LONG-TERM ELECTRICAL ENERGY CONSUMPTION FORECASTING FOR DEVELOPING AND DEVELOPED ECONOMIES BASED ON DIFFERENT OPTIMIZED MODELS AND HISTORICAL DATA TYPES

The objectives of this study are (a) development of optimized regression and ANN (artificial neural network) models for EEC (electrical energy consumption) forecasting based on several optimization methodologies, (b) examination of the effects of different historical data types on accuracy of EEC forecasting, and (c) long-term EEC forecasting for Iran and the U.S. as developing and developed economies, respectively. For long-term EEC forecasting for 2010–2030, the two types of historical data used in this study include, (i) EEC and (ii) socio-economic indicators, namely, gross domestic product, energy imports, energy exports, and population, for 1967–2009 period. For both types of economies, the results demonstrate that using historical data of socio-economic indicators leads to more accurate EEC forecasting than those of EEC, when IPSO (improved particle swarm optimization) is used for optimal design of ANN for EEC forecasting. It is found that for developing and developed economies, forecasted EEC trends are significantly different, as expected, and IPSO–ANN model can be utilized to forecast long-term EEC up to 2030 with mean absolute percentage error of 1.94 and 1.51% for Iran and the U.S., respectively.

[1]  M. M. Ardehali,et al.  Integrated resource planning for Iran: Development of reference energy system, forecast, and long-term energy-environment plan , 2012 .

[2]  P. K. Adom,et al.  Conditional dynamic forecast of electrical energy consumption requirements in Ghana by 2020: A comparison of ARDL and PAM , 2012 .

[3]  Xiping Wang,et al.  Grey prediction with rolling mechanism for electricity demand forecasting of Shanghai , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.

[4]  Diyar Akay,et al.  Grey prediction with rolling mechanism for electricity demand forecasting of Turkey , 2007 .

[5]  Jianzhou Wang,et al.  Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China , 2012 .

[6]  Xin Jin,et al.  Application of a Hybrid Model to Short-Term Load Forecasting , 2010, 2010 International Conference of Information Science and Management Engineering.

[7]  Turan Paksoy,et al.  A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey , 2012 .

[8]  Samer S. Saab,et al.  Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon , 2001 .

[9]  S. F. Ghaderi,et al.  Integration of Artificial Neural Networks and Genetic Algorithm to Predict Electrical Energy consumption , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[10]  P. Bodger,et al.  Forecasting electricity consumption in New Zealand using economic and demographic variables , 2005 .

[11]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[12]  Kadir Kavaklioglu,et al.  Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression , 2011 .

[13]  Yuanwei Jing,et al.  Short term load forecasting: A dynamic neural network based genetic algorithm optimization , 2010, Proceedings of 14th International Power Electronics and Motion Control Conference EPE-PEMC 2010.

[14]  M. M. Ardehali,et al.  Rural energy development in Iran: Non-renewable and renewable resources , 2006 .

[15]  Ghassan Dibeh,et al.  Econometric modeling of electricity consumption in post-war Lebanon , 2000 .

[16]  Ping-Feng Pai,et al.  Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .

[17]  G. T. S. Ho,et al.  A fuzzy logic approach to forecast energy consumption change in a manufacturing system , 2008, Expert Syst. Appl..

[18]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[19]  Ahmet Serdar Yilmaz,et al.  Long Term Energy Consumption Forecasting Using Genetic Programming , 2008 .

[20]  Ali Azadeh,et al.  A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran , 2008 .

[21]  Ge Xiurun,et al.  An improved PSO-based ANN with simulated annealing technique , 2005, Neurocomputing.

[22]  A. Azadeh,et al.  A meta-heuristic framework for forecasting household electricity consumption , 2011, Appl. Soft Comput..

[23]  Turan Paksoy,et al.  Swarm intelligence approaches to estimate electricity energy demand in Turkey , 2012, Knowl. Based Syst..

[24]  Hui Zhao,et al.  Analysis of Energy Consumption Prediction Model Based on Genetic Algorithm and Wavelet Neural Network , 2011, 2011 3rd International Workshop on Intelligent Systems and Applications.

[25]  Ilias Petrounias,et al.  A hybrid intelligent approach for the prediction of electricity consumption , 2012 .

[26]  Mehdi Khashei,et al.  A novel hybridization of artificial neural networks and ARIMA models for time series forecasting , 2011, Appl. Soft Comput..

[27]  Gwo-Ching Liao,et al.  Novel GA-based approach and neural fuzzy networks application in short-term load forecasting , 2004, IEEE Power Engineering Society General Meeting, 2004..

[28]  V. Bianco,et al.  Electricity consumption forecasting in Italy using linear regression models , 2009 .

[29]  M. R. AlRashidi,et al.  LONG TERM ELECTRIC LOAD FORECASTING BASED ON PARTICLE SWARM OPTIMIZATION , 2010 .

[30]  G. H. Riahy,et al.  Optimal design of a reliable hydrogen-based stand-alone wind/PV generating system, considering component outages , 2009 .

[31]  Ping Zhang,et al.  Fuzzy Wavelet Neural Networks for City Electric Energy Consumption Forecasting , 2012 .

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

[33]  Wei-Chiang Hong,et al.  Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model , 2009 .

[34]  K Padmakumari,et al.  Long term distribution demand forecasting using neuro fuzzy computations , 1999 .

[35]  Lambros Ekonomou,et al.  Greek long-term energy consumption prediction using artificial neural networks , 2010 .

[36]  Hossein Nezamabadi-pour,et al.  Estimation of electricity demand of Iran using two heuristic algorithms , 2010 .

[37]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[38]  S. A. Soliman,et al.  Long-term/mid-term electric load forecasting based on short-term correlation and annual growth , 2005 .

[39]  Alper Ünler,et al.  Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025 , 2008 .

[40]  Raymond R. Tan,et al.  Hybrid evolutionary computation for the development of pollution prevention and control strategies , 2007 .

[41]  Wei Sun,et al.  Application of Neural Network Model Based on Combination of Fuzzy Classification and Input Selection in Short Term Load Forecasting , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[42]  Hak-Keung Lam,et al.  Short-term electric load forecasting based on a neural fuzzy network , 2003, IEEE Trans. Ind. Electron..

[43]  M. Toksari.  Ant colony optimization approach to estimate energy demand of Turkey , 2007 .

[44]  V. Bianco,et al.  Analysis and forecasting of nonresidential electricity consumption in Romania , 2010 .