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Submitted:
06 October 2023
Posted:
09 October 2023
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ML Algorithm | MAE | MSE | RMSE | R2 | MAPE |
---|---|---|---|---|---|
Extra Trees Regressor | 2296.86 | 8756864.57 | 2932.96 | 0.9788 | 0.0464 |
Random Forest Regressor | 3186.05 | 14777499.11 | 3817.37 | 0.9684 | 0.0658 |
Ridge Regression | 3676.12 | 21641675.00 | 4466.14 | 0.9655 | 0.0736 |
Linear Regression | 3780.00 | 23739669.80 | 4668.86 | 0.9635 | 0.0825 |
Lasso Regression | 3779.85 | 23736214.80 | 4668.54 | 0.9635 | 0.0825 |
Least Angle Regression | 3780.00 | 23739655.90 | 4668.86 | 0.9635 | 0.0825 |
Lasso Least Angle Regression | 3779.85 | 23736205.30 | 4668.54 | 0.9635 | 0.0825 |
Orthogonal Matching Pursuit | 3780.00 | 23739655.90 | 4668.86 | 0.9635 | 0.0825 |
Huber Regressor | 3828.58 | 22823167.86 | 4595.40 | 0.9634 | 0.0785 |
AdaBoost Regressor | 3575.50 | 15583915.43 | 3934.88 | 0.9611 | 0.0691 |
Gradient Boosting Regressor | 3772.78 | 16872570.61 | 4096.12 | 0.9556 | 0.0707 |
Decision Tree Regressor | 3768.74 | 16856622.84 | 4094.38 | 0.9554 | 0.0707 |
Extreme Gradient Boosting | 3768.71 | 16856282.40 | 4094.34 | 0.9554 | 0.0706 |
K Neighbors Regressor | 3987.62 | 29417635.00 | 5274.57 | 0.9493 | 0.0848 |
Elastic Net | 7402.15 | 81150325.20 | 8721.88 | 0.8666 | 0.1248 |
Bayesian Ridge | 22303.79 | 705872003.20 | 25684.29 | -0.0970 | 0.4306 |
Light Gradient Boosting Machine | 22303.79 | 705872041.79 | 25684.29 | -0.0970 | 0.4306 |
Dummy Regressor | 22303.79 | 705872041.60 | 25684.29 | -0.0970 | 0.4306 |
Passive Aggressive Regressor | 40863.75 | 2361836689.9 | 48178.33 | -3.9041 | 0.5635 |
ML Algorithm | MAE | MSE | RMSE | R2 | MAPE |
---|---|---|---|---|---|
Extra Trees Regressor | 2989.27 | 17145375.48 | 4140.6975 | 0.9811 | 0.0406 |
Author(s) | Year | Method used | Dataset | Input Parameters | Forecasting for |
---|---|---|---|---|---|
Aslan [48] | 2023 | Archimedes Optimization Algorithm | 1979-2005 1979-2011 | GDP, Population, Import, Export |
Energy |
Korkmaz [49] | 2022 | Bezier Search Differential Evolution Black Widow Optimization (BWO) |
2000-2017 | Passenger-km, Freight-km, Carbon dioxide emissions, GDP, Infrastructure Investment |
Transportation Energy |
Aslan and Beşkirli [50] | 2022 | Improved Arithmetic Optimization Algorithm | 1979-2011 | GDP, Population, Import, Export |
Energy |
Ağbulut [51] | 2022 | Deep Learning (DL) Support Vector Machine (SVM) Artificial Neural Network (ANN) |
1970-2016 | GDP, Population, Vehicle-km, Year |
Transportation Energy |
Özdemir et al. [52] | 2022 | Modified Artificial Bee Colony Algorithm | 1979-2005 | GDP, Population Import, Export |
Energy |
Özkış [53] | 2020 | Vortex Search Algorithm (VS) | 1979-20051979-2011 | GDP, Population, Import, Export |
Energy |
Tefek et. al. [54] | 2019 | Hybrid gravitational search, teaching, learning-based optimization method | 1980-2014 | Population, GDP, Installed power, Gross Generation, Net Consumption |
Energy |
Beskirli et al. [55] | 2018 | Artificial Algae Algorithm (AAA) | 1979-2005 | GDP, Population, Import, Export |
Energy |
Cayir Ervural and Ervural [56] | 2018 | Grey Prediction Model Based on GA Grey Prediction Model Based on PSO |
