Parallel neural network-fuzzy expert system strategy for short-term load forecasting: system implementation and performance evaluation

Author

Srinivasan, Dipti ; Tan, Swee Sien ; Cheng, C.S. ; Chan, Eng Kiat

Author_Institution

Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore

Volume

14

Issue

3

fYear

1999

fDate

8/1/1999 12:00:00 AM

Firstpage

1100

Lastpage

1106

Abstract

The online implementation and results from a hybrid short-term electrical load forecaster that is being evaluated by a power utility are documented in this paper. This forecaster employs a new approach involving a parallel neural-fuzzy expert system, whereby Kohonen´s self-organizing feature map with unsupervised learning, is used to classify daily load patterns. Post-processing of the neural network outputs is performed with a fuzzy expert system which successfully corrects the load deviations caused by the effects of weather and holiday activity. Being highly automated, little human interference is required during the process of load forecasting. A comparison made between this model and a regression-based model currently being used in the control centre has shown a marked improvement in load forecasting results

Keywords

expert systems; fuzzy neural nets; load forecasting; parallel processing; power system analysis computing; Kohonen´s self-organizing feature map; computer simulation; daily load patterns classification; holiday activity; load deviations correction; parallel neural network-fuzzy expert system; performance evaluation; post-processing; short-term load forecasting; system implementation; unsupervised learning; weather effects; Automatic control; Expert systems; Humans; Hybrid intelligent systems; Interference; Load forecasting; Neural networks; Predictive models; Unsupervised learning; Weather forecasting;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.780934

Filename

780934