1. Introduction

Global energy use is still increasing at a breathtaking pace, fueled by industrialization, population growth, and technological change. Traditional combustion sources pose major environmental concerns (such as global warming effects) due to greenhouse gas production. Among all these, solar energy stands out as one of the most promising solutions due to its affordability and scalability (Rode et al ., 2021). The Sun is the ultimate source of energy for the Earth, and it drives the natural processes. It serves as the fundamental element for renewable energy systems (Mammadov et al ., 2022). Solar energy faces some important disadvantages such as intermittency, variability, and storage requirements. Solar energy stops working at night and weakens in poor weather. It is highly variable because of seasonal and daily fluctuations. In order to deploy solar energy, uninterrupted sunlight and a large panel area are required (Stevanović et al ., 2022). There are various Thermal Energy Storage (TES) technologies available in the market, such as sensible storage, latent storage, and thermochemical storage (Elkhatat & Al-Muhtaseb, 2023). Each of them offers advantages as well as disadvantages for solar energy systems. AI applications in renewable energy include forecasting solar and wind power, optimizing the power conversion, and grid integration (Albogamy et al ., 2022). Artificial Technologies offers the opportunity to optimize and integrate TES within solar energy systems by enhancing efficiency and enabling smarter control.

2. Literature Review

The search by (Wickramasinghe & Zhang, 2022) suggests that the TES technologies primarily fall into three major categories that are Sensible Heat Storage (SHS) systems, Latent Heat Storage (LHS) systems, and Thermochemical Heat Storage (THS) systems. SHS operates based after storage of thermal energy by cooling or heating a medium. This is mainly two types of storage sensible liquid storage and sensible solid storage. The concept of LHS is based on storage or release of the thermal energy while the source storage undergoes a phase change at constant temperature. These are derived from the PCM (Phase Change Materials) and play a significant role in performance on solar application, as far as waste heat utilization is considered. The LHS system may be divided into two large groups, and one of them is types of phase change or storage material (Khademi et al., 2022). According to the research done by (Kazancı et al., 2021), the storage density of Thermochemical Heat Storage (THS) materials is much higher, around 8 to 10 times higher than SHS systems, as well as approximately two times higher when compared to Latent Heat Storage (LHS) materials. The THS can thus be classified into sorption driven energy storage and Reversible Thermochemical reaction-based energy storage.

According to (Han et al., 2020) Molten salts are amongst the most highly employed medium and high temperature. Thermal Energy Storage (TES) materials due to their good melting point, low-cost material availability and a good thermal stability. Connected apparatuses such as the Organic Rankine Cycle (ORC) configurations and heat transfer fluids (HTFs) collect thermal energy on the solar collector to molten salt tanks, where it is stored. This allows easy storage of solar heat and further utilization to create power and this is cost effective in large systems of solar thermal power. As researched by (Yang et al., 2021), Phase Change Materials (PCMs) are an important part of the latent heat storage systems. This is extensively researched on the basis of its capacity of storing and offloading huge quantities of thermal energy nearly at continuous temperatures. These resources are very important because they can easily trap heat during the sun rays and dispel it when the sun is down or absent. The use of PCMs can further be streamlined forming a union with AI.

According to the research by (Mehraj et al., 2024), energy loss during the retrieval and storage of TES systems includes the degradation of materials and thermal conductivity also deteriorates the performance with time. Also, integrating TES into wide scale power grids is challenging and costly and demand sophisticated control methods. (Brahma & Wadhvani, 2020) reported that forecasting solar irradiance became particularly important in promoting the economic value and effective integration of solar electricity into the market. It conserves the credibility of the solar as one of the primary sources of green energy. The paper emphasizes that machine learning (ML) techniques along with data available in several places can produce powerful models to forecast the daily amount of solar energy. Use of AI and ML models and predictive maintenance in order to detect possible failure at earlier stage (Khalil & Rostam, 2024). Instantaneous control of TES of solar systems is facilitated through online optimization of charging and discharging cycles. Using AI and sophisticated learning algorithms, flow of energy can be controlled according to their need. Consistent with the findings of (Bharatee et al., 2022), a system known as HESS (Hybrid Energy storage System) by integrating Thermochemical Energy Storage (TES) and batteries is an approach to ensuring reliability, efficiency and the flexibility of solar energy with divergent complementary storage technologies.

