AI-Based Solar Thermal Cooling Optimization for Large-Scale Data Centers: A Sustainable Approach
DOI:
https://doi.org/10.22399/ijasrar.57Keywords:
Artificial Intelligence, Data Center, Solar thermal Cooling, Reinforcement Learning, SustainabilityAbstract
This study investigates the performance of three cooling configurations for a large-scale (1 MW) data center in a high solar irradiance region. The configurations include: (i) a conventional electric chiller system, (ii) a solar thermal cooling system with PID control, and (iii) an AI-optimized solar thermal cooling system using Deep Reinforcement Learning (DRL). A dynamic co-simulation environment was developed integrating TRNSYS, EnergyPlus, and a Python-based PPO agent to evaluate the trade-off between energy efficiency and water sustainability. The results demonstrate that the AI-based system reduces grid electricity consumption by 79% and water usage by 92% compared to the conventional baseline. Furthermore, it significantly improves thermal stability, achieving a Root Mean Square Error (RMSE) of 0.24°C relative to the setpoint, and shortens the response time to load disturbances to 11 seconds. This work validates that AI-enhanced solar cooling is a scalable and sustainable solution for data centers in water-stressed regions.
References
[1] Alkrush, A., Mohamed, S., Abdelrehim, O., & Hegazi, A. A. (2024). Data centers cooling: A critical review of techniques, challenges, and energy saving solutions. International Journal of Refrigeration, 160, 246-262. https://doi.org/10.1016/j.ijrefrig.2024.02.007
[2] Almaraz Almaraz, V., Méndez Lagunas, L. L., & Rodríguez Ramírez, J. (2024). Potencial de la energía solar para aplicaciones en la industria alimentaria en los PODEBIS. Tendencias en Energías Renova-bles y Sustentabilidad, 3(1), 226-230. https://doi.org/10.56845/terys.v3i1.219
[3] Amiri, L., Madadian, E., Bahrani, N., & Ghoreishi-Madiseh, S. (2021). Techno-Economic Analysis of Waste Heat Utilization in Data Centers: Application of Absorption Chiller Systems. Energies, 14(9), 2433. https://doi.org/10.3390/en14092433
[4] Arroyo, J., Manna, C., Spiessens, F., & Helsen, L. (2022). Reinforced model predictive control (RL-MPC) for building energy management. Applied Energy, 309, 118346. https://doi.org/10.1016/j.apenergy.2021.118346
[5] Azarifar, M., Arik, M., & Chang, J.-Y. (2024). Liquid cooling of data centers: A necessity facing chal-lenges. Applied Thermal Engineering, 247, 123112. https://doi.org/10.1016/j.applthermaleng.2024.123112
[6] Bakar Siddik, M., Shehabi, A., & Marston, L. (2021). The environmental footprint of data centers in the United States. Environmental Research Letters, 16. https://doi.org/10.1088/1748-9326/abfba1
[7] Borkowski, M., & Piłat, A. K. (2022). Customized data center cooling system operating at significant outdoor temperature fluctuations. Applied Energy, 306, 117975. https://doi.org/10.1016/j.apenergy.2021.117975
[8] Colomina Saló, C., & Galceran-Vercher, M. (2024). Las otras geopolíticas de la inteligencia artificial. Revista CIDOB d’Afers Internacionals, 138, 27-50. https://doi.org/10.24241/rcai.2024.138.3.27
[9] Hosseinionari, H., & Seethaler, R. (2024). The integration of Model Predictive Control and deep Rein-forcement Learning for efficient thermal control in thermoforming processes. Journal of Manufactur-ing Processes, 115, 82-93. https://doi.org/10.1016/j.jmapro.2024.01.085
[10] Kwame Nti, E., Cobbina, S. J., Attafuah, E. E., Opoku, E., & Amoah Gyan, M. (2022). Environmental sustainability technologies in biodiversity, energy, transportation and water management using artifi-cial intelligence: A systematic review. Sustainable Futures, 4, 100068. https://doi.org/10.1016/j.sftr.2022.100068
[11] Milidonis, K., Blanco, M. J., Grigoriev, V., Panagiotou, C. F., Bonanos, A. M., Constantinou, M., ... & Asselineau, C. A. (2021). Review of application of AI techniques to Solar Tower Systems. Solar Energy, 224, 500-515. https://doi.org/10.1016/j.solener.2021.06.009
[12] Reza Maghami, M., & Mutambara, A. G. (2023). Challenges associated with Hybrid Energy Systems: An artificial intelligence solution. Energy Reports, 9, 924-940. https://doi.org/10.1016/j.egyr.2022.11.195
[13] Sartori, V., Vidwans, A., & Bakhar, T. (2021). Data center cooling efficiency and water sustainability. Journal of Thermal Science, 30, 112-124.
[14] Temiz, M., & Dincer, I. (2022). A newly developed solar-based cogeneration system with energy stor-age and heat recovery for sustainable data centers: Energy and exergy analyses. Sustainable Energy Technologies and Assessments, 52, 102145. https://doi.org/10.1016/j.seta.2022.102145
[15] Zhang, Y., Zhao, Y., Dai, S., Nie, B., Ma, H., Li, J., ... & Ding, Y. (2022). Cooling technologies for data centres and telecommunication base stations – A comprehensive review. Journal of Cleaner Produc-tion, 334, 130280. https://doi.org/10.1016/j.jclepro.2021.130280
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Applied Sciences and Radiation Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
