Title :
Predictive model of load and price for restructured power system using neural network
Author :
Akole, Mohan ; Bongulwar, Milind ; Tyagi, Barjeev
Author_Institution :
Instrum. Eng. Dept., Gov. Coll. of Eng., Chandrapur, India
Abstract :
Load and price prediction are an important component in the economic and secures operation of the competitive restructured power system energy market. This paper presents the use of an artificial neural network to half hourly ahead load prediction and half hourly ahead price prediction applications. By using historical weather, load consumption, price and calendar data, a multi-layer feed forward (FF) neural network trained with Back propagation (BP) algorithm was developed for the half hour ahead prediction. The developed algorithm for half hourly prediction has been tested with Australian market data. The result of ANN prediction model is compared with the conventional Multiple Regression (MR) prediction model.
Keywords :
backpropagation; load forecasting; multilayer perceptrons; power consumption; power engineering computing; power markets; power system economics; power system security; ANN prediction model; Australian market data; artificial neural network; backpropagation algorithm; economic operation; half hour ahead prediction; historical weather; load consumption; load prediction; multilayer FF neural network; multilayer feedforward neural network training; power system security; predictive model; price prediction; restructured power system energy market; Analytical models; Artificial neural networks; Australia; Correlation; Load modeling; Predictive models; Training; Bad Data treatment; Load and Price Prediction; Multiple Regression (MR) and Artificial Neural Network (ANN); Selection of variables; electricity market;
Conference_Titel :
Energy, Automation, and Signal (ICEAS), 2011 International Conference on
Conference_Location :
Bhubaneswar, Odisha
Print_ISBN :
978-1-4673-0137-4
DOI :
10.1109/ICEAS.2011.6147106
