Towards estimation of electricity demand utilizing a robust multi-gene genetic programming technique

详细信息    查看全文

• 作者：Seyyed Mohammad Mousavi ; Elham Sadat Mostafavi ; Fariba Hosseinpour
• 关键词：Electricity demand ; Multi ; gene genetic programming ; Nonlinear system modeling
• 刊名：Energy Efficiency
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：8
• 期：6
• 页码：1169-1180
• 全文大小：883 KB
• 参考文献：Abdel-Aal, R. E. (2008). Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks. Computers & Industrial Engineering, 54(4), 903鈥?17.CrossRef
  Abdel-Aal, R. E., & Al-Garni, A. Z. (1997). Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis. Energy, 22, 1059鈥?069.CrossRef
  Abdel-Aal, R. E., Al-Garni, A. Z., & Al-Nassar, Y. N. (1997). Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks. Energy, 22(9), 911鈥?21.CrossRef
  Alavi, A. H., & Gandomi, A. H. (2011a). A robust data mining approach for formulation of geotechnical engineering systems. Engineering Computations, 28(3), 242鈥?74.MATH CrossRef
  Alavi, A. H., & Gandomi, A. H. (2011b). Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Computers & Structures, 89(23鈥?4), 2176鈥?194.CrossRef
  Alavi, A. H., & Gandomi, A. H. (2012). Energy-based models for assessment of soil liquefaction. Geoscience Frontiers, 3(4), 541鈥?55.CrossRef
  Alavi, A. H., Gandomi, A. H., Mousavi, M., & Mollahasani, A. (2010). High-precision modeling of uplift capacity of suction caissons using a hybrid computational method. Geomechanics and Engineering, 2(4), 253鈥?80.CrossRef
  Alavi, A. H., Gandomi, A. H., & Mollahasani, A. (2011a). A genetic programming-based approach for performance characteristics assessment of stabilized soil. Variants of evolutionary algorithms for real-world applications, Springer, Berlin, Chapter 9, 343鈥?75
  Alavi, A. H., Ameri, M., Gandomi, A. H., & Mirzahosseini, M. R. (2011b). Formulation of flow number of asphalt mixes using a hybrid computational method. Construction and Building Materials, 25(3), 1338鈥?355.CrossRef
  Annunziato, M., Bertini, I., Lucchetti, M., Pannicelli, A., & Pizzuti, S. (2004). The evolutionary control methodology: an overview. Artificial Evolution, Lecture Notes in Computer Science Volume, 2936, 331鈥?42.CrossRef
  Annunziato, M., Bertini, I., Iannone, R., & Pizzuti, S. (2006). Evolving feed-forward neural networks through evolutionary mutation parameters. Intelligent Data Engineering and Automated Learning鈥擨DEAL 2006. Lecture Notes in Computer Science Volume, 4224(2006), 554鈥?61.CrossRef
  Annunziato, M., Moretti, F., & Pizzuti, S. (2013). Urban traffic flow forecasting using neural-statistic hybrid modeling. Soft computing models in industrial and environmental applications. Advances in Intelligent Systems and Computing Volume, 188, 183鈥?90.CrossRef
  Azadeh, A., Ghaderi, S. F., Tarverdian, S., & Saberi, M. (2007). Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption. Applied Mathematics and Computation, 186, 1731鈥?741.MATH MathSciNet CrossRef
  Azadeh, A., Ghaderi, S. F., & Sohrabkhani, S. (2008a). Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and Management, 49, 2272鈥?278.CrossRef
  Azadeh, A., Ghaderi, S. F., & Sohrabkhani, S. (2008b). A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran. Energy Policy, 36, 2637鈥?644.CrossRef
  Bhattacharya, M., Abraham, A., & Nath, B. (2001). A linear genetic programming approach for modeling electricity demand prediction in Victoria. In Proceedings of the hybrid information systems, Adelaide, Australia, pp. 379鈥?93.
  Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: forecasting and control. San Francisco: Holden-Day.MATH
  Can, B., & Heavey, C. (2011). Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems. Computers & Industrial Engineering, 61(3), 447鈥?62.CrossRef
  Can, B., & Heavey, C. (2012). A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models. Computers & Operations Research, 39(2), 424鈥?36.CrossRef
  Catalao, J. P. S., Mariano, S. J. P. S., Mendes, V. M. F., & Ferreira, L. A. F. M. (2007). Short-term electricity prices forecasting in a competitive market: a neural network approach. Electric Power Systems Research, 77, 1297鈥?304.CrossRef
  Cho, M. Y., Hwang, J. C., & Che, S. (1995). Customer short term load forecasting by using ARIMA transfer function model. In Proceedings of the International Conference on Energy Management and Power Delivery, EMPD鈥?5, Singapore, pp. 317鈥?22.