However, research advances of TES technologies in commercial and practical applications are seriously unbalanced. The majority of investigations are concerned with simulations, laboratory experiments and theoretical models. Material decomposition and integration issues are stressed in these works however few industrial applications have been realized. And this is usually carried out under some sort of control and which does not even begin to address the multi-dimensional nature of what actually happens on the ground.

3. Methodology

The AI-optimised thermal energy storage in the solar system research is systematically reviewed by means of bibliometric method. A search in the Scopus database was conducted to Nov 2000–2024. Performance and science mapping analysis were implemented in the bibliometric software Bibliometric in R including its interface tool Biblioshiny. This investigation on annual scientific output, leading journals, contributing authors and countries and science mapping including keyword co-occurrence analysis, thematic development and conceptual structures. Methods to make visualizations, for example co-word networks, trend analysis and thematic maps were developed to mirror the intellectual and conceptual landscape of the field. This mixed-method bibliometric approach allowed to combine qualitative interpretation including emerging themes and research gaps with quantitative insights.

3.1. Rationale for Choosing Bibliometric Analysis

Bibliometric analysis was selected as it is an objective, repeatable, data-driven means of mapping the research landscape of a relatively new interdisciplinary field. AI-enhanced TES integrates diverse areas such as artificial intelligence, renewable energy, materials science, and thermodynamics making traditional literature reviews insufficient to capture its complexity. Bibliometric mapping helps to:

1. Identify research hotspots such as machine learning, solar energy, nanomaterials.
2. Trace thematic evolution from early thermodynamics and nanoparticles toward AI-based TES optimization.
3. Map collaboration networks among authors, institutions, and countries.
4. Highlight gaps where limited research exists such as sustainability integration, real-world applications.

3.2. Bibliometric Analysis (for trend/emerging research focus)

— Purpose: Identify how AI is being integrated into TES and solar energy, map emerging themes, and find knowledge gaps.

— Data: Scopus

— Tools: Biblioshiny (R).

3.3. Search term

«Artificial Intelligence» OR «AI» OR «Machine Learning» OR «Deep Learning» AND «Thermal Energy Storage» OR «TES» OR «Phase Change Material» OR «PCM» AND «Solar Energy» OR «Solar Power» OR «Concentrated Solar Power» OR «CSP» OR «Renewable Energy»

3. 4. Bibliometric Analysis

Fig. 1. Word cloud

This word cloud illustrates discussion topics on AI-empowered TES for solar energy systems. “Solar energy” is the dominant term, emphasizing its paramount position. Closely related terms are artificial intelligence, machine learning, neural networks and algorithms that reflect the increased use of AI for enhancing TES operation. They focus on technical matter such as temperature, phase transition, hot temperature, and thermal conductivity thermodynamic basis of storage. At the same time, renewable energy, sun light, water and carbon dioxide relate to sustainability and climate change. The presence of people, plants and animals indicate wider social as well as environmental implications. In general, this analysis shows that AI, thermal science and sustainability are converging.

Fig. 2. Tree map

This treemap demonstrates the importance of research topics of AI-enhanced thermal solar thermal systems for energy storage (TES). The single largest piece, solar photovoltaic (11 percent), demonstrates the central focus. Highly associated terms, such as temperature (5 percent), hot temperature (4.percent), phase transformation (3 percent) highlight the importance of heat transfer and thermodynamics. Sustainability and energy system linkage are shown by renewable energy (4 percent), carbon dioxide (2 percent) and electricity (2 percent). Other experience shows smaller blocks such as deep learning, biomass, hydrogen, porosity or even cellulose constitute the birth of new niches. Machine learning (5 %), artificial intelligence (4 %) and algorithms (3 %) all show that data-driven insights through predictive analytics are being integrated into. optimization. Altogether, the map presents an integration of fundamental solar-TES with both AI-for-inspiration and sustainable uses.

