10.22099/ijes.2013.2033
چکیده
پیش‌بینی قیمت و مصرف انرژی در امر تصمیم‌گیری و برنامه‌ریزی مؤثر و کارآمد برای مدیران، نقش اساسی دارد. با وجود اینکه روش‌های آماری و ریاضی بسیاری برای پیش‌بینی وجود دارد، استفاده از الگوریتم‌های هوشمند با ویژگی‌های مطلوب در سال‌های اخیر پیشرفت قابل ملاحظه‌ای داشته است. هدف اصلی در این تحقیق پیش‌بینی قیمت و مصرف انرژی در بخش صنعت ایران با استفاده از شبکه عصبی مصنوعی و سیستم استنتاج تطبیقی عصبی فازی است. داده‌ها شامل قیمت و مصرف گازوئیل، بنزین و گاز مایع در بازه زمانی فروردین ماه 1375 تا فروردین ماه 1389 است، که بر اساس آنها مصرف و قیمت انرژی برای سال‌های 1390 و1391 پیش‌بینی شده است. نتایج مطالعه نشان می‌دهد درحالی‌که هر دو روش ذکر شده در مدل‌سازی و پیش‌بینی داده‌ها موفق‌اند، ولی با توجه به شاخص میانگین مجذور خطا، روش سیستم استنتاج تطبیقی عصبی فازی در اکثر موارد با دقت بالاتری نسبت به شبکه عصبی، داده‌ها را پیش‌بینی کرده است
کلیدواژه‌ها
Aris, Z. & Mohamad, D. (2008). Application of artificial neural networks using Hijri Lunar transaction as extracted variables to predict stock trend direction. Labuan e-Journal of Muamalat and Society, 2, 9-16.
Azar, Z. & Afsar, A. (2006). A modeling of stock price forecasting using neuro- fuzzy System (In Persian). Iranian Journal of Trade Studies, 40, 33-52.
Abdulshahed, A., Longstaff, A. P., Fletcher, S. & Myers, A. (2013). Comparative study of ANN and ANFIS prediction models for thermal error compensation on CNC machine tools. Presented at 10th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM & Robotic Performance, UK.
Ahmari Nejad, A., Rajabi, H. & Sadeh, J. (2005). The important of load forecasting for electricity price forecasting with dividing of electricity price to separating different components of electrical energy in a competitive environment. Presented at 20th International Power System Conference, 98-f-SEA-366.
Baumgartner, T. & Midttun, A. (1987). The politics of energy forecasting: A comparative study of energy forecasting in Western Europe and North America. New York: Oxford University Press.
Bilgehan, M. (2011). Comparison of ANFIS and NN models-with a study in critical Buckling Load Estimation. Applied Soft Computing Journal, 11, 3779-3791.
Catalão, J. P. S., Mariano, S. J. P. S., Mendes, V. M. F. & Ferreira, L. A. F. M. (2007). An artificial neural network approach for short-term electricity prices forecasting. Engineering Intelligent Systems for Electrical Engineering & Communications, 15, 15-23.
Esmaeili, M., Osanloo, M., Rashidinejad, F., Aghajani, A. & Taji, M. (2012). Multiple regressions, ANN and ANFIS models for prediction of back-break in the open pit blasting, Engineering with computers. Berlin: Springer.
Farjamnia, I., Naseri, M. & Ahmadi, M. (2007). Oil price forecasting using ARIMA model and artificial neural networks (In Persian). Iranian Journal of Economic Research, 32, 161-183.
Fausett, L. (1994). Fundamentals of Neural networks. Architectures, algorithms, and applications. New Jersey: Prentice-Hall
Haidar I., Kulkarni S. & Pan H. (2008). Forecasting model for crude oil prices based on artificial neural networks. Intelligent Sensors, Sensor Networks and Information Processing, IEEE International Conference, Sydney, Australia.
Hippert, H. S., Pedreira, C. E. & Souza, R. C. (2001). Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems, 16, 44-55.
Kamruzzaman, J., & Sarker, R.A. (2003). Comparing ANN Based Models with ARIMA for Prediction of Forex Rates, The Australian Society for Operation Research Incorporated (ASOR) Bulletin, 22, 2-11.
Kartalopoulos, S. V. (1996). Understanding neural networks and fuzzy logic: Basic concepts and applications. New York: IEEE Press.
Latif, H. H., Zahin, S., Paul S. K. & Azeem, A. (2013). A comparative study of power demand forecasting between ANFIS, neural networks and traditional methods. International Journal of Business Information Systems, 13, 359-380.
Mandal, P., Senjyu, T., Urasaki, N., Funabashi, T. & Srivastava, A. K. (2007). A novel approach to forecast electricity price for PJM using neural network and similar days method. IEEE Transactions on Power Systems, 22, 2058-2065.
Mankiw, N. G. (2012). Principles of macroeconomics. 6th edition. Mason, OH: South-Western, Cengage Learning.
Mirbagheri, M. N. (2010). Fuzzy-logic and neural network fuzzy forecasting of Iran GDP growth. African Journal of Business Management, 4, 925-929.
Moshiri, S. & Foroutan, F. (2005). Turbulence test and predict future prices of crude oil (In Persian). Iranian Journal of Economic Studies, 21, 67-90.
Nauck, D., Klawonn, F. & Kruse, R. (1997). Foundation of neuro-fuzzy systems. New York: John Wiley & Sons Co.
Picton P. (2000). Neural networks. 2nd edition. New York: Palgrave Publisher.
Sadeghi, H., Zolfaghari, M. & Elhami Nejad, M. (2011). Performance comparison of neural networks and ARIMA models in the modeling and forecasting of short-term price of OPEC basket of crude oil. (In Persian) Energy Economics Studies Journal, 28, 25-47.
Sandberg, I. W., Lo, J. T., Fancourt, C. L., Principe, L.C., Katagiri, S. & Haykin, S. (2001). Nonlinear dynamical system: Feed forward neural network perspectives. New York: Wiley-Inter Science Publication.
Sarfraz, L. & Afsar A. (2005). The examination of effective factors of gold price and forecasting using neuro-fuzzy system. (In Persian), Quarterly Publication of the Economic Researches, 16, 149-165.
Shing, J. & Jang, R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transaction on System, Man and Cybernetics 23, 665-684.
Sinaie, H. A., Mortazavi, S. & Teymuri Y. (2005). Tehran stock index forecasting using neural networks. (In Persian) Iranian Accounting and Auditing Review Journal, 41, 59-83.
Vinod, N. M., Saxena, P. & Pardasani, K. R. (2010). A comparison between hybrid approaches of ANN and ARIMA for Indian stock trend forecasting. Business Intelligence Journal, 3, 23-42.
Wheelwright, S. C. & Makridakis, S. (1985). Forecasting methods for management. 4th edition, New York: John Wiley & Sons Co.
Zara Nejad, M. & Hamid, S. (2009). Forecasting of Iran inflation rate using dynamic artificial neural networks. (In Persian), Quarterly Journal of Economic Review, 1, 145-167.
Zhang G., Patuwo B. E. & Hu, M.Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14, 35-62
)