1996-2016 | Previous Annual Electricity Consumption Data | Energy |
Koç et al. [57] | 2018 | Gravity Search Algorithm (GSA), Invasive Weed Optimization Algorithm (IWO) | 1979-2011 | GDP, Population, Import, Export |
Energy |
Öztürk and Öztürk [58] | 2018 | ARIMA | 1970-2015 | Previous Energy Consumption Data | Energy |
Beskirli et al. [59] | 2017 | Differential Evolution Algorithm (DE) | 1979-2011 | GDP, Population, Import, Export |
Energy |
Daş [60] | 2017 | Neural Network Based on Particle Swarm Optimization | 1979-2005 | GDP, Population, Import, Export |
Energy |
Kankal and Uzlu [61] | 2017 | ANN | 1980-2012 | GDP, Population, Import, Export |
Electricity Energy |
Uguz et al. [62] | 2015 | Artificial Bee Colony with Variable Search Strategies (ABCVSS) | 1979-2005 | GDP, Population, Import, Export |
Energy |
Tutun et al. [63] | 2015 | Regression and ANN | 1975-2010 | Imports, Exports, Gross generation, Transmitted energy |
Electricity Energy consumption |
Kıran et al. [64] | 2012 | Hybrid Meta-Heuristic (Particle Swarm Optimization, Ant Colony Optimization) | 1979-2005 | GDP, Population, Import, Export |
Energy |
Kankal et al. [65] | 2011 | Regression Analysis/ ANN | 1980-2007 | GDP, Population, İmport, Export, Employment |
Energy |
Ediger and Akar [66] | 2007 | Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) | 1950-2005 | Energy | |
Ünler [17] | 2008 | Particle Swarm Optimization | 1979-2005 | GDP, Population, Import, Export |
Energy |
Toksarı [67] | 2007 | Ant Colony Optimization | 1970-2005 | Population, GDP, Import, Export |
Energy |
Sözen et al. [68] | 2005 | ANN | 1975-2003 | Population, Gross Generation, Installed Capacity, Import, Export |
Energy |
Canyurt et al. [69] | 2004 | Genetic Algorithm | 1970-2001 | GDP, Population Import, Export |
Energy |
Ceylan and Öztürk [70] | 2004 | Genetic Algorithm | 1970-2001 | GDP, Population, Import, Export |
Energy |
Ceylan et al. [71] | 2004 | Genetic Algorithm | 1990-2001 | GDP, Population, Import, Export |
Energy and exergy production and consumption |
Variable | The influencing factors for using this variable |
---|---|
GDP | There exists a strong correlation between GDP and energy consumption, as the level of economic activity directly impacts the demand for energy. When the GDP of a country increases, it generally indicates a growth in industrial and commercial activities, leading to higher energy consumption. Considering the substantial impact of GDP on energy demand, GDP is often chosen as an independent variable in studies analyzing energy consumption patterns. |
Population | Population growth directly affects the demand for energy in a country or region. As the population increases, there is a greater need for energy to meet the demands of the growing population, including residential, commercial, industrial, and transportation sectors. Understanding and considering population values as an independent variable is crucial for analyzing and planning energy resources. |
Import | The relationship between imports and energy consumption is significant, as the availability and reliance on imported energy resources can directly impact a country's energy demand. The import values of energy resources are choosen as independent variables in this study due to their influence on the overall energy consumption patterns. |