Fig. 3: Words’ frequency over time

This trend chart shows the time series of hot topics from 2019 to 2025. The most pronounced growth is of solar energy, which then grows rapidly after 2022, due to increasing international focus on the technology for solar thermal storage. Hot temp, temp and water also exhibit an overall tendency of increase, suggesting the thermodynamic basis for TES. On the AI front, artificial intelligence and machine learning continue to rise at a steady rate- notching expanding integration of data-driven methods. Renewable energy, solar and phase-transition are still strong but their growth shows less of a curve. In sum, the chart demonstrates a significant transition towards AI-enhanced solar energy storage as the leading research frontier since 2022.

Fig. 4: Trend topics

This thematic evolution map highlights how research terms have emerged and developed over time (2020–2025). Early studies (2020–2021) focused on thermodynamics, thermal conductivity, and nanoparticles, reflecting the material and heat-transfer foundations of TES. From 2022 onward, the focus shifted to temperature, hot temperature, sunlight, and electricity, marking applied studies in solar-based storage systems. By 2023–2024, machine learning, solar energy, and humans appear as central, high-impact themes, showing the rising influence of AI and human-centered sustainability. Recently (2024–2025), cellulose, agriculture, and gels emerge, indicating interest in bio-based and advanced materials for TES. Overall, the analysis shows a transition from fundamental thermal sciences to AI-driven, application-oriented, and sustainable TES research.

Fig. 5. Thematic map

The thematic map shows that AI, machine learning, phase transition materials, gels, cellulose, solar energy, and temperature control form the motor themes, representing the well-developed and highly relevant core of AI-enhanced thermal energy storage research. Basic themes, such as artificial intelligence, humans, and solar energy, are central to the field but less mature, requiring further theoretical and practical development. Niche themes, including industry, steel, and health-related topics, are specialized and well-developed in isolation but not strongly linked to the mainstream research stream. Meanwhile, emerging or declining themes like sustainable development, climate change, power plants, drug discovery, and waste management are underdeveloped and less central, indicating either early-stage exploration or decreasing research attention.

4. Artificial Intelligence in TES for Solar Energy Systems

The most significant assistance is the artificial intelligence that can help to provide the high-level programming of the storage in thermal energy storage systems and the accurate forecasts of loads. The classical models use less accurate guesses about the solar irradiance and demand, as well as conditions that are not always present. But it can also use machine algorithms to guess how much energy will be needed based on the last application, the weather, and seasonal trends (El-Azab et al., 2025). Because they can guess when to load and unload, they make TES less wasteful and easier to use. As a result, AI and solar power plants can better provide and limit TES. It also makes the systems easier for grid operators to use (Frew et al., 2021). The main problems with integrating renewable energy right now are grid stability and the movement of peak loads. AI, however, integrates strong solutions. Using reinforcement learning and optimization methods, AI systems can plan and optimize the charging schedule such that they produce electricity during peak hours and off-peak hours. Not only does it help generate the maximum amount of money for the energy producers, but it also does not need the grid to be over-strained.

Moreover, TES management is being redefined by more advanced approaches, including adaptive control, AI-based Internet of Things (IoT), and digital twins (Alnaser et al., 2024). The adaptive control systems utilize the power of AI to react to the alterations of the conditions to change the functioning of TES dynamically and guarantee the optimal efficiency. AI can synthesise information in a distributed data storage coupled with IoT-sensible sensors, which can be optimized on a global level. In the meantime, one of the technologies is the digital twin technology which creates virtual proxies of TES systems to reproduce a situation, strategies to be tested, and predict outcomes with frightening accuracy.

Recent studies, particularly those conducted between 2015 and 2025, have witnessed an acceleration in research efforts and advancements in research methodologies and approaches. Physics-informed neural networks, deep learning techniques, and reinforcement learning have emerged as prominent theories in this field. Machine learning and artificial intelligence are among the latest technologies used in designing and optimizing the operation of thermal energy storage systems for concentrated solar power plants.

Abundant studies have the integration of AI-enabled energy systems, particularly in combining solar thermal, photovoltaic, and wind power technologies, as well as battery storage using AI(Boretti, 2021).

It is worth noting that solar energy technology has emerged as a promising renewable energy solution, working in conjunction with thermal energy storage systems to address the challenges of solar power intermittency.

Furthermore, creating opportunities for distributed power generation, this technological advancement, from simple front-end grids to sophisticated deep learning algorithms, reflects the superiority and efficiency of data-driven approaches (Dudek, 2021).

