Estimation of Primary Energy Consumption for a Panel of Five ASEAN Countries by Models Derived from Trend Analysis

Öz

This work employed trend analysis as a simple and practical modeling technique to derive estimating models for the primary energy consumption (PEC). A panel of five ASEAN countries, known as the ASEAN-5 (Indonesia, the Philippines, Malaysia, Singapore and Thailand) were selected as the study's case. Various statistical indices were then employed to test the validity and robustness of the derived models. In addition, the PECs of the ASEAN-5 countries were estimated by the derived models from 2025 to 2035. The results showed that all of the derived models could achieve up to 97% accuracy in their estimations. Additionally, the projected results revealed that the ASEAN-5 countries' total PEC would increase at a rate of 1.82% annually and 20.33% overall by 2035, when compared to the data in 2023. In summary, this paper concludes that models for estimating the PEC may be derived efficiently using the trend analysis.

Anahtar Kelimeler

• Primary energy consumption
• Trend analysis
• Modelling
• Estimation

Trend Analizinden Türetilen Modeller ile Beş ASEAN Ülkesinin Birincil Enerji Tüketiminin Tahmini

Öz

Bu çalışmada, birincil enerji tüketimine (PEC) yönelik tahmin modelleri türetmek amacıyla basit ve pratik bir modelleme tekniği olarak trend analizi kullanılmıştır. ASEAN-5 (Endonezya, Filipinler, Malezya, Singapur ve Tayland) olarak bilinen beş ASEAN ülkesinden oluşan bir grup ülke, çalışmanın örneklemi olarak seçilmiştir. Ayrıca, türetilen modellerin geçerliliğini ve sağlamlığını test etmek için de çeşitli istatistiksel endeksler kullanılmıştır. Ek olarak, ASEAN-5 ülkelerinin PEC'leri türetilen modeller tarafından 2025'ten 2035'e kadar tahmin edilmiştir. Sonuçlar, türetilen tüm modellerin tahminlerinde %97'ye varan doğruluk elde edebileceğini göstermiştir. Tahmin sonuçları, ASEAN-5 ülkelerinin toplam PEC'nin 2023'teki verilerle karşılaştırıldığında 2035 yılına kadar yıllık %1,82 ve genel olarak %20,33 oranında artacağını ortaya koymuştur. Özetle, bu çalışma, PEC'ni tahmin etmek için trend analizi kullanılarak modellerin verimli bir şekilde türetilebileceği sonucuna varmıştır.

Anahtar Kelimeler

• Birincil enerji tüketimi
• Trend analizi
• Modelleme
• Tahmin

Kaynakça

1. [1] Aras, N. (2008). Forecasting residential consumption of natural gas using genetic algorithms. Energy Exploration & Exploitation, 26(4), 241-266. https://dx.doi.org/10.1260/014459808787548705.
2. [2] EI. (2024). Statistical review of world energy 2024. Retrieved from https://www.energyinst.org/statistical-review/resources-and-data-downloads [accessed 5 July 2024].
3. [3] Ozdemir, M., Pehlivan, S., Melikoglu, M. (2024). Estimation of greenhouse gas emissions using linear and logarithmic models: A scenario-based approach for Turkiye’s 2030 vision. Energy Nexus, 13, 100264. https://doi.org/10.1016/j.nexus.2023.100264.
4. [4] Husaini, D.H., Lean, H.H. (2024). Digitalization and Energy Sustainability in ASEAN. Resources, Conservation & Recycling, 184, 106377. https://doi.org/10.1016/j.resconrec.2022.106377.
5. [5] IEA. (2024). World energy outlook 2019. Retrieved from https://www.iea.org/reports/worldenergyoutlook-2019 [accessed 10 March 2025].
6. [6] Heffron, R., Merdekawati, M., Suryadi, B., Yurnaidi, Z. (2024). Defining a ‘Just Energy Investment’ for the ASEAN Just Transition. Environmental and Sustainability Indicators, 22, 100367. https://doi.org/10.1016/j.indic.2024.100367.
7. [7] Fikru, M.G., Kisswani, K.M. 2(023) Environmental impacts of household energy use in ASEAN-5 countries: Are there asymmetric effects?. Energy Policy, 182, 113771. https://doi.org/10.1016/j.enpol.2023.113771.
8. [8] Celiker, M., Yukseler, U., Dursun, O.F. (2021). Trend analyses for discharge-recharge of Tacin karstic spring (Kayseri, Turkey). Journal of African Earth Sciences, 184, 104344. https://doi.org/10.1016/j.jafrearsci.2021.104344.

