Prediction of heat transfer characteristics and energy efficiency of a PVT solar collector with corrugated-tube absorber using artificial neural network and group method data handling models

Li, Lei; Hassan, Waqed H.; Mohammed, Abrar A.; Montufar, Paul; AL-maamori, Zainab M.; Sultan, Abbas J.; Salahshour, Soheil; Esmaeili, Shadi

doi:10.1016/j.icheatmasstransfer.2024.107829

International Communications in Heat and Mass Transfer

2024

DOI: 10.1016/j.icheatmasstransfer.2024.107829

|View full text |Cite

Prediction of heat transfer characteristics and energy efficiency of a PVT solar collector with corrugated-tube absorber using artificial neural network and group method data handling models

Lei Li,

Waqed H. Hassan,

Abrar A. Mohammed

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An AI-Based Approach for the Assessment of the Lost Efficiencies of a PVT Plant

Aneli,

Tina,

Hajji

et al. 2024

Energy Exploration & Exploitation

View full text Add to dashboard Cite

Developments in solar technologies are making it possible to effectively exploit solar resources in several regions of Earth, including urban areas and regions with low solar irradiation intensity. Since power and heat are fundamental necessities for all users, f users, photovoltaic/thermal (PVT) technology has emerged as a promising solution for sustainably-minded communities. Generally, the output electrical generation of a PVT system depends on the intermittent solar insolation, efficiency, and operating temperature of the photovoltaic (PV) cell. At the same time, thermal power also depends on collector thermal insulation and the temperature difference with the surroundings. Consequently, it is essential to predict and diagnose the PVT system's power to optimally utilize the collected solar energy. In recent years, intelligent techniques and approaches have been introduced to evaluate the performance of PVT systems. Artificial Intelligence (AI)-based methods have become prominent due to their capability of producing accurate predictions against the uncertainty and nonlinearity phenomena. This work presents an innovative application of AI for both the forecasting and the diagnostic of the performance of a PVT plant. The analyses performed are based on the experimental investigation conducted on the PVT system installed at the University of Catania campus. The outcomes of this study showed that the AI accurately predicted photovoltaic energy production and consequently, it can be used to detect periods of performance loss. From the test carried out in the real plant, it was seen that the use of the AI allows for prompt identification of malfunctioning in PVT plants can translate into avoid loss of over 5% of the electricity produced, and the total loss of thermal energy.

show abstract

An AI-Based Approach for the Assessment of the Lost Efficiencies of a PVT Plant

Aneli,

Tina,

Hajji

et al. 2024

Energy Exploration & Exploitation

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Prediction of heat transfer characteristics and energy efficiency of a PVT solar collector with corrugated-tube absorber using artificial neural network and group method data handling models

Cited by 1 publication

References 37 publications

An AI-Based Approach for the Assessment of the Lost Efficiencies of a PVT Plant

An AI-Based Approach for the Assessment of the Lost Efficiencies of a PVT Plant

Contact Info

Product

Resources

About