  Currie, K. R. (1992). An intelligent grouping algorithm for cellular manufacturing. Computers & Industrial Engineering, 23(1鈥?), 109鈥?12.CrossRef
  Daim, T. U., Kayakutlu, G., & Cowan, K. (2010). Developing Oregon鈥檚 renewable energy portfolio using fuzzy goal programming model. Computers & Industrial Engineering, 59(4), 786鈥?93.CrossRef
  Desai, C. K., & Shaikh, A. (2012). Prediction of depth of cut for single-pass laser micro-milling process using semi-analytical, ANN and GP approaches. The International Journal of Advanced Manufacturing Technology, 60, 865鈥?82.CrossRef
  Ediger, V. S., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35, 1701鈥?708.CrossRef
  Fan, S., & Chen, L. (2006). Short-term load forecasting based on an adaptive hybrid method. IEEE Transactions on Power Systems, 21, 392鈥?01.CrossRef
  Gandomi, A. H., & Alavi, A. H. (2011). Multi-stage genetic programming: a new strategy to nonlinear system modeling. Information Sciences, 181(23), 5227鈥?239.CrossRef
  Gandomi, A. H., & Alavi, A. H. A. (2012a). New multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Computing & Applications, 21, 171鈥?87.CrossRef
  Gandomi, A. H., & Alavi, A. H. (2012b). A new multi-gene genetic programming approach to nonlinear system modeling. Part II: geotechnical and earthquake engineering problems. Neural Computing & Applications, 21, 189鈥?01.CrossRef
  Gandomi, A. H., & Alavi, A. H. (2013). Hybridizing genetic programming with orthogonal least squares for modeling of soil liquefaction. International Journal of Earthquake Engineering and Hazard Mitigation, 1(1), 2鈥?. Praise Worthy Prize.MathSciNet
  Gandomi, A. H., Alavi, A. H., Mirzahosseini, M. R., & Moghadas Nejad, F. (2011a). Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. Journal of Materials in Civil Engineering, 23(3), 1鈥?8.CrossRef
  Gandomi, A. H., Alavi, A. H., & Yun, G. J. (2011b). Nonlinear modeling of shear strength of SFRCB beams using linear genetic programming. Structural Engineering and Mechanics, 38(1), 1鈥?5.CrossRef
  Golbraikh, A., & Tropsha, A. (2002). Beware of q2. Journal of Molecular Graphics and Modelling, 20, 269鈥?76.CrossRef
  Gopalakrishnan, K., Kim, S., Ceylan, H., & Khaitan, S. K. (2010) Natural selection of asphalt stiffness predictive models with genetic programming. Proceedings of the Artificial Neural Networks In Engineering (ANNIE) 2010. In C. H. Dagli et al. (Eds.), St. Louis, Missouri, November 1鈥?, 2010.
  Henriksson, E., S枚derholm, P., & W氓rell, L. (2013). Industrial electricity demand and energy efficiency policy: the case of the Swedish mining industry. Energy Efficiency. doi:10.鈥?007/鈥媠12053-013-9233-7 .
  Hong, W. C. (2009). Electric load forecasting by support vector model. Applied Mathematical Modelling, 33, 2444鈥?454.MATH CrossRef
  Hsu, C. C., & Chen, C. Y. (2003). Regional load forecasting in Taiwan-applications of artificial neural networks. Energy Conversion and Management, 44, 1941鈥?949.CrossRef
  Kandananond, K. (2011). Forecasting electricity demand in Thailand with an artificial neural network approach. Energies, 4, 1246鈥?257.CrossRef
  Kaydani, H., Najafzadeh, M., & Mohebbi, A. (2014). Wellhead choke performance in oil well pipeline systems based on genetic programming. Journal of Pipeline Systems Engineering and Practice, 5(3), 06014001.CrossRef
  Kheirkhah, A., Azadeh, A., Saberi, M., Azaron, A., & Shakouri, H. (2013). Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis. Computers & Industrial Engineering, 64(1), 425鈥?41.CrossRef
  Koza, J. R. (1992). Genetic programming, on the programming of computers by means of natural selection. Cambridge: MIT Press.MATH
  Lee, D. G., Lee, B. W., & Chang, S. H. (1997). Genetic programming model for long-term forecasting of electric power demand. Electric Power Systems Research, 40, 17鈥?2.CrossRef
  Miranda, V., Srinivasan, D., & Proenca, L. M. (1998). Evolutionary computation in power systems. International Journal of Power & Energy Systems, 20(2), 89鈥?8.CrossRef
  Mitchell, T. (1997). Does machine learning really work? AI Magazine, 18(3), 11鈥?0.
  Moghrabi, C., & Eid, M. S. (1998). Modeling users through an expert system and a neural network. Computers & Industrial Engineering, 35(3鈥?), 583鈥?86.CrossRef
  Mohamed, N., Ahmad, M. H., Ismail, Z., & Suhartono. (2010). Double seasonal ARIMA model for forecasting load demand. Matematika, 26, 217鈥?31.MathSciNet
  Mostafavi, E. S., Mostafavi, S. I., Hosseinpour, F., & Jaafari, A. (2013a). A novel machine learning approach for the estimation of electricity demand. Energy Conversion and Management, 74, 548鈥?55.CrossRef