9. [9] Kone, A.C., Buke, T. (2010). Forecasting of CO2 emissions from fuel combustion using trend analysis. Renewable and Sustainable Energy Reviews, 14, 2906–2915. https://doi.org/10.1016/j.rser.2010.06.006.
10. [10] Lau, H.C. (2022). Decarbonization roadmaps for ASEAN and their implications. Energy Reports, 8, 6000–6022. https://doi.org/10.1016/j.egyr.2022.04.047.
11. [11] ASEAN. (2024). About ASEAN, Association of Southeast Asian Nations. Retrieved from https://asean.org/about-asean/ [accessed 01 April 2025]
12. [12] WBI. (2025) Worldbank indicators on urban population. Retrieved from https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS [accessed 15 March 2025]
13. [13] Kaboli, A.H.S., Fallahpour, A., Selvaraj, J., Rahim, N.A. (2017). Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming. Energy, 126, 144-164. http://dx.doi.org/10.1016/j.energy.2017.03.009.
14. [14] URL-1. (2025). Colorful Asean map on white background. Stock Vector. Retrieved from https://stock.adobe.com/images/colorful-asean-map-on-white-background/97922674 [accessed 20 March 2025].
15. [15] Kavaklıoğlu, K., Ceylan, H., Ozturk, K.H., Canyurt, E.O. (2009). Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion and Management, 50, 2719–2727. https://doi.org/10.1016/j.enconman.2009.06.016.
16. [16] Kankal, M., Akpınar, A., Komurcu, I.M., Ozsahin, S.T. (2011). Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Applied Energy, 88, 1927–1939. https://doi.org/10.1016/j.apenergy.2010.12.005.
17. [17] Shapi, M.K.M., Ramli, N.A., Awalin, L.J. (2021). Energy consumption prediction by using machine learning for smart building: Case study in Malaysia. Developments in the Built Environment, 5, 100037. https://doi.org/10.1016/j.dibe.2020.100037.
18. [18] Erbay, S., Beydogan, H.O. (2017). Attitudes of educators towards educational research. Kırsehir Journal of the Faculty of Education, 18(3), 246-260.
19. [19] George, D., Mallery, P. (2021). IBM SPSS statistics 27 step by step: A simple guide and reference, New York, by Routledge, 418 pages.
20. [20] Ostrom, C.W. (1978). Time series analysis-regression techniques, the USA, Sage publications, 82 pages.
21. [21] Aydin, G., Kaya, S., Karakurt, I. (2015). Modeling of energy consumption based on population: The case of Turkey. 24th. International Mining Congress and Exhibition of Türkiye, Antalya-Türkiye, pp.88-92. [22] Aydin, G. (2015). Forecasting natural gas production using various regression models. Petroleum Science and Technology, 33, 1486–1492. http://doi.org/10.1080/10916466.2015.1076842.
22. [23] Kok, B., Benli, H. (2017). Energy diversity and nuclear energy for sustainable development in Turkey. Renewable Energy, 111, 870-877. https://doi.org/10.1016/j.renene.2017.05.001.
23. [24] Gonzalez, M.W., Crujeiras, R.M. (2013). An updated review of goodness-of-fit tests for regression models. Test, 22, 361-411. https://doi.org/10.1007/s11749-013-0327-5.
24. [25] Aydin, G. (2015). Production modeling in the oil and natural gas industry: An application of trend analysis. Petroleum Science and Technology, 32(5), 555-564. https://doi.org/10.1080/10916466.2013.825271.
25. [26] Despotovic, M., Nedic, V., Despotovic, D., Cvetanovic, S. (2015). Review and statistical analysis of different global solar radiation sunshine models. Renewable and Sustainable Energy Reviews, 52, 1869–1880. http://dx.doi.org/10.1016/j.rser.2015.08.035.
