Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors

This paper presents an artificial neural network (ANN) approach for annual electricity consumption in high energy consumption industrial sectors. Chemicals, basic metals and non-metal minerals industries are defined as high energy consuming industries. It is claimed that, due to high fluctuations of energy consumption in high energy consumption industries, conventional regression models do not forecast energy consumption correctly and precisely. Although ANNs have been typically used to forecast short term consumptions, this paper shows that it is a more precise approach to forecast annual consumption in such industries. Furthermore, the ANN approach based on a supervised multi-layer perceptron (MLP) is used to show it can estimate the annual consumption with less error. Actual data from high energy consuming (intensive) industries in Iran from 1979 to 2003 is used to illustrate the applicability of the ANN approach. This study shows the advantage of the ANN approach through analysis of variance (ANOVA). Furthermore, the ANN forecast is compared with actual data and the conventional regression model through ANOVA to show its superiority. This is the first study to present an algorithm based on the ANN and ANOVA for forecasting long term electricity consumption in high energy consuming industries.

[1]  V. Jain,et al.  Modelling of electrical energy consumption in Delhi , 1999 .

[2]  Rey-Chue Hwang,et al.  An adaptive modular artificial neural network hourly load forecaster and its implementation at electric utilities , 1995 .

[3]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[4]  Hsiao-Tien Pao,et al.  Forecasting electricity market pricing using artificial neural networks , 2007 .

[5]  George G. Karady,et al.  Advancement in the application of neural networks for short-term load forecasting , 1992 .

[6]  Ping-Feng Pai,et al.  Hybrid ellipsoidal fuzzy systems in forecasting regional electricity loads , 2006 .

[7]  Chia-Yon Chen,et al.  Regional load forecasting in Taiwanapplications of artificial neural networks , 2003 .

[8]  Seref Sagiroglu,et al.  Power factor correction technique based on artificial neural networks , 2006 .

[9]  T. Yalcinoz,et al.  Short term and medium term power distribution load forecasting by neural networks , 2005 .

[10]  A. Gil,et al.  Forecasting of electricity prices with neural networks , 2006 .

[11]  Yusuf Oysal,et al.  A comparative study of adaptive load frequency controller designs in a power system with dynamic neural network models , 2005 .

[12]  Fuat Egelioğlu,et al.  Economic variables and electricity consumption in Northern Cyprus , 2001 .

[13]  Tommy W. S. Chow,et al.  Neural network based short-term load forecasting using weather compensation , 1996 .

[14]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[15]  Osama A. Mohammed,et al.  Practical experiences with an adaptive neural network short-term load forecasting system , 1995 .

[16]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[17]  Moon-Hee Park,et al.  Short-term Load Forecasting Using Artificial Neural Network , 1992 .

[18]  G. G. Karady,et al.  An adaptive neural network approach to one-week ahead load forecasting , 1993 .

[19]  T. Senjyu,et al.  Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market , 2006 .

[20]  Yuk Yee Yan Climate and residential electricity consumption in Hong Kong , 1998 .

[21]  Lon-Mu Liu,et al.  Dynamic structural analysis and forecasting of residential electricity consumption , 1993 .

[22]  Jae Hong Park,et al.  Composite modeling for adaptive short-term load forecasting , 1991 .

[23]  Alireza Khotanzad,et al.  An artificial neural network hourly temperature forecaster with applications in load forecasting , 1996 .

[24]  Ali Azadeh,et al.  Forecasting electrical consumption by integration of Neural Network, time series and ANOVA , 2007, Appl. Math. Comput..

[25]  V. Lo Brano,et al.  Forecasting daily urban electric load profiles using artificial neural networks , 2004 .

[26]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[27]  Benjamin F. Hobbs,et al.  Artificial neural networks for short-term energy forecasting: Accuracy and economic value , 1998, Neurocomputing.