OPPORTUNITIES OF INTEGRATING ARTIFICIAL INTELLIGENCE INTO THE EXPLOITATION OF RENEWABLE ENERGY IN KAZAKHSTAN

The purpose of the research is to delve into the intersection of renewable energy sources and artificial intelligence in Kazakhstan. It aims to uncover the potential benefits, challenges, and overall impact on the country's economy and energy sector, offering a fresh perspective on this emerging field.

Research methodology adopts a mixed-methods approach, incorporating both systematic review and comprehensive research methods. Data is gathered through academic databases, journals, relevant literature analysis, and case studies, which offer practical insights into the applications and outcomes of AI-driven renewable energy projects in Kazakhstan.

Originality / value of the research is unique in its focus on the intersection of artificial intelligence and renewable energy within the specific context of Kazakhstan. It offers valuable insights into how emerging technologies can drive economic growth and sustainability in a developing country and provides practical strategies for harnessing these opportunities. The study significantly contributes to the literature by providing a deep and comprehensive analysis of the economic implications and strategic opportunities associated with this integration.

The research findings reveal that the application of artificial intelligence to renewable energy sources in Kazakhstan can significantly enhance energy efficiency, reduce costs, and increase the reliability of energy supply. Moreover, it identifies several key economic opportunities, including job creation, technological ad- vancement and the capacity for the state to establish the regional leadership regarding the renewable energy. This study reveals the challenges and requirements for the significant funding into the infrastructure, personnel development, regulatory amendments, setting a roadmap for policymakers and industry professionals.

1. Tokayev, K. (2023). President Kassym-Jomart Tokayev’s state of the Nation Address, a fair state. One nation. Prosperous Society. Official website of the President of the Republic of Kazakhstan. Retrieved April 15, 2024, from https://www.akorda.kz/en/president-kassym-jomart-tokayevs-state-of-the-nation-address-181857.

2. Kalikov, M., Uteyev, B. Z., Khajieva, A., & Torekulova, U. A. (2020). Green economy as a paradigm of sustainable development of the Republic of Kazakhstan. Journal of Environmental Accounting and Management, 8(3), 281-292.

3. Akhanova, G., Nadeem, A., Kim, J. R., & Azhar, S. (2020). A multi-criteria decision-making framework for building sustainability assessment in Kazakhstan. Sustainable Cities and Society, 52, Article 101842.

4. Wang, X., Zheng, H., Wang, Z., Shan, Y., Meng, J., Liang, X., Feng, K., & Guan, D. (2019). Kazakhstan's CO2 emissions in the post-Kyoto Protocol era: Production-and consumption-based analysis. Journal of Environmental Management, 249, Article 109393.

5. Mukhamediev, R. I., Mustakayev, R., Yakunin, K., Kiseleva, S., & Gopejenko, V. (2019). Multi-criteria spatial decision making support system for renewable energy development in Kazakhstan. IEEE Access, 7, 122275-122288.

6. Hosseini, S. E., & Abdul Wahid, M. (2015). Effects of burner configuration on the characteristics of biogas flameless combustion. Combustion Science and Technology, 187(8), 1240-1262.

7. Zheng, M., Feng, G. F., Jang, C. L., & Chang, C. P. (2021). Terrorism and green innovation in renewable energy. Energy Economics, 104, Article 105695.

8. Wei, W., Zou, S., Duan, W., Chen, Y., Li, S., & Zhou, Y. (2023). Spatiotemporal variability in extreme precipitation and associated large-scale climate mechanisms in Central Asia from 1950 to 2019. Journal of Hydrology, 620, Article 129417.

9. Zahraee, S. M., Khademi, A., Khademi, S., Abdullah, A., & Ganjbakhsh, H. (2014). Application of design experiments to evaluate the effectiveness of climate factors on energy saving in green residential buildings. Jurnal Teknologi, 69(5), 107-111.

10. Patz, J. A., Frumkin, H., Holloway, T., Vimont, D. J., & Haines, A. (2014). Climate change: Challenges and opportunities for global health. Jama, 312(15), 1565-1580.

11. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change / Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.). (2014). IPCC, Geneva, Switzerland: IPCC. 151 p.

12. UNFCCC. (2015). Kazakhstan, Intended Nationally Determined Contribution ‐ Submission of the Republic of Kazakhstan. 4 p.

13. UNFCCC. (2019). Report On the Individual Review of the Annual Submission of Kazakhstan Submitted in 2017. FCCC/ARR/2017/KAZ. 148 p.

14. Russell, A., Ghalaieny, M., Gazdiyeva, B., Zhumabayeva, S., Kurmanbayeva, A., Akhmetov, K. K., Mukanov, Y., McCann, M., Ali, M., Tucker, A., & Vitolo, C. (2018). A spatial survey of environmental indicators for Kazakhstan: An examination of current conditions and future needs. International Journal of Environmental Research, 12, 735-748.

15. Hepbasli, A., & Alsuhaibani, Z. (2014). Estimating and comparing the exergetic solar radiation values of various climate regions for solar energy utilization. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 36(7), 764-773.

16. Xin-gang, Z., & You, Z. (2018). Technological progress and industrial performance: A case study of the solar photovoltaic industry. Renewable and Sustainable Energy Reviews, 81, 929-936.

17. Witajewski-Baltvilks, J., & Fischer, C. (2023). Green Innovation and Economic Growth in a NorthSouth Model. Environmental and Resource Economics, 85(3), 615-648.

18. Alekseeva, L., Azar, J., Gine, M., Samila, S., & Taska, B. (2021). The demand for AI skills in the labor market. Labour Economics, 71, Article 102002.

