Evolving Systems

, Volume 8, Issue 4, pp 271–285 | Cite as

Calculating production by using short term demand forecasting models: a case study of fuel supply system

  • Amir Abbas Shojaie
  • Ali Dolatshahi Zand
  • Shahrzad Vafaie
Original Paper
  • 78 Downloads

Abstract

In today’s world, energy is one of the key elements of growth and economic development. Because of the critical role of energy in production costs and social security and environmental issues, it is very important to forecast and to optimize energy consumption. This study investigates the influential factors in urban energy (petrol) consumption. Artificial neural network was used to forecast fuel consumption in one of fuel stations in Tehran. A neural network was trained by Levenberg method and genetic algorithm. Results obtained by this method were compared with results obtained by neural network and regression. This comparison showed that neural network and training by genetic algorithm was more efficient than Levenberg and regression. All of the data used in this study were collected from fuel distribution system in Tehran.

Keywords

Fuel consumption Forecasting Neural network Genetic algorithm 

References

  1. Aghaian H (2013) Statistics of petroleum consumption products, 1392Google Scholar
  2. AL-Garni AZ, Zubair SM, Nizami JS (1994) A regression model for electric energy consumption forecasting in eastern Saudi Arabia. Energy 19(10):1043–1049CrossRefGoogle Scholar
  3. Al-Saba T, El-Amin I (1999) Artificial neural networks as applied to long-term demand forecasting. Artif Intell Eng 13(2):189–197CrossRefGoogle Scholar
  4. Anpalagan A, Venetsanopoulos B, Venkatesh AS, Khwaja M, Naeem A (2015) Improved short-term load forecasting using bagged neural. Electric Power Syst Res 125:109–115CrossRefGoogle Scholar
  5. Becker-Reshef I, Vermote E, Lindeman M, Justice C (2010) A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine Using MODIS data. Remote Sens Environ 114(6):1312–1323CrossRefGoogle Scholar
  6. Ebadi Jalal M, Hosseini M, Karlsson S (2016) Forecasting incoming call volumes in call centers with recurrent neural networks. J Bus Res 69(11):4811–4814CrossRefGoogle Scholar
  7. Ediger VS, Akar S (2007) ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35(3):1701–1708CrossRefGoogle Scholar
  8. Gonzalez-Romera E et al (2006) Monthly Electric Energy Demand Forecasting Based on Trend Extraction. Power Syst IEEE Trans 21:1946–1953CrossRefGoogle Scholar
  9. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182Google Scholar
  10. Hippert HS et al (2001) Neural networks for short-term load forecasting: a review and evaluation. Power Syst IEEE Trans 16:44–55CrossRefGoogle Scholar
  11. Hu R, Wen S, Karlsson S, Zeng Z, Huang T (2017) A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221:24–31CrossRefGoogle Scholar
  12. Jammazi R, Aloui C (2012) Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Econ 34(3):828–841CrossRefGoogle Scholar
  13. Kermanshahi B (1998) Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities. Neuro comput 23(1–3):125–133Google Scholar
  14. Khwaja AS, Zhang X, Anpalagan A, Venkatesh B (2017) Boosted neural networks for improved short-term electric load forecasting. Electric Power Syst Res 143:431–437CrossRefGoogle Scholar
  15. Lee KY, Choi TI, Ku CC, Park JH (1993) Short-term load forecasting Using diagonal recurrent neural network. In Neural Networks to Power Systems, 1993. ANNPS’93, Proceedings of the Second International Forum on Applications Of. pp 227–232Google Scholar
  16. Lolli F, Gamberini R, Regattierib A, BaluganiaE, Gatosb T, Guccib S (2017) Single-hidden layer neural networks for forecasting intermittent demand. Int J Product Econo Part A 183:116–128CrossRefGoogle Scholar
  17. Lopez M, Valero S, Senabre C, Aparicio J, Gabaldon A (2012) Application of SOM neural networks to short-term load forecasting: the Spanish electricity market case study. Electric Power Syst Res 91:18–27CrossRefGoogle Scholar
  18. Maidment DR, Miaou SP, Crawford MM (1985) Transfer function models of daily urban water use. Water Resour Res 21(4):425–432CrossRefGoogle Scholar
  19. Nelles O (2000) Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer, BerlinMATHGoogle Scholar
  20. Papalexopoulos AD, Hesterberg TC (1990) A regression-based approach to short term system load forecasting. Power Syst IEEE Trans 5(4):1535–1547CrossRefGoogle Scholar
  21. Siddique N, Adeli H (2013) Computational intelligence. Wiley, New JerseyCrossRefGoogle Scholar
  22. Szoplik J (2015) Forecasting of natural gas consumption with artificial neural networks. Energy 85:208–220CrossRefGoogle Scholar
  23. Wu J-D, Liu J-C (2012) A forecasting system for car fuel consumption using a radial basis function neural network. Expert Syst Appl 39(2):1883–1888CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Amir Abbas Shojaie
    • 1
  • Ali Dolatshahi Zand
    • 1
  • Shahrzad Vafaie
    • 2
  1. 1.School of Industrial Engineering, South Tehran BranchIslamic Azad UniversityTehranIran
  2. 2.School of Industrial Engineering, North Tehran BranchIslamic Azad UniversityTehranIran

Personalised recommendations