Title :
Non iterative-state estimation based neural network for short term load forecasting of distribution systems
Author :
Prakash, K. ; Sydulu, M.
Author_Institution :
Nat. Inst. of Technol., Warangal, India
Abstract :
In this paper, a new non-iterative state estimation based neural network is proposed for solving short term load forecasting of distribution systems. In this approach, the weights between the layers of neural network have been estimated using the weighted least square state estimation (WLSSE) technique without any iterative approach. The WLSSE technique could offer well established weights by accounting noise associated with the input and output data. This approach is very fast compared to conventional back propagation technique. The proposed method can predict day-ahead loads of the distribution system. The effectiveness of the method is examined on practical NPDCL Warangal, Indian 132/33 kV substation distribution system. The method can provide more accurate results than the back propagation neural networks. The test results are compared with that of the results obtained from conventional ANN method.
Keywords :
backpropagation; least squares approximations; load forecasting; neural nets; power distribution; power engineering computing; state estimation; substations; ANN method; artificial neural network; back propagation technique; distribution systems; noniterative state estimation; short term load forecasting; substation distribution system; weighted least square state estimation techniques; Artificial neural networks; Computer errors; Iterative methods; Learning systems; Least squares approximation; Load forecasting; Neural networks; Power system reliability; State estimation; Testing; Back Propagation Neural Network; Distribution System; Short Term Load Forecasting; Weighted Least Square State Estimation;
Conference_Titel :
Power & Energy Society General Meeting, 2009. PES '09. IEEE
Conference_Location :
Calgary, AB
Print_ISBN :
978-1-4244-4241-6
DOI :
10.1109/PES.2009.5275474
