Articles
| Open Access | ENHANCING ENERGY EFFICIENCY IN GREENHOUSES USING PCM-BASED THERMAL STORAGE SYSTEMS INTEGRATED WITH ARTIFICIAL INTELLIGENCE AND BIG DATA TECHNOLOGIES
O..Z. Toirov, D.O. Hojiev,E.T. Juraev , Tashkent State Technical University, Tashkent, Uzbekistan/National Research Institute of Renewable Energy Sources under the Ministry of Energy, Tashkent, UzbekistanAbstract
This paper investigates the role of artificial intelligence (AI) and Big Data technologies in enhancing energy efficiency in greenhouses equipped with thermal storage systems based on phase change materials (PCMs). Research indicates that AI can be utilized to forecast temperature fluctuations and optimize PCM performance, while Big Data supports identifying the most efficient solutions through analysis of large datasets. Literature reviews suggest that such integrated systems can reduce energy consumption by 17–25%. However, their effectiveness varies with climatic conditions and greenhouse design, highlighting the need for system adaptation to specific environments.
Keywords
PCM, greenhouse, solar energy, thermal storage, AI, Big Data
References
Chen, Y., Zhang, G., & Wang, J. (2021). Optimization of Phase Change Material Usage in Greenhouses Using Artificial Intelligence Algorithms. Renewable Energy Journal, 172, 1154–1163.
Kumar, R., & Singh, M. (2020). Big Data Analytics in Smart Agriculture: Enhancing Energy Efficiency in PCM-based Greenhouses. Journal of Clean Energy Technologies, 8(6), 123–130.
Zhang, H., & Li, X. (2019). Thermal Performance Enhancement of PCM Systems Using Predictive Control Models. Applied Thermal Engineering, 147, 922–930.
Lin, Z., et al. (2022). AI-driven Climate Control in Greenhouses with Thermal Energy Storage. Energy Reports, 8, 4440–4452..
International Energy Agency (IEA). (2022). Digitalization and Energy. Retrieved from iea.org
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This work is licensed under a Creative Commons Attribution 4.0 International License.