H. S. Hippert, C. E. Pedreira and R. C. Souza, “Neural Networks for Short-Term Load Forecasting: A Review and Evaluation,” IEEE Transactions on Power Systems, Vol. 16, No. 1, 2011, pp. 44-55. doi: 10.1109/59.910780

has been cited by the following article:

• TITLE: A Hybrid Short Term Load Forecasting Model of an Indian Grid

  AUTHORS: R. Behera, B. P. Panigrahi, B. B. Pati

  KEYWORDS: Short Term Load Forecasting, Parameter Estimation, Trending Technique, Co-Relation

  JOURNAL NAME: Energy and Power Engineering, Vol.3 No.2, May 18, 2011

  ABSTRACT: This paper describes an application of combined model of extrapolation and correlation techniques for short term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of elec-trical load forecasting considering the factors, past data of the load, respective weather condition and finan-cial growth of the people. These factors are derived by curve fitting technique. Then simulation has been conducted using MATLAB tools. Here it has been suggested that consideration of 20 years data for a devel-oping country should be ignored as the development of a country is highly unpredictable. However, the im-portance of the past data should not be ignored. Here, just previous five years data are used to determine the above factors.