19. Grashof, N., & Kopka, A. (2023). Artificial intelligence and radical innovation: An opportunity for all companies? Small Business Economics, 61(2), 771-797.

20. Füller, J., Hutter, K., Wahl, J., Bilgram, V., & Tekic, Z. (2022). How AI revolutionizes innovation management–Perceptions and implementation preferences of AI-based innovators. Technological Forecasting and Social Change, 178, Article 121598.

21. Mishra, S., Ewing, M. T., & Cooper, H. B. (2022). Artificial intelligence focus and firm performance. Journal of the Academy of Marketing Science, 50(6), 1176-1197.

22. Frías-Paredes, L., Mallor, F., Gastón-Romeo, M., & León, T. (2017). Assessing energy forecasting inaccuracy by simultaneously considering temporal and absolute errors. Energy Conversion and Management, 142, 533-546.

23. Chen, Z. M., & Chen, G. Q. (2011). An overview of energy consumption of the globalized world economy. Energy Policy, 39(10), 5920-5928.

24. Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018). Solar photovoltaic generation forecasting methods: A review. Energy Conversion and Management, 156, 459-497.

25. Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., Horan, B., & Stojcevski, A. (2018). Forecasting of photovoltaic power generation and model optimization: A review. Renewable and Sustainable Energy Reviews, 81, 912-928.

26. Daut, M. A. M., Hassan, M. Y., Abdullah, H., Rahman, H. A., Abdullah, M. P., & Hussin, F. (2017). Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. Renewable and Sustainable Energy Reviews, 70, 1108-1118.

27. Bhandari, B., Lee, K. T., Lee, G. Y., Cho, Y. M., & Ahn, S. H. (2015). Optimization of hybrid renewable energy power systems: A review. International Journal of Precision Engineering and ManufacturingGreen Technology, 2, 99-112.

28. Zhang, L., Ling, J., & Lin, M. (2022). Artificial intelligence in renewable energy: A comprehensive bibliometric analysis. Energy Reports, 8, 14072-14088.

29. Xu, J., Assenova, A., & Erokhin, V. (2018). Renewable energy and sustainable development in a resource-abundant country: Challenges of wind power generation in Kazakhstan. Sustainability, 10(9), 1-21.

30. Nurgisaeva, A. A., & Tamenova, S. S. (2022). Gosudarstvenno-chastnoe partnerstvo v oblasti «Zelenoj» ekonomiki megapolisa. Central Asian Economic Review, 3, 75-87. https://doi.org/10.52821/2789-44012022-3-75-87 (In Russian).

31. Concept for transition of the Republic of Kazakhstan to Green Economy. Contents, approved by Decree of the President of the Republic of Kazakhstan on 20 May 2013 № 557. (2013). Astana. 51 p.

32. PWC. (2023). Energy transition in Kazakhstan – Back to the sustainable future. PwC Kazakhstan. 66 p.

33. Global Wind Energy Council (GWEC). (2020). Wind energy in emerging markets. Retrieved May 28, 2024, from https://www.gwec.net/wind-energy-emerging-markets.

34. International Energy Agency (IEA). (2021). AI and the future of energy. Retrieved May 28, 2024, from https://www.iea.org/reports/ai-and-the-future-of-energy.

35. First Wind Power Station. (2024). Company history. Retrieved May 28, 2024, from https://pves.kz/en/company/story.

36. National Renewable Energy Laboratory (NREL). (2016). Integration of renewable energy in Kazakhstan. Retrieved May 28, 2024, from https://www.nrel.gov/docs/fy19osti/74216.pdf.

37. Ministry of Energy of the Republic of Kazakhstan. (2021). Development of renewable energy sources. Retrieved May 28, 2024, from https://www.gov.kz/memleket/entities/energo/press/article/details/47382?lang=en.

38. PwC. (2024). The Committee for Artificial Intelligence and Innovation Development has been established in Kazakhstan. Nalogovyj i yuridicheskij vestnik, Special edition №233. Retrieved May 12, 2024, from https://www.pwc.com/kz/en/pwc-news/ta-reports/tax-legal-alert-fy19/233-may-2024.html.

39. Zhazetova, Zh. (2024). Every fifth public service in Kazakhstan is going to be provided with the help of AI by 2029. Kursiv. Retrieved May 12, 2024, from https://kz.kursiv.media/en/2024-03-18/every-fifth-publicservice-in-kazakhstan-is-going-to-be-provided-with-the-help-of-ai-by-2029/#:~:text=Kazakhstan%20is%20going%20to%20adopt,year%20to%2020%25%20by%202029.

40. Zhou, Y. (2022). Artificial intelligence in renewable systems for transformation towards intelligent buildings. Energy and AI, 10, Article 100182.

41. Chatterjee, J., & Dethlefs, N. (2021). Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future. Renewable and Sustainable Energy Reviews, 144, Article 111051.

42. Zhang, G., Jin, Y., & Wang, B. (2023). Application of artificial intelligence and communication technology to water and energy balance models. Water Supply, 23(7), 2847-2864.

43. Khare, V., & Bhuiyan, M. A. (2022). Tidal energy-path towards sustainable energy: A technical review. Cleaner Energy Systems, 3, Article 100041.

44. Liao, M., & Yao, Y. (2021). Applications of artificial intelligence‐based modeling for bioenergy systems: A review. GCB Bioenergy, 13(5), 774-802.

45. Moraga, J., Duzgun, H. S., Cavur, M., & Soydan, H. (2022). The geothermal artificial intelligence for geothermal exploration. Renewable Energy, 192, 134-149.