  Mostafavi, E. S., Saeedi, S., Sarvar, R., Izadi Moud, H., & Mousavi, S. M. (2013b). A hybrid computational approach to estimate solar global radiation: an empirical evidence from Iran. Energy, 49, 204鈥?10.CrossRef
  Najafzadeh, M., & Azamathulla, H. M. (2013a). Group method of data handling to predict scour depth around bridge piers. Neural Computing and Applications, 23(7鈥?), 2107鈥?112.CrossRef
  Najafzadeh, M., & Azamathulla, H. (2013a). Neuro-fuzzy GMDH to predict the scour pile groups due to waves. Journal of Computing in Civil Engineering. doi:10.鈥?061/鈥?ASCE)CP.鈥?943-5487.鈥?000376 , 04014068.
  Najafzadeh, M., & Lim, S. Y. (2014). Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Science Informatics 8(1), 187鈥?96.
  Najafzadeh, M., Barani, G., & Azamathulla, H. M. (2012). Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling. Neural Computing and Applications, 24(3鈥?), 629鈥?35.
  Najafzadeh, M., Barani, G., & Hessami-Kermani, M. (2013). Group method of data handling to predict scour depth around vertical piles under regular waves. Scientia Iranica, 20(3), 406鈥?13.
  Najafzadeh, M., Barani, G., & Hessami Kermani, M. (2014a). Estimation of pipeline scour due to waves by GMDH. Journal of Pipeline Systems Engineering and Practice, 5(3), 06014002.CrossRef
  Najafzadeh, M., Barani, G., & Hessami-Kermani, M. (2014b). Group method of data handling to predict scour at downstream of a ski-jump bucket spillway. Earth Science Informatics, 7(4), 231鈥?48.CrossRef
  Pino, R., de la Fuente, D., Priore, P., & Parre帽o, J. (2000). Short term forecasting of the electricity market of Spain using neural networks. In: The 20th International Symposium on Forecasting, ISF鈥?0, pp. 39-53, Lisboa, Portugal.
  Pino, R., Parreno, J., Gomez, A., & Priore, P. (2008). Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks. Engineering Applications of Artificial Intelligence, 21(1), 53鈥?2.CrossRef
  Pizzuti, S., Annunziato, M., & Moretti, F. (2013). Smart street lighting management. Energy Efficiency, 6(3), 607鈥?16.CrossRef
  Ringwood, J. V., Bofelli, D., & Murray, F. T. (2001). Forecasting electricity demand on short, medium and long time scales using neural networks. Journal of Intelligent and Robotic Systems, 31, 129鈥?47.MATH CrossRef
  Roy, P. P., & Roy, K. (2008). On some aspects of variable selection for partial least squares regression models. QSAR and Combinatorial Science, 27, 302鈥?13.CrossRef
  Searson, D. P. (2009). GPTIPS: genetic programming & symbolic regression for MATLAB. White paper. http://鈥媤ww.鈥媍s.鈥媌ham.鈥媋c.鈥媢k/鈥媬wbl/鈥媌iblio/鈥媍ache/鈥媓ttp_鈥媉鈥媉鈥媠ites.鈥媑oogle.鈥媍om_鈥媠ite_鈥媑ptips4matlab_鈥媐ile-cabinet_鈥婫PTIPSGuide1.鈥?.鈥媝df . Accessed 01 November 2013.
  Searson, D. P., Willis, M. J., & Montague, G. A. (2007). Co-evolution of nonlinear PLS model components. Journal of Chemometrics, 2, 592鈥?03.CrossRef
  Searson, D. P., Leahy, D. E., & Willis, M. J. (2010). GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. Proc Int Multi Conf Eng Comput Scie Hong Kong.
  Smith, G. N. (1986). Probability and statistics in civil engineering. London: Collins.
  Srinivasan, D. (2008). Energy demand prediction using GMDH networks. Neurocomputing, 72(1鈥?), 625鈥?29.CrossRef
  Suhartono, & Endharta, A. J. (2009). Short term electricity load demand forecasting in Indonesia by using double seasonal recurrent neural networks. International Journal of Mathematical Models and Methods in Applied Sciences, 3, 171鈥?78.
  Tay, J. C., & Ho, N. B. (2008). Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering, 54(3), 453鈥?73.CrossRef
  Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54, 799鈥?05.MATH CrossRef
  Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204, 139鈥?52.MATH CrossRef
  Tien Pao, H. (2007). Forecasting electricity market pricing using artificial neural networks. Energy Conversion and Management, 48, 907鈥?12.CrossRef
  Tsujimura, Y., Gen, M., & Ishizaki, S. (1997). Optimal routing in multiple IO data network using neural network with perturbed energy function. Computers & Industrial Engineering, 33(3鈥?), 477鈥?80.CrossRef
  Weise, T. (2009). Global optimization algorithms鈥攖heory and application [online]. Germany. Available from Internet: http://鈥媤ww.鈥媔t-weise.鈥媎e . Accessed 21 October 2013.
  Yang, X. S., Gandomi, A. H., Talatahari, S., & Alavi, A. H. (2012). Metaheuristics in water resources, geotechnical and transportation engineering. Waltham: Elsevier.
  
