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Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting

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3 Author(s)
Dipti Srinivasan ; Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore ; C. S. Chang ; A. C. Liew

This paper describes the implementation and forecasting results of a hybrid fuzzy neural technique, which combines neural network modeling, and techniques from fuzzy logic and fuzzy set theory for electric load forecasting. The strengths of this powerful technique lie in its ability to forecast accurately on weekdays, as well as, on weekends, public holidays, and days before and after public holidays. Furthermore, use of fuzzy logic effectively handles the load variations due to special events. The fuzzy-neural network (FNN) has been extensively tested on actual data obtained from a power system for 24-hour ahead prediction based on forecast weather information. Very impressive results, with an average error of 0.62% on weekdays, 0.83% on Saturdays and 1.17% on Sundays and public holidays have been obtained. This approach avoids complex mathematical calculations and training on many years of data, and is simple to implement on a personal computer.

Published in:

IEEE Transactions on Power Systems  (Volume:10 ,  Issue: 4 )