26. [27] Yildiriz, G., Ozturk, A. (2022). Electrical energy consumption forecasting using regression method considering temperature effect for distribution network. Electrical Engineering, 104, 3465–3476. https://doi.org/10.1007/s00202-022-01559-8.
27. [28] Uma, M.J., Yun, H., Jeong, C.S., Heo, J.H. (2011). Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea. Journal of Hydrology, 410, 189–203. https://doi.org/10.1016/j.jhydrol.2011.09.016.
28. [29] Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M.Z., Barrow, D.K., Taieb, S.B. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38, 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001.
29. [30] Singh, K.P., Basant, A., Malik, A., Jain, G. (2009). Artificial neural network modeling of the river water quality – a case study. Ecological Modelling, 220(6), 888–895. https://doi.org/10.1016/j.ecolmodel.2009.01.004.
30. [31] Paiva, H., Afonso, R.J.M., Caldeira, F.M.S.L.A., Velasquez, E.A. (2021). A computational tool for trend analysis and forecast of the COVID-19 pandemic. Applied Soft Computing, 105, 107289. https://doi.org/10.1016/j.asoc.2021.107289.
31. [32] Bianco, V., Scarpa, F., Tagliafico, L.A. (2014). Analysis and future outlook of natural gas consumption in the Italian residential sector. Energy Conversion and Management, 87, 754–764. https://doi.org/10.1016/j.enconman.2014.07.081.
32. [33] Azadeh, A., Saberi, M., Asadzadeh, S.M., Khakestani, M. (2011). A hybrid fuzzy mathematical programming-design of experiment framework for improvement of energy consumption estimation with small data sets and uncertainty: The cases of USA, Canada, Singapore, Pakistan and Iran. Energy, 36, 6981-6992. https://doi.org/10.1016/j.energy.2011.07.016.
33. [34] Byrne, R.F. (2012). Beyond traditional time-series: Using demand sensing to improve forecasts in volatile times. The Journal of Business Forecasting: Methods & Systems, 31(2), 13-20. https://api.semanticscholar.org/CorpusID:151263481.
34. [35] Kim, S., Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32, 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003.
35. [36] Bianco, V., Manca, O., Nardini, S., Mine, A.A. (2010). Analysis and forecasting of nonresidential electricity consumption in Romania. Applied Energy, 87, 3584–3590. https://doi.org/10.1016/j.apenergy.2010.05.018.
36. [37] Li, M.F., Tang, X.P., Wu, W., Liu, H.B. (2013). General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Conversion and Management, 70, 139–48. http://dx.doi.org/10.1016/j.enconman.2013.03.004.
37. [38] Lewis, C.D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting, London. Butterworths scientific, 143 pages.
38. [39] UN. (2018). United Nations, department of economic and social affairs, population division, world urbanization prospects: The 2018 revision. Retrieved from https://population.un.org/wup/Download/; [accessed 10 March 2025].
39. [40] Kunvitaya, A., Dhakal, S. (2017). Household energy requirements in two medium-sized Thai cities with different population densities. Environment and Urbanization, 29(1), 267-282. https://doi.org/10.1177/0956247816659.
40. [41] Ohlan, R. (2015). The impact of population density, energy consumption, economic growth and trade openness on CO2 emissions in India. Natural Hazards, 79(2), 1409–1428. https://doi.org/10.1007/s11069-015-1898-0.
41. [42] Otsuka, A. (2018). Population agglomeration and residential energy consumption: Evidence from Japan. Sustainability, 10(2), 469(1-12). https://doi:10.3390/su10020469.
