A review of recent developments in the application of machine learning in solar thermal collector modelling

Vakili, M.; Salehi, Seyedeh Akram

doi:10.1007/s11356-022-24044-y

Cited by 16 publications

(3 citation statements)

References 174 publications

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“…To successfully implement artificial intelligence-based control systems, predictive maintenance algorithms, and energy management platforms, it is necessary to have robust hardware, dependable data connection, and smooth compatibility with preexisting systems and protocols. In addition, it is of the utmost importance to guarantee the cybersecurity and data privacy of solar energy systems that are enabled by artificial intelligence to protect against possible attacks and weaknesses [248]- [250].…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…261 Such high-resolution characterization tools might offer a powerful technique to capture the missed biological features that also contribute to their outstanding thermal regulation/utilization capability. Recently emerged artificial intelligence and machine learning techniques 262,263 would help speed up the process for revealing the key bioinspired features that determine the thermal energy regulation and utilization performances. To explore suitable approaches for controlled fabrication of bioinspired structures, we can also learn from nature and explore bottom-up micro-/ nanofabrication methods, 264 while developing 3D nanoprinting and other advanced high-precision fabrication techniques.…”

Section: Discussionmentioning

confidence: 99%

Biological and Bioinspired Thermal Energy Regulation and Utilization

Shi

Jiang

et al. 2023

Chem. Rev.

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The regulation and utilization of thermal energy is increasingly important in modern society due to the growing demand for heating and cooling in applications ranging from buildings, to cooling high power electronics, and from personal thermal management to the pursuit of renewable thermal energy technologies. Over billions of years of natural selection, biological organisms have evolved unique mechanisms and delicate structures for efficient and intelligent regulation and utilization of thermal energy. These structures also provide inspiration for developing advanced thermal engineering materials and systems with extraordinary performance. In this review, we summarize research progress in biological and bioinspired thermal energy materials and technologies, including thermal regulation through insulation, radiative cooling, evaporative cooling and camouflage, and conversion and utilization of thermal energy from solar thermal radiation and biological bodies for vapor/electricity generation, temperature/infrared sensing, and communication. Emphasis is placed on introducing bioinspired principles, identifying key bioinspired structures, revealing structure–property–function relationships, and discussing promising and implementable bioinspired strategies. We also present perspectives on current challenges and outlook for future research directions. We anticipate that this review will stimulate further in-depth research in biological and bioinspired thermal energy materials and technologies, and help accelerate the growth of this emerging field.

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“…The initial layer, named the input layer, accepts input data and sends the input impulses into the subsequent layer depending on the strength of connections with neurons in other layers. Neuron weight refers to the strength of each neuron's connections with other neurons, and it determines how many neurons are present in each layer as well as how many neurons were present in the layer 51 Many essential advances have been achieved with simple and cheap computer simulations. After an initial period of enthusiasm and activity in this field, a period of reluctance and notoriety has passed.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Numerical investigation and artificial brain structure-based modeling to predict the heat transfer of hybrid Ag/Au nanofluid in a helical tube heat exchanger

Abazari Bahnemiri,

Oloomi,

Mirjalily

et al. 2023

Advances in Mechanical Engineering

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In recent years, due to the low thermal coefficients of common fluids and the increase in size and cost of heat exchangers, technologies for improving heat transfer and reducing dimensions have been developed and widely used in industries such as refrigeration, cooling of processing cells, chemical industries, and more. Previously, increasing heat exchange capacity in heat exchangers was achieved by altering parameters such as boundary conditions, flow geometry, heat exchanger geometry, or changing the type of fluid. Additionally, apart from the use of nanofluids, various other operational methods can be employed to improve the thermal performance of heat exchangers. Accordingly, considering the combination of the aforementioned innovative techniques, this study presents the modeling of flow and heat transfer inside helically coiled tube heat exchangers under the flow of nanofluids containing nickel, gold, silver, and gold/silver hybrid nanoparticles using numerical and artificial intelligence methods. In this study, the effect of variations in the inner diameter of the coiled tube and the volume fraction of nanoparticles was examined. The results showed that increasing the inner diameter and volume fraction of nanoparticles leads to an increase in heat transfer coefficient and Nusselt number, while the friction factor decreases with an increase in Reynolds number and increases with an increase in diameter and volume fraction. Finally, the accuracy and validity of the model were evaluated using statistical parameters and experimental results, which showed a 99.9% level of agreement between the predicted and experimental outcomes.

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A review of recent developments in the application of machine learning in solar thermal collector modelling

Cited by 16 publications

References 174 publications

Artificial intelligence applications in solar energy

Artificial intelligence applications in solar energy

Biological and Bioinspired Thermal Energy Regulation and Utilization

Numerical investigation and artificial brain structure-based modeling to predict the heat transfer of hybrid Ag/Au nanofluid in a helical tube heat exchanger

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