Title :
A New Spike Based Neural Network for Short-Term Electrical Load Forecasting
Author :
Kulkarni, Santosh ; Simon, Sishaj P.
Author_Institution :
Dept. of EEE, Nat. Inst. of Technol., Trichy, India
Abstract :
Accurate short-term power demand forecast is of great importance to utility companies for power system planning and operation. Based on the literature survey, artificial neural networks (ANN) are found to be an alternative to classical statistical methods in terms of the accuracy of the forecasted results. This paper presents the implementation of a Spiking Neural Network (SNN) for short-term load forecasting model to forecast one day ahead and one week ahead hourly demand pattern. The selection of input variables, SNN architecture and training algorithm are discussed in this paper. The suitability and the validation of the proposed model is investigated and compared with a feed forward back-propagation ANN model.
Keywords :
learning (artificial intelligence); load forecasting; neural nets; power engineering computing; power system planning; power utilisation; statistical analysis; SNN; artificial neural network; feedforward back-propagation ANN model; literature survey; new spike based neural network; power system planning; short-term electrical load forecasting; short-term power demand forecast; statistical method; training algorithm; Artificial neural networks; Biological neural networks; Load forecasting; Load modeling; Neurons; Predictive models; Training; ANN; SNN; Spike Response Model (SRM); short-term load forecasting;
Conference_Titel :
Computational Intelligence and Communication Networks (CICN), 2012 Fourth International Conference on
Conference_Location :
Mathura
Print_ISBN :
978-1-4673-2981-1
DOI :
10.1109/CICN.2012.26