The recent reviews identify a clear development from basic artificial neural networks for the 2015–2018 to state-of-the-art deep learning, physics-informed neural networks, and reinforcement learning approaches for the period 2015–2025. Deep learning architectures, like CNNs and RNNs have been used to capture thermal system spatial and temporal trends. Rodriguez has been studying the classification and modeling methods for power-to-heat and thermal energy storage, noting that empirical model learning, artificial neural networks, CNNs, and RNN-based models are being used for TES systems with phase change materials (Prieto et al., 2018).

In general, the applications of neural networks focus on energy storage and release rates, temperature distributions, and predicting transient thermal performance. Studies that dealt with the Multi-Layer Perceptron (MLP) technique enabled rapid evaluation of design alternatives, unlike when numerical simulation was used for multi-objective optimization, which was computationally prohibitive. MLP was employed for predicting total melting time of cylindrical encapsulated phase change materials, obtaining a 75 % increase in forecast accuracy in mean absolute percentage error(İzgi, 2024).

Other developments in that field include the physics-informed neural networks (PINNs) that signify a notable methodological progression, integrating physical principles and governing equations as constraints in the neural network training procedure. A PINN-based method was designed for PCM-based thermal energy storage that works with energy exchangers that are coated with desiccant. The largest difference between the method and the experimental findings was (±7.8 %)(Priyadarshi et al., 2024).

One of the leading applications of machine learning is in the sustainability of phase change materials (PCMs). Studies have shown that using machine learning methods improves the operating conditions of TES systems, reduces wear, and increases reliability. By integrating machine learning with Carbon Footprint (CFT), supercooling for LiNO₃·3H₂O was lowered to 1°C with less than 6 % energy storage capacity degradation across 800 cycles. Therefore, experiments have proven that using intelligent thermal management helps extend system lifespan and maintain good performance (Priyadarshi et al., 2024).

Training models employing transient PCM-temperature data or transient surface-temperature data that achieve prediction errors of around 5 % of total melting time throughout the final phases (Chuttar & Banerjee, 2021).

As a result, the surface-temperature technique offers practical advantages, including lower sensor prices, increased reliability, and easier deployment. As well as the incorporation of machine learning with MPC helps fundamental difficulties such as model building, computing efficiency, and adaptation to changing system features (Bellan et al., 2015), (Liang & Li, 2019), (Taheri et al., 2022).

Other research that has discussed methods of outlet temperature regulation in parabolic trough solar fields is the study conducted by Himour et al. comparing nonlinear neural predictive control and infinite-gain neural predictive control, where the infinite-gain neural predictive control study outperformed four types of nonlinear predictive control, emphasizing the importance of adaptive gain scheduling for systems with changing operating conditions (Himour et al., 2023).

Despite the advancements in the performance and applications of AI-powered thermal storage devices, several technical challenges remain. Machine learning models are a major concern due to limitations in generalizing to system configurations or operating states, or because of material outside the scope of the training data. Physics-based neural networks have partially addressed this limitation by incorporating physical constraints; purely data-driven models may exhibit poor generalization.

5. Emerging Trends

Hybrid Energy Storage Systems (HESS) is a promising solution to solve the challenges of PV cells. On the other hand, by combining several storage technologies which complement each property, HESS prolongs the life and improves the stability of the system (Sikha & Popov, 2004). HESS is composed of two elementary storage large high-energy storage (HES) and high-power storage (HPS). HES is developed to satisfy the long-term energy requirements (Sutikno et al., 2022). AI-based predictive control for multi objective optimization is fundamental to handle the solar energy intermittency and uncertain consumption profiles. This method provides a trade-off between efficiency and cost and reliability, therefore an optimal use of solar energy (Peters & Kamrul, 2025). Solar thermal energy systems have a great potential benefit from nanotechnology and advanced materials. Some forms include Nanofluids, Carbon Nanotubes (CNTs), and Phase Change Material (PCM) Composites (Ghasemzadeh & Shayan, 2020). DTT makes it possible to simulate and optimize TES again based on real-time technology; in the sense that one can create a digital twin of the physical plants. Digital twins that predict performance Through ongoing, AI-powered Analytic Monitoring. In smart grids and microgrids, AI supports the distributed control of TES systems. Besides, AI is used for lifecycle and sustainable measurement of TES systems considering material degradation, energy consumption, carbon footprint and long-term benefits (Belik & Rubanenko, 2023).

