Short term daily average and peak load predications using a hybrid intelligent approach

Conference · Sun Dec 31 00:00:00 EST 1995

OSTI ID:438709

Dash, P K; Satpathy, H P ^[1]; Rahman, S ^[2]

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1. Regional Engineering Coll., Rourkela (India)
2. Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States)

A fuzzy neural network based on the multilayer perceptron and capable of fuzzy classification of patterns is presented in this paper. A hybrid learning algorithm consisting of unsupervised and supervised learning phases is used for training the network. In the supervised learning phase linear Kalman filter equations are used for tuning the weights and membership functions. Extensive tests have been performed on two-year utility data for generation of peak and average load profiles for 24- and 168-hours ahead time frames and results for winter and summer months are given to confirm the effectiveness of the new approach.

• Sponsoring Organization: National Science Foundation, Washington, DC (United States)
• OSTI ID: 438709
• Report Number(s): CONF-951136-; ISBN 0-7803-2981-3; TRN: IM9711%%170
• Resource Relation: Conference: 1995 International conference on energy management and power delivery, Singapore (Singapore), 21-23 Nov 1995; Other Information: PBD: 1995; Related Information: Is Part Of EMPD `95 -- 1995 international conference on energy management and power delivery: Proceedings. Volume 2; PB: 325 p.
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
29 ENERGY PLANNING AND POLICY
PEAK LOAD
POWER DEMAND
NEURAL NETWORKS
FORECASTING
EXPERT SYSTEMS
LEARNING