REGIONAL DEMAND FORECASTING STUDY FOR TRANSPORTATION FUELS IN TURKEY

Transcription

1 REGIONAL DEMAND FORECASTING STUDY FOR TRANSPORTATION FUELS IN TURKEY Özlem Atalay Prof. Gürkan Kumbaroğlu

2 INTRODUCTION The prediction of fuel consumption has been an important tool for energy planning, with the following purposes which cited as to (i) develop right pricing and taxation systems, (ii) help decide future investments on fuels, (iii) aid address emission and pollution issues, and (iv) plan of future energy needs, identify national infrastructure and research and development requirements.

3 INTRODUCTION In general, established models can be handled in six main categories. Multi Linear Regression Models (region-based and yearly) Moving Average (region-based and yearly) Double Moving Average (region-based and yearly) Simple Exponential Smoothing (region-based and yearly) Double Exponential Smoothing (region-based and yearly) Time series models Holt Winters model (region-based and quarterly)

4 Regions and Fuel Distribution According to Regions for the year 2013 Figure. Regions and Fuel Distribution According to Regions for the year 2013

5 THE TRANSPORT SECTOR IN TURKEY Figure. Road Transportation Fuel Demand Figure. The Percentage Distribution of Vehicles According to Fuel Type in Turkey and Istanbul

6 INDEPENDENT VARIABLES Population Gross Value Added Table. Forecasts and Accuracy Results of Istanbul for MA=2 and MA=3 Forecast 2013 MSE MAD MAPE MA=2 177,921,777 TL 182,197,788,884,410 11,606, % MA=3 171,083,829 TL 282,149,287,971,249 15,117, % Figure. MA=2 Results of GVA for Istanbul

7 INDEPENDENT VARIABLES Selling Price Table. Forecasts and Accuracy Results of Selling Prices for Isatnbul Forecast 2013 MSE MAD MAPE Gasoline MA = TL % MA = TL % Diesel MA = TL % MA = TL % LPG Regression TL % MA = TL % Figure. MA=2 Result of Gasoline Selling Price for Istanbul

8 INDEPENDENT VARIABLES Figure. MA=2 Result of Diesel Selling Price for Istanbul Figure. Regression Analysis of LPG Selling Price for Istanbul

9 INDEPENDENT VARIABLES Car Park CP (it) =b 0 + b 1 * GVA (t) / P (t) + b 2 * CP (i, t-1) - i is the fuel type index, - t is the year index, - CP (it) is the car park of fuel type i in time t, - GVA (t) is the Gross Value Added in time t, - P (t) is the population in time t, - CP (i, t-1) is the car park of fuel type i in time t-1, - b0 is the fixed sales value, - b1 is the increment of the rate of the GVA and Population which causes the car park, - b2 is the increment of the car park from previous year which causes the car park.

10 INDEPENDENT VARIABLES Table. Car Park(Gasoline) Car Park (Gasoline) Istanbul = * GVA/Population * The car park from previous year(gasoline) Figure. Regression Results of Car Park for Istanbul

11 Regression Analysis Table. Regression Analysis of Gasoline

12 Regression Analysis

13 Regression Analysis Figure. Regression Results of the Amount of Yearly Gasoline Sales for Istanbul

14 COMPARISON OF THE FUEL DEMAND MSE MAD MAPE 9.54E+09 73, % Figure. Comparison of the Amount of Yearly Gasoline Sales for Istanbul (MA=2) MSE MAD MAPE 15,195,631, ,514 20% Figure. Comparison of the Amount of Yearly Gasoline Sales for Istanbul (MA=3)

15 COMPARISON OF THE FUEL DEMAND MSE MAD MAPE 16,696,051, ,953 24% Figure. Comparison of the Amount of Yearly Gasoline Sales for Istanbul (DMA=2) MSE MAD MAPE 1,220,000,000 31, % Figure. Comparison of the Amount of Yearly Gasoline Sales for Istanbul (Regression)

16 COMPARISON OF THE FUEL DEMAND MSE MAD MAPE 7,915,307,493 71, % Figure. Comparison of the Amount of Yearly Gasoline Sales for Istanbul (SES) MSE MAD MAPE 6,567,972,636 67, % Figure. Comparison of the Amount of Yearly Gasoline Sales for Istanbul (DES)

17 TRIPLE EXPONENTIAL SMOOTHING Figure. Winter s Method for The Amount of Yearly Gasoline Sales (Quarterly) Table. Accuracy of Winter s Method for Gasoline Sales MSE MAD MAPE Q E % Q Α β γ Q Q

18 CONCLUSION Table. Best Performing Methods and Percent Change Projections for Fuel Demand Regions The Best Performing Method Regions The Best Performing Method Istanbul West Marmara Aegean East Marmara Regression (G) -26% -29% Regression (G) -4% -6% MA=3 (D) -7% -2% Central Anatolia Regression (D) +7% +5% Regression (LPG) +1% +1% Regression (LPG) +9% +9% Regression (G) +2% -6% Regression (G) +1% -1% Regression (D) +6% +8% West Black Sea Regression (D) +2% -1% Regression (LPG) +10% +10% DES (LPG) +10% +18% Regression (G) +0,37% -3% Regression (G) -0,95% -3% Regression (D) +3% +5% East Black Sea Regression (D) +5% +12% DES (LPG) +10% +19% DES (LPG) +10% +18% Regression (G) -3% -6% Regression (G) -4% -8% DMA=2 (D) +8% +14% Northeast Anatolia Regression (D) +4% +3% Regression (LPG) +2% +1% Regression (LPG) -14% -10% West Anatolia Mediterranean Regression (G) -6% -9% MA=3 (G) +0,24% -0,48% Regression (D) +4% +4% Central East Anatolia Regression (D) +5% +10% DES (LPG) +8% +15% Regression (LPG) +10% +19% SES (G) -11% -13% SES (G) +10% +11% Regression (D) -8% +7% South East Anatolia Regression (D) +11% +9% DES (LPG) +11% +20% Regression (LPG) +7% +9%