Short term daily average and peak load predications using a hybrid intelligent approach
Conference
·
OSTI ID:438709
- Regional Engineering Coll., Rourkela (India)
- 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
- Country of Publication:
- United States
- Language:
- English
Similar Records
Heterogeneous artificial neural network for short term electrical load forecasting
Heterogeneous artificial neural network for short term electrical load forecasting
A neural network based technique for short-term forecasting of anomalous load periods
Journal Article
·
Wed Jan 31 23:00:00 EST 1996
· IEEE Transactions on Power Systems
·
OSTI ID:244773
Heterogeneous artificial neural network for short term electrical load forecasting
Conference
·
Sat Dec 30 23:00:00 EST 1995
·
OSTI ID:367392
A neural network based technique for short-term forecasting of anomalous load periods
Journal Article
·
Thu Oct 31 23:00:00 EST 1996
· IEEE Transactions on Power Systems
·
OSTI ID:435361