6. Future Prospects

There is a great potential for AI-enhanced TES in solar systems in the future, but a number of obstacles should be overcome. The major challenges are the upfront expenses of sophisticated TES resources and systems, the inaccessible nature of high-quality datasets to train AI models, and the complexity of computation resources to optimize the use of AI models in real-time and predictive analytics. The barriers need to be overcome to allow economically viable and reliable implementation at the industrial and grid scale. The ability to manage information and follow ethical considerations in AI-based decision-making is paramount to provide transparency, provide fairness, and ensure the responsible conduct of autonomous energy systems.

AI-enhanced TES can help decrease the carbon footprint by maximizing the use of renewable energy sources, reducing the use of fossil fuels, and increasing efficiency in energy use. To achieve this potential, interdisciplinary studies have to be undertaken that integrate AI with materials science and energy engineering to come up with new storage materials, predictive models, and control algorithms.

7. Discussion

Heliogen is a US-based enterprise that focuses on the line of artificial intelligence-driven concentrated solar thermal (CST) technology. It collects sunbeams with computerized heliostats monitored daily to produce high-temperature heat, later stored through thermal energy storage to be used on command or to go through industrial processes such as cement and steel. Scalability Projects, such as the Capella Project, represent practical scalability. AI and computer vision can be used to optimize the system efficiency and heliostat alignment (“Heliogen Concludes Capella Demonstration, Advancing Next-Generation Concentrated Solar Technology,” n.d.). The aspect that makes Heliogen relevant to this paper is the technology that does not use fossil fuels, promotes decarbonization, and is an example that has demonstrated how AI can be combined with renewable energy sources.

Solar Thermoelectricity through Advanced Latent heat storage (STEALS) patent has developed a new method which combines the latent heat storage with thermoelectric systems to transform a portion of stored thermal energy to electricity. Using phase change materials (PCMs), makes solar power smoother and more efficient by smoothly optimizing solar power and aligning with AI-driven optimization of inexpensive TES to a sustainable and scalable renewable power provider (Olsen et al., 2017).

Smart technologies that are powered by AI improve TES more than traditional TES through predictive analytics, online optimization, and predictive control. In comparison to the traditional systems, which operate on a fixed schedule and manual control, AI-assisted TES can continuously change charge and discharge cycles in response to the solar irradiance forecast, energy demand, and grid conditions.

Despite the advancements in the performance and applications of AI-powered thermal storage devices, several technical challenges remain. Machine learning models are a major concern due to limitations in generalizing to system configurations or operating states, or because of material outside the scope of the training data. Physics-based neural networks have partially addressed this limitation by incorporating physical constraints; purely data-driven models may exhibit poor generalization.

Furthermore, the unavailability of high-quality training data and the difficulty in collecting it for thermal storage methods or materials negatively impact the accuracy of high-performance machine learning models. Data quality problems, such as sensor noise, missing values, and measurement errors, also significantly reduce model performance. Therefore, costly pilot campaigns or high-fidelity simulations are required to obtain better predictions.

8. Conclusion

The primary problems with solar energy, including intermittency, variability, and storage limitations, could be resolved with AI-enhanced thermal energy storage (TES). AI integration with predictive analytics, adaptive control, and advanced materials discovery ensures that energy storage is more dependable, cost-effective, and efficient. Future scalability opportunities are consistently represented by recent advancements such as digital twin technologies, nanotechnology, and hybrid energy storage. Future research on interdisciplinary collaboration between energy engineering, materials science, and artificial intelligence is required. Open-source datasets, sustainable materials, and AI-driven energy regulations must all be incorporated into the policy architecture, and Marie of Preciosity should be given real-world pilots to test the solutions based on AI-generated TES.

