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2026
III International Bioeconomy Forum: Special Issue

V-SEKSIYA - SIRKULYAR IQTISODIYOT VA QAYTA TIKLANUVCHI ENERGIYA MANBALARINI INTEGRATSIYALASH IMKONIYATLARINI OSHIRISH

KICHIK BIZNES SUBYEKTLARIDA QAYTA TIKLANUVCHI ENERGIYA RESURSLARIDAN SAMARALI FOYDALANISHNI SUNʼIY INTELLEKT TEXNOLOGIYALARI ASOSIDA BOSHQARISH

DOI 10.67668/mj/2026issmaxsus/2077 Pages 202-212

Published 2026-09-25

Authors
Tursunxoʻjayeva Gulnoza Jamoliddin qizi
Affiliation
Alfraganus University image/svg+xml
III Bioiqtisodiyot xalqaro forumi, Buxoro, 24-25-sentabr 2026
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Abstract

The article examined the theoretical and practical foundations of managing the use of renewable energy resources in small business entities on the basis of artificial intelligence technologies. The author generalised the energy efficiency gap theory, the prosumer concept and demand-side management approaches and interpreted artificial intelligence as a tool compensating for scarce information and managerial resources in small enterprises. The sectoral structure of solar installations in Uzbekistan, capacity dynamics at business facilities and state incentives were assessed using official sources. A conceptual model was developed that combines modules for generation and load forecasting, battery operation optimisation and predictive equipment maintenance. An assessment procedure based on the self-consumption ratio and the economic effect was proposed, and practical recommendations were substantiated for expanding intelligent energy management in small business. The proposed approach helps entrepreneurs reduce energy costs and strengthens the stability of the national energy system.

Keywords

small business, renewable energy, artificial intelligence, solar photovoltaic plant, energy management system, demand-side management, predictive maintenance, energy efficiency

References

  1. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834. https://doi.org/10.1016/j.jclepro.2021.125834
  2. Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020). Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renewable and Sustainable Energy Reviews, 130, 109899. https://doi.org/10.1016/j.rser.2020.109899
  3. Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. da P., Basto, J. P., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
  4. Energyforum.uz. (2025, December 1). Almost 2 GW of solar panels have been installed in Uzbekistan at 141 thousand facilities. https://energyforum.uz/en/news/almost-2-gw-of-solar-panels-have-been-installed-in-uzbekistan-at-141-thousand-facilities
  5. IRENA. (2024). Renewable Power Generation Costs in 2023. Abu Dhabi: International Renewable Energy Agency. https://www.irena.org/Publications/2024/Sep/Renewable-Power-Generation-Costs-in-2023
  6. Jaffe, A. B., & Stavins, R. N. (1994). The energy-efficiency gap: What does it mean? Energy Policy, 22(10), 804-810. https://doi.org/10.1016/0301-4215(94)90138-4
  7. Mariano-Hernández, D., Hernández-Callejo, L., Zorita-Lamadrid, A., Duque- Pérez, O., & Santos García, F. (2021). A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis. Journal of Building Engineering, 33, 101692. https://doi.org/10.1016/j.jobe.2020.101692
  8. Thollander, P., Danestig, M., & Rohdin, P. (2007). Energy policies for increased industrial energy efficiency: Evaluation of a local energy programme for manufacturing SMEs. Energy Policy, 35(11), 5774-5783. https://doi.org/10.1016/j.enpol.2007.06.013
  9. Trianni, A., & Cagno, E. (2012). Dealing with barriers to energy efficiency and SMEs: Some empirical evidences. Energy, 37(1), 494-504. https://doi.org/10.1016/j.energy.2011.11.005
  10. Vázquez-Canteli, J. R., & Nagy, Z. (2019). Reinforcement learning for demand response: A review of algorithms and modeling techniques. Applied Energy, 235, 1072-1089. https://doi.org/10.1016/j.apenergy.2018.11.002
  11. Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., & Fouilloy, A. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569-582. https://doi.org/10.1016/j.renene.2016.12.095
  12. Oʻzbekiston Respublikasi Prezidentining 16-fevral 2026-yil PF-21-son “Mamlakat taraqqiyotining 2030-yilgacha moʻljallangan ustuvor yoʻnalishlari doirasida islohotlarni izchil davom ettirish va yangi bosqichga olib chiqishning qoʻshimcha chora-tadbirlari toʻgʻrisida”gi Farmoni https://lex.uz/ru/docs/-8050769
  13. Oʻzbekiston Respublikasi Prezidentining 2025-yil 19-mart PF-50-son “Kichik va oʻrta biznesning iqtisodiyotdagi oʻrnini oshirish chora-tadbirlari toʻgʻrisida”gi Farmoni. https://lex.uz/uz/docs/-7444738
  14. Oʻzbekiston Respublikasi Prezidentining 2023-yil 16-fevral PQ-57-son “2023-yilda qayta tiklanuvchi energiya manbalarini va energiya tejovchi texnologiyalarni joriy etishni jadallashtirish chora-tadbirlari toʻgʻrisida”gi Qarori. https://lex.uz/uz/docs/-6385716
  15. Oʻzbekiston Respublikasi Prezidentining 2024-yil 14-oktabr PQ-358-son “Sunʼiy intellekt texnologiyalarini 2030-yilga qadar rivojlantirish strategiyasini tasdiqlash toʻgʻrisida”gi Qarori. https://lex.uz/docs/-7158604
  16. Gazeta.uz. (2026, 22-aprel). Oʻzbekistonda 145 mingdan ortiq obyektga umumiy quvvati 2114 MVt boʻlgan quyosh panellari oʻrnatilgan. https://www.gazeta.uz/oz/2026/04/22/solar-panel/
  17. Spot.uz. (2025, 1-may). Uch oyda fuqarolar quyosh panellari orqali 8 mlrd soʻmdan ortiq elektr energiyasi sotdi. https://www.spot.uz/oz/2025/05/01/solar-energy-subsidies
  18. Oʻzbekiston Respublikasi Milliy statistika qoʻmitasi. (2025, 28-oktabr). Oʻzbekiston Respublikasining yalpi ichki mahsuloti (2025-yil yanvar-sentabr): press- reliz. https://stat.uz/img/press-relizlar/analitika-vvp_zb-lotin_p95454.pdf

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