Volume No. :   1

Issue No. :  2

Year :  2010

ISSN Print :  0976-2973

ISSN Online :  2321-581X


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Short Term Load Forecasting a Case Study of Kota City



Address:   Ajay Sharma1*, Annapurna Bhargava2, R.D. Rathor3, Fanibhushan Sharma4 and Nirmala Sharma5
1Asst. Professor Maharishi Arvind International Institute of Technology Kota (RAJ)
2Associate Professor and Head, Dept. of Electrical Engg., Rajasthan Technical University Kota(RAJ)
3Sr. Lecturer, Govt. Polytechnic, Kota(RAJ)
4Asst. Professor, St. Margaret Engineering College, Neemrana (RAJ)
5Asst. Professor, Rajasthan Technical University, Kota(RAJ)
*Corresponding Author
DOI No:

ABSTRACT:
Short-term load forecasting is an important component in the power system load forecast, it is very important to unit optimum combination, economic scheduling, optimum current of dispatching department. Classical load forecasting methods include time sequence, regression method, and so on, but many of them have defects, for example, numerical value is instability and the factor which influences load can’t be considered. Artificial intelligence method is main method now; neural network BP algorithm is representative among of them. When using neural network to predict electric power load, front neural network can predict with more precision fitting high linking and non-linear relation of shining upon between inputting and outputting from complicated sample data through studying. However some new problems have appeared while predicting electric power load using this method, it can't distinguish the impact on load data of the influence factor clearly, network structure can't be optimized and fixed automatically and need to confirm network structure artificially, the result is easy to fall into local optimum. So General Regression Neural Network—GRNN is proposed in this paper, it achieves global optimizing and can sample or calculate the data obtained to revise the network directly under the same structure, it need not calculate the parameter again, but only need a simple smooth parameter, it needn't carry on the training course of circulation.
KEYWORDS:
Short Term Load Forecasting, Artificial Neural Network, Generalized Regression Neural Network, Radial Basis function.
Cite:
Ajay Sharma, Annapurna Bhargava, R.D. Rathor, Fanibhushan Sharma, Nirmala Sharma. Short Term Load Forecasting a Case Study of Kota City. Research J. Engineering and Tech. 1(2): Oct. - Dec.2010 page 58-64.
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