References:

1. Albogamy, F. R., Khan, S. A., Hafeez, G., Murawwat, S., Khan, S., Haider, S. I., Basit, A., & Thoben, K.-D. (2022). Real-time energy management and load scheduling with renewable energy integration in smart grid. Sustainability, 14(3), 1792.
2. Alnaser, A. A., Maxi, M., & Elmousalami, H. (2024). AI-powered digital twins and internet of things for smart cities and sustainable building environment. Applied Sciences, 14(24), 12056.
3. Belik, M., & Rubanenko, O. (2023). Implementation of digital twin for increasing efficiency of renewable energy sources. Energies, 16(12), 4787.
4. Bellan, S., Gonzalez-Aguilar, J., Romero, M., Rahman, M. M., Goswami, D. Y., & Stefanakos, E. K. (2015). Numerical investigation of PCM-based thermal energy storage system. Energy Procedia, 69, 758–768.
5. Bharatee, A., Ray, P. K., Subudhi, B., & Ghosh, A. (2022). Power management strategies in a hybrid energy storage system integrated AC/DC microgrid: A review. Energies, 15(19), 7176.
6. Boretti, A. (2021). Integration of solar thermal and photovoltaic, wind, and battery energy storage through AI in NEOM city. Energy and AI, 3, 100038.
7. Brahma, B., & Wadhvani, R. (2020). Solar irradiance forecasting based on deep learning methodologies and multi-site data. Symmetry, 12(11), 1830.
8. Chuttar, A., & Banerjee, D. (2021). Machine learning (ML) based thermal management for cooling of electronics chips by utilizing thermal energy storage (TES) in packaging that leverages phase change materials (PCM). Electronics, 10(22), 2785.
9. Dudek, G. (2021). A constructive approach to data-driven randomized learning for feedforward neural networks. Applied Soft Computing, 112, 107797.
10. El-Azab, H.-A. I., Swief, R. A., El-Amary, N. H., & Temraz, H. K. (2025). Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors. Scientific Reports, 15(1), 9252.
11. Elkhatat, A., & Al-Muhtaseb, S. A. (2023). Combined “renewable energy–thermal energy storage (RE–TES)” systems: A review. Energies, 16(11), 4471.
12. Frew, B., Sergi, B., Denholm, P., Cole, W., Gates, N., Levie, D., & Margolis, R. (2021). The curtailment paradox in the transition to high solar power systems. Joule, 5(5), 1143–1167.
13. Ghasemzadeh, F., & Shayan, M. E. (2020). Nanotechnology in the service of solar energy systems. In Nanotechnology and the Environment. IntechOpen. https://books.google.com/books?hl=en&lr=&id=tGwtEAAAQBAJ&oi=fnd&pg=PA59&dq=Ghasemzadeh,+F.,+ %26+Shayan,+M.+E.+(2020).+Nanotechnology+in+the+service+of+solar+energy+systems.+In+Nanotechnology+and+the+Environment.+IntechOpen.+https://www.intechopen.com/chapters/73145 %3Ffbclid %3DIwAR38DqUCkcKDheC1U2J&ots=x-zypw_H1o&sig=3LeR1f46Pz2lxj0-FvWcZ3t4VUo
14. Han, D., Lougou, B. G., Xu, Y., Shuai, Y., & Huang, X. (2020). Thermal properties characterization of chloride salts/nanoparticles composite phase change material for high-temperature thermal energy storage. Applied Energy, 264, 114674.
15. Heliogen Concludes Capella Demonstration, Advancing Next-Generation Concentrated Solar Technology. (n.d.). Heliogen. Retrieved February 18, 2026, from https://www.heliogen.com/press-releases/heliogen-concludes-capella-demonstration-advancing-next-generation-concentrated-solar-technology/
16. Himour, Y., Tadjine, M., & Boucherit, M. S. (2023). Nonlinear and infinite gain scheduling neural predictive control of the outlet temperature in a parabolic trough solar field: A comparative study. Engineering Applications of Artificial Intelligence, 126, 106862.
17. İzgi, B. (2024). Machine learning predictions and optimization for thermal energy storage in cylindrical encapsulated phase change material. International Journal of Energy Studies, 9(2), 199–218.
18. Kazancı, S., Qasim, O., Bahauldın, Y., & Samancı, A. (2021). Thermochemical Heat Storage System for Domestic Application: A Review. Renewable Energy Sources Energy Policy and Energy Management, 2(3), 1–11.
