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Fuzzified Neural Network Approach for Load Forecasting Problems
Int. J. on Engineering Intelligent Systems (2001)
  • D. K. Chaturvedi, Dayalbagh Educational Institute
  • P. S. Satsangi, Dayalbagh Educational Institute
  • P. K. Kalra
In load forecasting, the operator or the concerned person uses his or her experience and intuitions to obtain a good guess of the load demand. This guess is normally supported by sophisticated mathematical prediction techniques. The short term load not only varies from hour to hour, but is also influenced by the nature of events, load demand, the type of the load considered, seasonal variations, weekend day or holidays, and also by sudden demand and loss of load. Accordingly, it is quite clear that the electrical load-forecasting problem is quite difficult to model with mathematical difference or differential equations. In this paper the short term load forecasting problem has been formulated using artificial neural networks model. But the existing neural networks have various drawbacks like large training time, huge data requirement to train for a non linear complex load forecasting problem, the relatively larger number of hidden nodes required etc. Hence, an attempt has been made to develop a non linear load forecasting model using fuzzified neuron sub- models to overcome the above mentioned problems. These models would have the capability of representing operators' experience and complex mathematical formulation needed for short term load forecasting.
  • neural networks,
  • local forecasting,
  • ANNs model
Publication Date
March, 2001
Citation Information
D. K. Chaturvedi, P. S. Satsangi and P. K. Kalra. "Fuzzified Neural Network Approach for Load Forecasting Problems" Int. J. on Engineering Intelligent Systems Vol. 9 Iss. 1 (2001)
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