• 作者单位：Seyyed Mohammad Mousavi (1)
  Elham Sadat Mostafavi (2)
  Fariba Hosseinpour (3)
  
  1. Department of Geography and Urban Planning, Islamic Azad University, Science and Research Branch, Tehran, Iran
  2. Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran
  3. Faculty of Economic and Accounting, Islamic Azad University, Central Tehran Branch (IAUCTB), Tehran, Iran
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Environment
  Environmental Economics
  Energy Economics
  Renewable Energy Sources
  
• 出版者：Springer Netherlands
• ISSN：1570-6478

文摘

Multi-gene genetic programming (MGGP) is a new nonlinear system modeling approach that integrates the capabilities of standard genetic programming and classical regression. This paper deals with the application of this robust technique for the prediction of annual electricity demand in Thailand. The predictor variables included in the analysis were population, gross domestic product, stock index, and total revenue from exporting industrial products. Several statistical criteria were used to verify the validity of the model. A sensitivity analysis was performed to evaluate the contributions of the input features. The correlation coefficients between the measured and predicted electricity demand values are equal to 0.999 and 0.997 for the calibration and testing data sets, respectively. In addition to its high accuracy, MGGP outperforms regression and other powerful soft computing-based techniques. Keywords Electricity demand Multi-gene genetic programming Nonlinear system modeling