Practical experiences with an adaptive neural network short-term load forecasting system

Author

Mohammed, O. ; Park, D. ; Merchant, R. ; Dinh, T. ; Tong, C. ; Azeem, A. ; Farah, J. ; Drake, C.

Author_Institution

Florida Int. Univ., Miami, FL, USA

Volume

10

Issue

1

fYear

1995

fDate

2/1/1995 12:00:00 AM

Firstpage

254

Lastpage

265

Abstract

An adaptive neural network based short-term electric load forecasting system is presented. The system is developed and implemented for Florida Power and Light Company (FPL). Practical experiences with the system are discussed. The system accounts for seasonal and daily characteristics, as well as abnormal conditions such as cold fronts, heat waves, holidays and other conditions. It is capable of forecasting load with a lead time of one hour to seven days. The adaptive mechanism is used to train the neural networks when on-line. The results indicate that the load forecasting system presented gives robust and more accurate forecasts and allows greater adaptability to sudden climatic changes compared with statistical methods. The system is portable and can be modified to suit the requirements of other utility companies

Keywords

load forecasting; neural nets; power system analysis computing; Florida Power and Light Company; abnormal conditions; adaptive neural network; cold fronts; daily characteristics; heat waves; holidays; neural network training; seasonal characteristics; short-term load forecasting system; statistical methods; Adaptive systems; Artificial neural networks; Demand forecasting; Fuels; Load forecasting; Neural networks; Power generation; Robustness; Statistical analysis; Weather forecasting;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.373948

Filename

373948