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.
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References
Aghaian H (2013) Statistics of petroleum consumption products, 1392
AL-Garni AZ, Zubair SM, Nizami JS (1994) A regression model for electric energy consumption forecasting in eastern Saudi Arabia. Energy 19(10):1043–1049
Al-Saba T, El-Amin I (1999) Artificial neural networks as applied to long-term demand forecasting. Artif Intell Eng 13(2):189–197
Anpalagan A, Venetsanopoulos B, Venkatesh AS, Khwaja M, Naeem A (2015) Improved short-term load forecasting using bagged neural. Electric Power Syst Res 125:109–115
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):1312–1323
Ebadi Jalal M, Hosseini M, Karlsson S (2016) Forecasting incoming call volumes in call centers with recurrent neural networks. J Bus Res 69(11):4811–4814
Ediger VS, Akar S (2007) ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35(3):1701–1708
Gonzalez-Romera E et al (2006) Monthly Electric Energy Demand Forecasting Based on Trend Extraction. Power Syst IEEE Trans 21:1946–1953
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Hippert HS et al (2001) Neural networks for short-term load forecasting: a review and evaluation. Power Syst IEEE Trans 16:44–55
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 221:24–31
Jammazi R, Aloui C (2012) Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Econ 34(3):828–841
Kermanshahi B (1998) Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities. Neuro comput 23(1–3):125–133
Khwaja AS, Zhang X, Anpalagan A, Venkatesh B (2017) Boosted neural networks for improved short-term electric load forecasting. Electric Power Syst Res 143:431–437
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 227–232
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 183:116–128
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 91:18–27
Maidment DR, Miaou SP, Crawford MM (1985) Transfer function models of daily urban water use. Water Resour Res 21(4):425–432
Nelles O (2000) Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer, Berlin
Papalexopoulos AD, Hesterberg TC (1990) A regression-based approach to short term system load forecasting. Power Syst IEEE Trans 5(4):1535–1547
Siddique N, Adeli H (2013) Computational intelligence. Wiley, New Jersey
Szoplik J (2015) Forecasting of natural gas consumption with artificial neural networks. Energy 85:208–220
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):1883–1888
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Shojaie, A.A., Dolatshahi Zand, A. & Vafaie, S. Calculating production by using short term demand forecasting models: a case study of fuel supply system. Evolving Systems 8, 271–285 (2017). https://doi.org/10.1007/s12530-016-9173-5
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DOI: https://doi.org/10.1007/s12530-016-9173-5