Citation :

Okwe Gerald, Mustapha Abdullahi, Uneze Ijeoma, Inyama Kelechi, "AI-Driven Multi-Objective Optimization of Floating Photovoltaic-Thermal Systems with MATLAB-HOMER Pro Validation," International Journal of Computer Science and Engineering, vol. 13, no. 6, pp. 12-21, 2026. Crossref, https://doi.org/10.14445/23488387/IJCSE-V13I6P102

Abstract

The shift towards renewable energy has increased interest in Floating Photovoltaic Thermal (FPVT) systems for their capacity to produce electricity and heat with a low land footprint. However, improvement of the FPV T scheme is difficult due to the multi-dimensional relationship between the electrical, thermal, and economic aspects. This research introduces an Artificial Intelligence (AI)-based multi-objective optimization and techno-economic analysis approach for FPV T systems based on ANN, GA, PSO, and RL System models. ANN surrogate predictors and optimization algorithms were implemented in MATLAB. The outcome of simulation results at STC yielded an active power and thermal output of 64.8 kW and 260 kW, respectively, resulting in a useful energy output of 324.8 kW. The conventional PV baseline system, however, produced 56.2 kW of output, at 15.3% gain in performance, higher than 480% gain in total energy utilization with the addition of the thermal recovery. The ANN predictor was highly accurate with an error of less than 0.01%, and the GA and PSO algorithms produced consistent optimal results. The reinforcement learning algorithm effectively controlled the flow rate of the coolant to achieve the desired outlet temperatures. The findings showed that a hybrid AI approach enhances electrical and thermal efficiency, speeds up the optimization process with surrogate modeling, and recommends low LCOE and attractive NPC configurations. The techno-economic modeling with HOMER Pro also validates the AI-optimized solutions. This technology advances renewable energy research through the use of multi-objective optimization, surrogate modeling, and techno-economic validation.

Keywords

Artificial intelligence, Floating photovoltaic-thermal, Genetic algorithm, HOMER-Pro, Reinforcement learning.

References

1. Ehsanolah Assareh et al., “Performance Evaluation and Optimization of a Photovoltaic/Thermal (PV/T) System According to Climatic Conditions,” Energies, vol. 15, no. 20, pp. 1-14, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Seyed Morteza Javadpour et al., “Multi-Objective Optimization of Hybrid Photovoltaic-Thermal (PVT) System with Nano-Enhanced Phase Change Material using SVM Modeling,” Applied Thermal Engineering, vol. 283, 2026.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Patel Nirmal Rajnikant, and Ritu Khanna, “Optimization Techniques in Renewable Energy Systems: A Mathematical Approach to Efficiency,” World Journal of Advanced Engineering Technology and Sciences, vol. 15, no. 3, pp. 1308-1315, 2025.
   [CrossRef] [Publisher Link]
4. S. Sankarananth et al., “AI-Enabled Metaheuristic Optimization for Predictive Management of Renewable Energy Production in Smart Grids,” Energy Reports, vol. 10, pp. 1299-1312, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Hassan A. Hameed Al-Hamzawi, and Ali S.Abed Al Sailawi, “AI-Assisted Optimization of a Hybrid Renewable Energy System for Semi-Arid Regions using Multi-Source Engineering Data,” Energy 360, vol. 5, pp. 1-13, 2026.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Mamoon Ur Rashid et al., “Techno-Economic Analysis of Grid-Connected Hybrid Renewable Energy System for Remote Areas Electrification using Homer Pro,” Journal of Electrical Engineering and Technology, vol. 17, no. 2, pp. 981-997, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Rasha S. Aweid, Omer K. Ahmed, and Sameer Algburi, “Performance of Floating Photovoltaic/Thermal System: Experimental Assessment,” International Journal of Energy Research, vol. 46, no. 15, pp. 24229-24242, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Samuel Kofi Ahiave Dzamesi et al., “Comparative Performance Evaluation of Ground-Mounted and Floating Solar PV Systems,” Energy for Sustainable Development, vol. 80, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Jalal Alsarraf et al., “Application of PSO–ANN Modelling for Predicting the Exergetic Performance of a Building Integrated Photovoltaic/Thermal System,” Engineering with Computers, vol. 36, no. 2, pp. 633-646, 2019.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Ali H.A. Al-Waeli et al., “Artificial Neural Network Modeling and Analysis of Photovoltaic/Thermal System based on the Experimental Study,” Energy Conversion and Management, vol. 186, pp. 368-379, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]    
11. A.H. Elsheikh, and M. Abd Elaziz, “Review on Applications of Particle Swarm Optimization in Solar Energy Systems,” International Journal of Environmental Science and Technology, vol. 16, no. 2, pp. 1159-1170, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Burcu Adıgüzel Mercangöz, Applying Particle Swarm Optimization: New Solutions and Cases for Optimized Portfolios, 1^st ed., Springer Cham, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Omar Zebet al., “Deep Reinforcement Learning-Driven Robust Adaptive Control for Hybrid Energy Storage in Synrm-based Light Electric Vehicle Drives,” IEEE Access, vol. 14, pp. 1439-1467, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Nga Thi-Thuy Vu, Ha Duc Nguyen, and Anh Tuan Nguyen, “Reinforcement Learning-based Adaptive Optimal Fuzzy MPPT Control for Variable Speed Wind Turbine,” IEEE Access, vol. 10, pp. 95771-95780, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Antonio Gagliano et al., “Analysis of the Performances of a Building-Integrated PV/Thermal System,” Journal of Cleaner Production, vol. 320, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Tao Zhang, Wenhua Li, and Rui Wang, “Surrogate-Assisted Multimodal Multi-Objective Optimization for Hybrid Renewable Energy System,” Complex and Intelligent Systems, vol. 9, no. 4, pp. 4075-4087, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Chaima Ghanjati, and Slim Tnani, “Optimal Sizing and Energy Management of a Stand-Alone Photovoltaic/Pumped Storage Hydropower/Battery Hybrid System using Genetic Algorithm for Reducing Cost and Increasing Reliability,” Energy and Environment, vol. 34, no. 6, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Marine Cauz et al., “Reinforcement Learning for Efficient Design and Control Co-Optimisation of Energy Systems,” arXiv Preprint, pp. 1-14, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Ramin Cheraghi, and Mohammad Hossein Jahangir, “Multi-Objective Optimization of a Hybrid Renewable Energy System Supplying a Residential Building using NSGA-II and MOPSO Algorithms,” Energy Conversion and Management, vol. 294, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
20. David Ross-Hopley, Lord Ugwu, and Hussameldin Ibrahim, “Review of Techno-Economic Analysis Studies using HOMER Pro Software,” Engineering Proceedings, vol. 76, no. 1, pp. 1-8, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]