Title :
Annual Electricity Consumption Forecasting with Neural Network in High Energy Consuming Industrial Sectors of Iran
Author :
Azadeh, M. Ali ; Sohrabkhani, Sara
Author_Institution :
Tehran Univ., Tehran
Abstract :
Due to various changes in electricity consumption in Iran, it is hard to model with conventional methods and makes it suitable to estimate with Artificial Neural Network. Altough this method typically has been used to forecast short term consumptions, we use Neural Network to forecast annual consumption This paper illustrates an Artificial Neural Network (ANN) approach based on supervised multi layer perceptron (MLP) network for the electrical consumption forecasting and shows that it can estimate the annual consumption with lesser error. This study shows the advantage of Neural Network methodology through analysis of variance (ANOVA). Furthermore, actual data is compared with ANN and conventional regression model.
Keywords :
load forecasting; multilayer perceptrons; power engineering computing; ANN; Iran; MLP; annual electricity consumption forecasting; artificial neural network; electrical analysis of variance; high energy consuming industrial sectors; supervised multi layer perceptron; Artificial neural networks; Chemical industry; Construction industry; Electrical equipment industry; Energy consumption; Load forecasting; Manufacturing industries; Metals industry; Neural networks; Predictive models;
Conference_Titel :
Industrial Technology, 2006. ICIT 2006. IEEE International Conference on
Conference_Location :
Mumbai
Print_ISBN :
1-4244-0726-5
Electronic_ISBN :
1-4244-0726-5
DOI :
10.1109/ICIT.2006.372572