42. [43] Rahman, M.M. (2020). Exploring the effects of economic growth, population density and international trade on energy consumption and environmental quality in India. International Journal of Energy Sector Management, 14(6), 1177-1203. https://doi.org/10.1108/IJESM-11-2019-0014.
43. [44] Rahman, M.M., Vu, X.B. (2021). Are energy consumption, population density and exports causing environmental damage in China? Autoregressive distributed lag and vector error correction model approaches. Sustainability, 13, 3749. https://doi.org/10.3390/su13073749.
44. [45] Alisjahbana, A.S. (2024). The 2010 – 2035 Indonesian population projection. Retrieved from https://media.neliti.com/media/publications/49322-ID-proyeksi-penduduk-indonesia-indonesia-population-projection-2010-2035.pdf [accessed 10 March 2025].
45. [46] Azam, M., Khan, A.O., Zaman, K., Ahmad, M. (2015). Factors determining energy consumption: Evidence from Indonesia, Malaysia and Thailand. Renewable and Sustainable Energy Reviews, 42, 1123–1131. http://dx.doi.org/10.1016/j.rser.2014.10.061.
46. [47] Patarasuk, R., Gurney, K.R., O’Keeffe, D., Song, Y., Huang, J., Rao, P. (2016). Urban high-resolution fossil fuel CO2 emissions quantification and exploration of emission drivers for potential policy applications. Urban Ecosystems, 19, 1013–1039. https://doi.org/10.1007/s11252-016-0553-1.
47. [48] Vo, D.H., Vo, A.T., Ho, C.M. (2024). Urbanization and renewable energy consumption in the emerging ASEAN markets: A comparison between short and long-run effects. Heliyon, 10, e30243. https://doi.org/10.1016/j.heliyon.2024.e30243.
48. [49] Liu, Y., Noor, R. (2024). Energy efficiency in ASEAN: Trends and financing schemes, ADBI Working Paper Series, No. 1196, Asian Development Bank Institute (ADBI), Tokyo. Retrieved from https://hdl.handle.net/10419/238553; [accessed 10 March 2025].
49. [50] Abrigo, M.R.M., Ortiz, M.K.P. (2024). Residential electricity consumption over the demographic transition in the Philippines. Journal of the Economics of Ageing, 27, 100503. https://doi.org/10.1016/j.jeoa.2024.100503.
50. [51] PDP. (2024). Updated Philippine development plan. Retrieved from https://pdp.neda.gov.ph/wp-content/uploads/2021/12/Chapter-03.pdf [accessed 27 March 2025].
51. [52] PSA. (2024). Philippine statistics authority. Retrieved from https://www.foi.gov.ph/requests/the-total-population-in-manila-city-from-2018-2023-and-its-annual-growth-rate/; [accessed 25 March 2024].
52. [53] Raitzer, D.A., Bosello, F., Tavoni, M., Orecchia, C., Marangoni, G., Samson, J.N.G. (2024). Southeast Asia and the economics of global climate stabilization. Asian Development Bank, https://www.adb.org/sites/default/files/publication/178615/sea-economics-global-climate-stabilization.pdf; [accessed 02 April 2025].
53. [54] Binh, P.T., Nguyen, T.T.T. (2024). Exploring the impact of public investment on income, energy consumption, and CO2 emissions in ASEAN: New insights from a panel cointegration approach. Environmental Science and Pollution Research, 31, 6301–6315. https://doi.org/10.1007/s11356-023-31588-0.
54. [55] Erdiwansyah, M., Mamat, R., Sani, M.S.M., Khoerunnisa, F.,Kadarohman, A. (2019) Target and demand for renewable energy across 10 ASEAN countries by 2040. The Electricity Journal, 32(10), 106670. https://doi.org/10.1016/j.tej.2019.106670