Export | The relationship between exports and energy consumption is an important aspect of understanding a country's energy demand. The export values of energy resources are choosen as independent variables in this study due to their potential impact on a country's overall energy consumption patterns. |
Years | Population (106) | GDP ($ 109) |
Import ($ 109) |
Export ($ 109) |
Energy (Mtoe) |
---|---|---|---|---|---|
1979 | 43.19 | 82.00 | 5.07 | 2.26 | 26.37 |
1980 | 44.09 | 68.82 | 7.91 | 2.91 | 27.51 |
1981 | 44.98 | 71.04 | 8.93 | 4.70 | 27.60 |
1982 | 45.95 | 64.55 | 8.84 | 5.75 | 29.59 |
1983 | 47.03 | 61.68 | 9.24 | 5.73 | 30.25 |
1984 | 48.11 | 59.99 | 10.76 | 7.13 | 31.75 |
1985 | 49.18 | 67.23 | 11.34 | 7.96 | 32.73 |
1986 | 50.22 | 75.73 | 11.10 | 7.46 | 34.59 |
1987 | 51.25 | 87.17 | 14.16 | 10.20 | 38.70 |
1988 | 52.28 | 90.85 | 14.34 | 11.66 | 39.73 |
1989 | 53.31 | 107.14 | 15.80 | 11.62 | 40.40 |
1990 | 54.32 | 150.68 | 22.30 | 12.96 | 42.24 |
1991 | 55.32 | 150.03 | 21.05 | 13.59 | 43.09 |
1992 | 56.30 | 158.46 | 22.87 | 14.71 | 44.70 |
1993 | 57.30 | 180.17 | 29.43 | 15.35 | 48.26 |
1994 | 58.31 | 130.69 | 23.27 | 18.11 | 45.77 |
1995 | 59.31 | 169.49 | 35.71 | 21.64 | 50.53 |
1996 | 60.29 | 181.48 | 43.63 | 23.22 | 54.85 |
1997 | 61.28 | 189.83 | 48.56 | 26.26 | 57.99 |
1998 | 62.24 | 275.97 | 45.92 | 26.97 | 57.12 |
1999 | 63.19 | 256.39 | 40.67 | 26.59 | 55.22 |
2000 | 64.11 | 274.30 | 54.50 | 27.77 | 61.60 |
2001 | 65.07 | 201.75 | 41.40 | 31.33 | 55.60 |
2002 | 65.99 | 240.25 | 51.55 | 36.06 | 59.49 |
2003 | 66.87 | 314.59 | 69.34 | 47.25 | 64.59 |
2004 | 67.79 | 408.88 | 97.54 | 63.17 | 68.24 |
2005 | 68.70 | 506.31 | 116.77 | 73.48 | 70.33 |
2006 | 69.60 | 557.06 | 139.58 | 85.53 | 74.82 |
2007 | 70.47 | 681.34 | 170.06 | 107.27 | 79.79 |
2008 | 71.32 | 770.46 | 201.96 | 132.03 | 77.76 |
2009 | 72.23 | 649.27 | 140.93 | 102.14 | 78.36 |
2010 | 73.20 | 776.99 | 185.54 | 113.88 | 79.84 |
2011 | 74.17 | 838.76 | 240.84 | 134.91 | 84.91 |
2012 | 75.28 | 880.56 | 236.55 | 152.46 | 88.84 |
2013 | 76.58 | 957.78 | 260.82 | 161.48 | 88.07 |
2014 | 78.11 | 938.95 | 251.14 | 166.50 | 89.25 |
2015 | 79.65 | 864.32 | 213.62 | 150.98 | 99.47 |
2016 | 81.02 | 869.69 | 202.19 | 149.25 | 104.57 |
2017 | 82.09 | 859.00 | 238.72 | 164.50 | 111.65 |
2018 | 82.81 | 778.47 | 231.15 | 177.17 | 109.44 |
2019 | 83.48 | 759.94 | 210.35 | 180.83 | 110.65 |
2020 | 84.14 | 720.30 | 219.52 | 169.64 | 113.70 |
2021 | 84.78 | 819.04 | 271.42 | 225.29 | 123.86 |
Model | Input |
M1 | GDP, Population |
M2 | GDP, Import |
M3 | GDP, Export |
M4 | Population, Import |
M5 | Population, Export |
M6 | Import, Export |
M7 | GDP, Population, Import |
M8 | GDP, Population, Export |
M9 | Population, Import, Export |
M10 | GDP, Import, Export |
M11 | *GDP, Population, Import, Export |
Models | ML Algorithm | MAE | MSE | RMSE | R2 | MAPE |
---|---|---|---|---|---|---|
M1 | Extra Trees Regressor | 2743.27 | 14435527.00 | 3622.18 | 0.9751 | 0.0546 |
Huber Regressor | 3419.46 | 20734897.00 | 4395.82 | 0.9642 | 0.0749 | |
Extreme Gradient Boosting | 3725.91 | 22807495.50 | 4551.60 | 0.9625 | 0.0655 | |
M2 | K Neighbors Regressor | 6881.86 | 84149616.80 | 8596.44 | 0.8710 | 0.1104 |
Random Forest Regressor | 6809.08 | 114145888.80 | 9963.98 | 0.8252 | 0.1167 | |
Extra Trees Regressor | 6452.43 | 123658755.00 | 9929.56 | 0.8117 | 0.1178 | |
M3 | Extra Trees Regressor | 3977.99 | 44054367.30 | 5730.78 | 0.9299 | 0.0695 |
Random Forest Regressor | 4631.80 | 55354193.10 | 6583.28 | 0.9162 | 0.0797 | |
Gradient Boosting Regressor | 5351.58 | 64199913.90 | 7220.71 | 0.9031 | 0.0901 | |
M4 | Extra Trees Regressor | 3042.48 | 17937995.55 | 3844.90 | 0.9733 | 0.0591 |
Random Forest Regressor | 3666.07 | 22957290.18 | 4448.29 | 0.9716 | 0.0685 | |
Gradient Boosting Regressor | 4156.41 | 26308872.75 | 4930.11 | 0.9652 | 0.0742 | |
M5 | Huber Regressor | 3864.21 | 36775418.87 | 5527.85 | 0.9541 | 0.0601 |
Lasso Regression | 4003.55 | 36200997.00 | 5456.62 | 0.9530 | 0.0678 | |
Least Angle Regression | 4003.72 | 36196280.60 | 5456.35 | 0.9530 | 0.0678 | |
M6 | K Neighbors Regressor | 5707.22 | 80792845.60 | 7992.88 | 0.8962 | 0.1035 |
Random Forest Regressor | 5455.21 | 77021388.80 | 8193.73 | 0.8644 | 0.0930 | |
Extra Trees Regressor | 5511.29 | 82472925.80 | 8300.80 | 0.8540 | 0.0950 | |
M7 | Extra Trees Regressor | 2308.94 | 12339053.90 | 3277.79 | 0.9754 | 0.0488 |
Random Forest Regressor | 2972.75 | 17408515.60 | 3854.32 | 0.9608 | 0.0576 | |
AdaBoost Regressor | 3400.27 | 18546026.70 | 4014.45 | 0.9538 | 0.0640 | |
M8 | Extra Trees Regressor | 3189.81 | 17110325.38 | 3874.79 | 0.9716 | 0.0537 |
AdaBoost Regressor | 4293.89 | 29715232.81 | 5282.08 | 0.9475 | 0.0728 | |
Random Forest Regressor | 4287.09 | 35730037.10 | 5185.02 | 0.9460 | 0.0700 | |
M9 | Extra Trees Regressor | 3018.13 | 30503239.40 | 4394.13 | 0.9477 | 0.0407 |
Random Forest Regressor | 3583.32 | 45575422.90 | 5273.91 | 0.9304 | 0.0473 | |
AdaBoost Regressor | 4069.59 | 37580683.81 | 5358.08 | 0.9285 | 0.0609 | |
M10 | K Neighbors Regressor | 5670.51 | 74121506.00 | 7652.91 | 0.9017 | 0.1003 |
Random Forest Regressor | 5372.22 | 75187291.90 | 7930.92 | 0.8896 | 0.0942 | |
Ridge Regression | 7009.35 | 69191604.80 | 8277.45 | 0.8621 | 0.1643 | |
M11 | Extra Trees Regressor | 2296.86 | 8756864.57 | 2932.96 | 0.9788 | 0.0464 |
Random Forest Regressor | 3186.05 | 14777499.11 | 3817.37 | 0.9684 | 0.0658 | |
Ridge Regression | 3676.12 | 21641675.00 | 4466.13 | 0.9655 | 0.0736 |
Sample Size | Population | GDP | Import | Export | Energy | Prediction |
---|---|---|---|---|---|---|
1 | 65988664 | 2.40E+11 | 51553796 | 36059088 | 59486 | 62736.99 |
2 | 70468872 | 6.81E+11 | 170062720 | 107271752 | 79785 | 76948.98 |
3 | 44089068 | 6.88E+10 | 7909364 | 2910122 | 27507.96 | 26962.77 |
4 | 76576120 | 9.58E+11 | 260822800 | 161480912 | 88074.43 | 91513.96 |
5 | 78112072 | 9.39E+11 | 251142432 | 166504864 | 89248.78 | 96054.71 |
6 | 47026424 | 6.17E+10 | 9235002 | 5727834 | 30249.94 | 28972.36 |
7 | 52275888 | 9.09E+10 | 14335398 | 11662024 | 39733.94 | 38689.99 |
8 | 84775408 | 8.19E+11 | 271424480 | 225291392 | 123859.48 | 113726.34 |
9 | 48106764 | 6.00E+10 | 10757032 | 7133604 | 31747.45 | 30416.86 |
10 | 73195344 | 7.77E+11 | 185544336 | 113883216 | 79840.07 | 81925.22 |
11 | 53305232 | 1.07E+11 | 15792143 | 11624692 | 40395.43 | 40529.37 |
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