An Adaptive-Network-Based Fuzzy Inference System-Data Envelopment Analysis Algorithm for Optimization of Long-Term Electricity Consumption, Forecasting and Policy Analysis: The Case of Seven Industrialized Countries

Abstract This article presents an adaptive-network-based fuzzy inference system (ANFIS)-data envelopment analysis (DEA) algorithm for improvement of long-term electricity consumption forecasting and analysis. Six models are proposed to forecast annual electricity demand. Six different membership functions and several linguistic variables are considered in building ANFIS. The proposed models consist of two input variables, namely, gross domestic product and population. All trained ANFIS are then compared with respect to mean absolute percentage error. To meet the best performance of the intelligent-based approaches, data are pre-processed (scaled) and finally our outputs are post-processed (returned to its original scale). DEA is used to optimize the electricity consumption as well as examine the behavior of electricity consumption. To show the applicability and superiority of the ANFIS-DEA algorithm, actual electricity consumption in the USA, Canada, Germany, United Kingdom (UK), Japan, France and Italy from 1980–2007 is considered. Electricity consumption is then forecasted up to 2015. The unique features of the ANFIS-DEA algorithm are: behavioral analysis and optimization in complex, non-linear and uncertain environments.

[1]  Kelvin K. W. Yau,et al.  Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .

[2]  S Gonzales Chavez,et al.  Forecasting of energy production and consumption in Asturias (northern Spain) , 1999 .

[3]  Yahachiro Tsukamoto,et al.  AN APPROACH TO FUZZY REASONING METHOD , 1993 .

[4]  A. Azadeh,et al.  A total fuzzy regression algorithm for energy consumption estimation , 2008, 2008 6th IEEE International Conference on Industrial Informatics.

[5]  Gholam Reza Jahanshahloo,et al.  A Complete Efficiency Ranking of Decision Making Units in Data Envelopment Analysis , 1999, Comput. Optim. Appl..

[6]  Shiro Kadoshin,et al.  The trend in current and near future energy consumption from a statistical perspective , 2000 .

[7]  A. Jai Persaud,et al.  An eclectic approach in energy forecasting: a case of Natural Resources Canada's (NRCan's) oil and gas outlook☆ , 2001 .

[8]  F. Gorucu.  Artificial Neural Network Modeling for Forecasting Gas Consumption , 2004 .

[9]  Ahmed Nafidi,et al.  Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model , 2005 .

[10]  A. J. Persuad,et al.  An eclectic approach in energy forecasting: a case of Natural Resources Canada's (NRCan's) oil and gas outlook , 2002 .

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

[12]  Stanislaw Nagy,et al.  Estimation of natural-gas consumption in Poland based on the logistic-curve interpretation , 2003 .

[13]  Alireza Khotanzad,et al.  Combination of artificial neural-network forecasters for prediction of natural gas consumption , 2000, IEEE Trans. Neural Networks Learn. Syst..

[14]  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.

[15]  Harun Kemal Ozturk,et al.  Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach , 2004 .

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

[17]  Ali Azadeh,et al.  INTEGRATION OF GENETIC ALGORITHM, COMPUTER SIMULATION AND DESIGN OF EXPERIMENTS FOR FORECASTING ELECTRICAL ENERGY CONSUMPTION , 2007 .

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

[19]  E. F. Sánchez-Úbeda,et al.  Modeling and forecasting industrial end-use natural gas consumption☆ , 2007 .

[20]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[21]  V. Ediger,et al.  Forecasting the primary energy demand in Turkey and analysis of cyclic patterns , 2002 .

[22]  P. Balachandra,et al.  Integrated energy-environment-policy analysis — a case study of India , 2003 .

[23]  A. Azadeh,et al.  An adaptive network based fuzzy inference system-fuzzy data envelopment analysis for gas consumption forecasting and analysis: The case of South America , 2010, 2010 International Conference on Intelligent and Advanced Systems.

[24]  Ali Azadeh,et al.  Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors , 2008 .

[25]  Jyoti K. Parikh,et al.  Demand projections of petroleum products and natural gas in India , 2007 .

[26]  M. Saberi,et al.  Improved Estimation of Electricity Demand Function by Integration of Fuzzy System and Data Mining Approach , 2006, 2006 IEEE International Conference on Industrial Technology.

[27]  Ali Azadeh,et al.  A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE , 2011 .

[28]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[29]  Ali Azadeh,et al.  An Adaptive-Network-Based Fuzzy Inference System for Long-Term Electric Consumption Forecasting (2008-2015): A Case Study of the Group of Seven (G7) Industrialized Nations: U.S.A., Canada, Germany, United Kingdom, Japan, France and Italy , 2010, 2010 Fourth UKSim European Symposium on Computer Modeling and Simulation.

[30]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[31]  Guoqiang Peter Zhang,et al.  Neural network forecasting for seasonal and trend time series , 2005, Eur. J. Oper. Res..

[32]  A. Azadeh,et al.  A review and comparison of fuzzy regression models for energy consumption estimation , 2008, 2008 6th IEEE International Conference on Industrial Informatics.

[33]  OLCAY ERSEL CANYURT,et al.  Energy Demand Estimation Based on Two-Different Genetic Algorithm Approaches , 2004 .

[34]  Seung-Jun Kwak,et al.  Estimating the residential demand function for natural gas in Seoul with correction for sample selection bias , 2009 .

[35]  Larry Chuen-ho Chow,et al.  A study of sectoral energy consumption in Hong Kong (1984-97) with special emphasis on the household sector , 2002 .

[36]  . S.FaridGhader,et al.  Modeling and Forecasting the Electricity Demand for Major Economic Sectors of Iran , 2006 .

[37]  Ali Azadeh,et al.  An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea , 2011 .