Abstract:
This paper presents a neuro-based approach for annual gasoline demand forecast in Iran by taking into account several socio-economic indicators. To analyze the influence of economic and social indicators on the gasoline demand, gross domestic product (GDP), population and the total number of vehicles are selected. This approach is structured as a hierarchical artificial neural network (ANN) based on supervised multi-layer perceptron (MLP), trained with back-propagation (BP) algorithm. This hierarchical ANN is designed properly. The input variables are GDP, population, total number of vehicles and the gasoline demand in the last one year. The output variable is the gasoline demand. The paper proposes a hierarchical network by which the inputs to the ending level are obtained as outputs of the starting levels. Actual Iranian data between 1967 and 2008 were used to test the hierarchical ANN hence; it illustrated the capability of the approach. Comparison of the model predictions with validation data shows validity of the model. Furthermore, the demand for the period between 2011 and 2030 is estimated. It is noticeable that if there will not be any price shock or efficiency improvement in the transportation sector, the gasoline consumption may achieve a threatening level of about 54 billion liters by 2030 in Iran.
این مقاله با استفاده از شبکههای عصبی و با در نظرگرفتن شاخصهای اقتصادی و اجتماعی» تقاضای
بنزین در ایران را پیشبینی کرده است. برای بررسی تأثیر شاخصهای اقتصادی و اجتماعی بر تقاضای
بنزین» تولید ناخالص ملی؛ جمعیت و تعداد ودرو موزاد توجه قرار گرفتهاند. با استفاده از شبېکههای
عصبی سلسلهمراتبی پرسپترون چندلایه که پا الگوریتم پینانتشارخطا آموزش داده شدهانک پیشبینی
انجام شده است. متغیرهای ورودی» تولید ناخجالصن مل تعداد خودرو و تقاضای بنزین در
سال قبل و متغیر خروجی تقاضای بنزین میباشد. این مقالهیک شبکه سلسله مراتبی را پیشنهاد داده
است که ورودیهای لایه آخر خروجیهای لایههائ" اولیه هستند. دادههای سالهای ۱۹6۷ تا ۲۰۰۸
برای آموزش شبکه عصبی سلیسله فیراتبی' استفاده شکه.:مقایسیةمقادیر پیشبینی مدل با دادههای اعتبار
اعتبار مدل را نشان میدهد. علاوه بر این تقاضای زین ۲۰۱۱ تا ٢030 نیز پیش بینی
شده است. قاپل ذگر است در صورت عدم اتخاڈ سیاست قیمتی مناسب و بهبود بخش حال و تقسل»
مصرف بنزین به سطح بحرانی 54 میلیارد لیتر در سال 2030 خواهد رسید.
Machine summary:
This approach is structured as a hierarchical artificial neural network (ANN) based on supervised multi-layer perceptron (MLP), trained with back-propagation (BP) algorithm.
Murat and Ceylan developed yet another model based on artificial neural network to predict transportation of energy demand in Turkey (Murat & Ceylan, 2006: 3165-72).
Azadeh, Ghaderi and Sohrabkhani formulated a neural network model to predict the annual electricity consumption in high energy consuming industrial sectors in Iran (Azadeh et al.
Ekonomou developed a neural network model to predict Greek long-term energy consumption (Ekonomou, 2010:512-17).
The same year, Azadeh, Arab and Behfard came up with a model to forecast long-term gasoline demand in the US, Canada, Japan, Kuwait and Iran using artificial neural networks (Azadeh et al.
In this section gasoline demand in Iran from 2011 to 2030 is forecasted regarding socio-economic and transport related indicators using a hierarchical ANN model.
One way is to scale input and output variables ( zi ) in interval [Ai ] corresponding to the range of the transfer function (Basheer &Hajmeer, 2000:3-31): min Zi Z, ( population, GDP, the total number of vehicles and the gasoline demand.
98 Conclusion This paper focused on forecasting the annual gasoline demand regarding socio-economic and transport related indicators using a hierarchical artificial neural networks.
Population, GDP and the total number of vehicles were forecasted using ANNs. Actual data from 1967 to 2008 were used and the gasoline demand of Iran from 2011 to 2030 was I0 forecasted.
A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran .