Research Article
A Curve Fitting Modelling Approach to Forecast Long-Term Electrical Energy Consumption: Case Study of Turkey
Abstract
For Turkey to achieve the targets of Vision 2023 of being in the top ten economies of the world, the eleventh National Development Plan (NDP11) focuses on ensuring uninterrupted, high-quality, sustainable, reliable and affordable energy supply. In this regard medium- and long-term energy supply-demand planning is regarded as a key input to the planning process. Medium and long-term planning is possible only when reliable forecasts are available. Using Turkey’s electrical energy consumption data from 1970 to 2015, this study presents novel Gaussian, Fourier and Exponential curve fitting and extrapolation approaches to forecast Turkey’s electrical energy consumption up to the year 2025. Major interest is put on how the model forecasts electrical energy consumption for year 2023 because this year marks a century of the establishment of the Republic of Turkey and all strategic plans are focused on how to achieve the targets as outline in Vision 2023. We evaluate the performance of the models on how best they forecast electrical energy consumption for the year 2023. Our forecasts for the year 2023 are 352.7TWh, 377.4 TWh, and 460.1TWh for the Gaussian, Fourier and Exponential models respectively which compare well with NDP11’s estimated 375.8 TWh electrical energy consumption in 2023.
Thanks
The authors thank Mr. Mehmet Alkan for his valuable help in simulation.
References
- C. Erdin, and G. Ozkaya, “Turkey’s 2023 Energy Strategies and Investment Opportunities for Renewable Energy Sources,” Site Selection Based on ELECTRE. Sustainability, vol. 11, pp. 21-36, 2019.
- Turkey Electricity Transmission Company, Electricity Generation - Transmission Statistics of Turkey, 2018.
- Turkey Electricity Distribution Company, Electricity Distribution and Consumption Statistics of Turkey, 2018.
- Presidency of the Republic of Turkey, Eleventh Development Plan (2019-2023), July 2019
- Y. Yunus and J. Mo, “Forecasting of Turkey’s Electricity Consumption Using Artificial Neural Network,” World Automation Congress 2014 ISI Press.
- A. Gülsüm, “Forecasting Regional Electricity Demand for Turkey,” International Journal of Energy Economics and Policy, vol. 7, no. 4, pp. 275-282, 2017.
- B Şule, T. Ayça, “Modelling and Forecasting Turkey’s Electricity Consumption by using Artificial Neural Network,” American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), vol. 25, no. 1, pp. 192-208, 2016.
- A. Gokhan, “Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections,” Renewable and Sustainable Energy Reviews, vol. 35, pp. 382-389, 2014.
- D.C. Sansom, T. Downs, T. K. Saha, “Evaluation of support vector machine-based forecasting tool in electricity price forecasting for Australian national electricity market participants,” Journal of Electrical and Electronics Engineering Australia, vol. 22, no. 3, pp. 227-234, 2003.
- C. Kuster, Y. Rezgui, M. Mourshed, “Electrical load forecasting models: A critical systematic review,” Sustainable Cities and Society, vol. 35, pp. 257-270, 2017.
- A.T. Barran, A. Alonso, J.R. Dorronsoro, “Regression tree ensembles for wind energy and solar radiation prediction,” Neurocomputing, vol. 326, pp. 151-160, 2019.
- O. F. Ertugrul, “Forecasting electricity load by a novel recurrent extreme learning machines approach,” Electrical Power and Energy Systems, vol. 78, pp. 429-435, 2016.
- J. Zhang, Y.M. Wei, D. Li, Z. Tan, J. Zhou, “Short term electricity load forecasting using a hybrid model,” Energy, vol. 158, pp. 774-781, 2018.
- A. Khosravi, R. N. N. Koury, L. Machado, J.J.G. Pabon, “Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system,” Sustainable Energy Technologies and Assessments, vol. 25, pp. 146-160, 2018.
- A. Unler, “Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025,” Energy Policy, vol. 36, pp. 1937-1944, 2008.
- M. Kankal, E. Uzlu, “Neural network approach with teaching learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey,” Neural Comput and Applic, vol. 28, no.1, pp. 737-747, 2017.
- İ. Topcu, F. Ülengin, Ö. Kabak, M.Işık, B. Ünver, Ş.Ö. Ekici, “The evaluation of electricity generation resources: The case of Turkey,” Energy, vol. 167, pp. 417-427, 2019.
- F. Kaytez, “A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption”, Energy, vol. 197, pp. 187-200, 2020.
- H.H. Çevik, H. Harmancı, M. Çunkaş, “Forecasting Hourly Electricity Demand Using a Hybrid Method,” 2017 International Conference on Consumer Electronics and Devices.
- C. Hamzaçebi, H.A. Es, R. Çakmak, “Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network,” Neural Comput and Applic, vol. 31, pp. 2217-2231, 2019.
- G. Çeribaşı G, M. Çalışkan, “Short and long term prediction of energy to be produced in hydroelectric energy plants of Sakarya Basin in Turkey,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1-16, 2019.
- M.A. Farahat, M. Talaat, “A New Approach for Short-Term Load Forecasting Using Curve Fitting Prediction Optimized by Genetic Algorithms” Mathematics and Engineering,” pp. 19-21, 2010.
- E. Ismail, B. Rachid, A. Abdelah, A. Othman, M.G. Josep, “Energy Production: A Comparison of Forecasting Methods using the Polynomial Curve Fitting and Linear Regression” International Renewable and Sustainable Energy Conference (IRSEC), Tangier, pp. 1-5, 2017.
- R. J. Hyndman and A.B. Koehler, “Another look at measures of forecast accuracy” International Journal of Forecasting vol. 22, pp. 679– 688, 2006.
- K. Kavaklioglu, H. Ceylan, H.O. Kemal, O.E. Canyurt, “Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks,” Energy Conversion and Management, vol. 50, pp. 2719-2727, 2009.
- K. Kavaklioglu, “Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression,” Applied Energy vol. 88, pp. 368-375, 2011.
- D. Toksarı, “Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey,” Energy Policy, vol. 37, pp. 1181-1187, 2009.
- M.S. Kıran, E. Ozceylan, M. Gündüz, T. Paksoy, “Swarm intelligence approaches to estimate electricity energy demand in Turkey,” Knowledge-Based Systems, vol. 36, pp. 93-103, 2012.
Year 2021,
Volume: 4 Issue: 2, 266 - 276, 31.08.2021
Abstract
References
- C. Erdin, and G. Ozkaya, “Turkey’s 2023 Energy Strategies and Investment Opportunities for Renewable Energy Sources,” Site Selection Based on ELECTRE. Sustainability, vol. 11, pp. 21-36, 2019.
- Turkey Electricity Transmission Company, Electricity Generation - Transmission Statistics of Turkey, 2018.
- Turkey Electricity Distribution Company, Electricity Distribution and Consumption Statistics of Turkey, 2018.
- Presidency of the Republic of Turkey, Eleventh Development Plan (2019-2023), July 2019
- Y. Yunus and J. Mo, “Forecasting of Turkey’s Electricity Consumption Using Artificial Neural Network,” World Automation Congress 2014 ISI Press.
- A. Gülsüm, “Forecasting Regional Electricity Demand for Turkey,” International Journal of Energy Economics and Policy, vol. 7, no. 4, pp. 275-282, 2017.
- B Şule, T. Ayça, “Modelling and Forecasting Turkey’s Electricity Consumption by using Artificial Neural Network,” American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), vol. 25, no. 1, pp. 192-208, 2016.
- A. Gokhan, “Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections,” Renewable and Sustainable Energy Reviews, vol. 35, pp. 382-389, 2014.
- D.C. Sansom, T. Downs, T. K. Saha, “Evaluation of support vector machine-based forecasting tool in electricity price forecasting for Australian national electricity market participants,” Journal of Electrical and Electronics Engineering Australia, vol. 22, no. 3, pp. 227-234, 2003.
- C. Kuster, Y. Rezgui, M. Mourshed, “Electrical load forecasting models: A critical systematic review,” Sustainable Cities and Society, vol. 35, pp. 257-270, 2017.
- A.T. Barran, A. Alonso, J.R. Dorronsoro, “Regression tree ensembles for wind energy and solar radiation prediction,” Neurocomputing, vol. 326, pp. 151-160, 2019.
- O. F. Ertugrul, “Forecasting electricity load by a novel recurrent extreme learning machines approach,” Electrical Power and Energy Systems, vol. 78, pp. 429-435, 2016.
- J. Zhang, Y.M. Wei, D. Li, Z. Tan, J. Zhou, “Short term electricity load forecasting using a hybrid model,” Energy, vol. 158, pp. 774-781, 2018.
- A. Khosravi, R. N. N. Koury, L. Machado, J.J.G. Pabon, “Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system,” Sustainable Energy Technologies and Assessments, vol. 25, pp. 146-160, 2018.
- A. Unler, “Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025,” Energy Policy, vol. 36, pp. 1937-1944, 2008.
- M. Kankal, E. Uzlu, “Neural network approach with teaching learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey,” Neural Comput and Applic, vol. 28, no.1, pp. 737-747, 2017.
- İ. Topcu, F. Ülengin, Ö. Kabak, M.Işık, B. Ünver, Ş.Ö. Ekici, “The evaluation of electricity generation resources: The case of Turkey,” Energy, vol. 167, pp. 417-427, 2019.
- F. Kaytez, “A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption”, Energy, vol. 197, pp. 187-200, 2020.
- H.H. Çevik, H. Harmancı, M. Çunkaş, “Forecasting Hourly Electricity Demand Using a Hybrid Method,” 2017 International Conference on Consumer Electronics and Devices.
- C. Hamzaçebi, H.A. Es, R. Çakmak, “Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network,” Neural Comput and Applic, vol. 31, pp. 2217-2231, 2019.
- G. Çeribaşı G, M. Çalışkan, “Short and long term prediction of energy to be produced in hydroelectric energy plants of Sakarya Basin in Turkey,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1-16, 2019.
- M.A. Farahat, M. Talaat, “A New Approach for Short-Term Load Forecasting Using Curve Fitting Prediction Optimized by Genetic Algorithms” Mathematics and Engineering,” pp. 19-21, 2010.
- E. Ismail, B. Rachid, A. Abdelah, A. Othman, M.G. Josep, “Energy Production: A Comparison of Forecasting Methods using the Polynomial Curve Fitting and Linear Regression” International Renewable and Sustainable Energy Conference (IRSEC), Tangier, pp. 1-5, 2017.
- R. J. Hyndman and A.B. Koehler, “Another look at measures of forecast accuracy” International Journal of Forecasting vol. 22, pp. 679– 688, 2006.
- K. Kavaklioglu, H. Ceylan, H.O. Kemal, O.E. Canyurt, “Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks,” Energy Conversion and Management, vol. 50, pp. 2719-2727, 2009.
- K. Kavaklioglu, “Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression,” Applied Energy vol. 88, pp. 368-375, 2011.
- D. Toksarı, “Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey,” Energy Policy, vol. 37, pp. 1181-1187, 2009.
- M.S. Kıran, E. Ozceylan, M. Gündüz, T. Paksoy, “Swarm intelligence approaches to estimate electricity energy demand in Turkey,” Knowledge-Based Systems, vol. 36, pp. 93-103, 2012.
There are 28 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Software Engineering (Other) |
| Journal Section | Articles |
| Authors | |
| Publication Date | August 31, 2021 |
| Submission Date | June 17, 2021 |
| Acceptance Date | August 22, 2021 |
| Published in Issue | Year 2021Volume: 4 Issue: 2 |
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