Title :
Development of an Intelligent System for Short-Term Electrical Power Load Forecasting in Maharashtra State
Author :
Kelo, Sanjay M. ; Dudul, Sanjay V.
Author_Institution :
Prof. Ram Meghe Inst. of Technol. & Res., Badnera
Abstract :
In this paper an Elman RNN is developed to forecast Maharashtra´s total real time electrical power load reasonably one day in advance. This paper compares the performance of two NNs configurations namely a well known RBF and the proposed Elman RNN. Load data are clustered according to the differences in their characteristics. Special days are extracted from the normal training sets. In this way, solution is provided for different load types, including working days, weekend and, special days. RBFNN is constructed for the same data as a benchmark. It is shown that, the proposed Elman NN clearly outperforms the RBFNN. Results show that the optimal Elman NN model has average MSE as low as 0.00408; average correlation coefficient has as high as 0.8266 and, average prediction error 6.21% obtained for the year 2006. With the developed optimal Elman NN, the average of MAPE on all three clusters has been reduced by 1% which is the major outcome of this research.
Keywords :
load forecasting; power engineering computing; radial basis function networks; Elman RNN; Elman neural network; Maharashtra State; RBF; intelligent system; radial basis function; short-term electrical power load forecasting; Data mining; Intelligent systems; Load forecasting; Mean square error methods; Neural networks; Predictive models; Radial basis function networks; Real time systems; Recurrent neural networks; Weather forecasting; Elman neural network; Radial basis function neural network; Short-Term Electrical load forecasting;
Conference_Titel :
Power System Technology and IEEE Power India Conference, 2008. POWERCON 2008. Joint International Conference on
Conference_Location :
New Delhi
Print_ISBN :
978-1-4244-1763-6
Electronic_ISBN :
978-1-4244-1762-9
DOI :
10.1109/ICPST.2008.4745247