19. Khademi, A., Shank, K., Mehrjardi, S. A. A., Tiari, S., Sorrentino, G., Said, Z., Chamkha, A. J., & Ushak, S. (2022). A brief review on different hybrid methods of enhancement within latent heat storage systems. Journal of Energy Storage, 54, 105362.
20. Khalil, A. F., & Rostam, S. (2024). Machine learning-based predictive maintenance for fault detection in rotating machinery: A case study. Engineering, Technology & Applied Science Research, 14(2), 13181–13189.
21. Liang, X., & Li, Y. (2019). Transient analysis and execution-level power tracking control of the concentrating solar thermal power plant. Energies, 12(8), 1564.
22. Mammadov, N. S., Ganiyeva, N. A., & Aliyeva, G. A. (2022). Role of renewable energy sources in the world. Journal of Renewable Energy, Electrical, and Computer Engineering, 2(2), 63–67.
23. Mehraj, N., Mateu, C., & Cabeza, L. F. (2024). Use of artificial intelligence methods in designing thermal energy storage tanks: A bibliometric analysis. Journal of Energy Storage, 97, 112794.
24. Olsen, M. L., Toberer, E. S., Ginley, D. S., Parilla, P. A., Warren, E. L., Martinez, A. D., Rea, J. E., Hardin, C. L., Oshman, C. J., & Siegel, N. P. (2017). Solar thermoelectricity via advanced latent heat storage (United States Patent No. US20170102192A1). https://patents.google.com/patent/US20170102192A1/en#citedBy
25. Peters, I., & Kamrul, G. (2025). Applications AI-driven solar energy management system for smart grids using predictive analytics and adaptive control. J. Quantum Nano-Green Environ. Syst, 1(1), 14–24.
26. Prieto, C., Rodriguez, A., Patino, D., & Cabeza, L. F. (2018). Thermal energy storage evaluation in direct steam generation solar plants. Solar Energy, 159, 501–509.
27. Priyadarshi, G., Murali, C., Agarwal, S., & Naik, B. K. (2024). Parametric investigation and optimization of phase change material-based thermal energy storage integrated desiccant coated energy exchanger through physics informed neural networks oriented deep learning approach. Journal of Energy Storage, 80, 110231.
28. Rode, A., Carleton, T., Delgado, M., Greenstone, M., Houser, T., Hsiang, S., Hultgren, A., Jina, A., Kopp, R. E., & McCusker, K. E. (2021). Estimating a social cost of carbon for global energy consumption. Nature, 598(7880), 308–314.
29. Sikha, G., & Popov, B. N. (2004). Performance optimization of a battery–capacitor hybrid system. Journal of Power Sources, 134(1), 130–138.
30. Stevanović, S., Stevanović, S., & Živković, R. (2022). Advantages and disadvantages of solar energy production and use. https://agris.fao.org/search/en/providers/122550/records/68b6dbff68d9e6806700a99b
31. Sutikno, T., Arsadiando, W., Wangsupphaphol, A., Yudhana, A., & Facta, M. (2022). A review of recent advances on hybrid energy storage system for solar photovoltaics power generation. Ieee Access, 10, 42346–42364.
32. Taheri, M., Mohammadian, A., Ganji, F., Bigdeli, M., & Nasseri, M. (2022). Energy-based approaches in estimating actual evapotranspiration focusing on land surface temperature: A review of methods, concepts, and challenges. Energies, 15(4), 1264.
33. Wickramasinghe, Y. W., & Zhang, L. (2022). Life cycle assessment of Sensible, latent and thermochemical thermal energy storage systems for climate change Mitigation–A systematic review. Journal of Energy Technologies and Policy.
34. https://www.researchgate.net/profile/YasaraWickramasinghe/publication/365944654_Life_Cycle_Assessment_of_Sensible_Latent_and_Thermochemical_Thermal_Energy_Storage_Systems_for_Climate_Change_Mitigation_-A_Systematic_Review/links/638989fe7d9b40514e086a65/Life-Cycle-Assessment-of-Sensible-Latent-and-Thermochemical-Thermal-Energy-Storage-Systems-for-Climate-Change-Mitigation-A-Systematic-Review.pdf
35. Yang, T., King, W. P., & Miljkovic, N. (2021). Phase change material-based thermal energy storage. Cell Reports Physical Science, 2(8). https://www.cell.com/cell-reports-physical-science/fulltext/S2666–3864(21)00251–4.