AI-Powered Optimization of Solar Absorbers: Enhancing Industrial Thermal Energy Harvesting Through Deep Learning

Kuzhaloli S; S. Sivakumar; M. Bheemalingaiah; P. Suresh; P. Balaramesh; Sunanda Kakroo

doi:10.63278/mme.v31i1.1238

Authors

Kuzhaloli S Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
S. Sivakumar Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology, Avadi, Chennai, Tamilnadu, India.
M. Bheemalingaiah Department of Computer Science and Engineering, J.B. Institute of Engineering and Technology, Hyderabad, India
P. Suresh Department of Mechatronics Engineering, Sona college of Technology, Salem, India.
P. Balaramesh Department of Science and Humanities, R.M.K.Engineering College, RSM Nagar, Kavaraipettai, Gummidipoondi (TK), Tiruvallur (DT), India
Sunanda Kakroo Department of Computer Science, Accurate Institute of Management and Technology, Greater Noida, India

DOI:

https://doi.org/10.63278/mme.v31i1.1238

Keywords:

Deep Learning Model, Solar Absorber, Thermal Energy Harvesting, Solar Radiation.

Abstract

Thermal energy harvesting is a recent attention due to the possibility of harnessing the sun to generate sustainable energy. The solar collector is essential components of this process because it turns the sun's rays into heat. A solar deep learning model (SDLM) is used to improve the efficiency of solar absorber in current industrial settings for collecting thermal energy. Several devices in this model gather information over time about things like moisture, speed of the wind, temperature, pressure of air as well as sun energy. This information is utilized for ML program that can predict the energy of a certain panel. For the proposed SDLM, the thresholds were 75.05 percent for absorption prevalence, 69.89 percent for absorption discovery, 81.41 percent for absorption omission, 90.82 percent for crucial success index, and 73.20 percent for threshold. To estimate the amount of thermal energy that may be gathered more precisely, the system includes other parameters like motion as well as insulation. In order to turn sunlight into heat, solar filters are employed in manufacturing. This thermal energy is crucial for many electrical systems, including heating and cooling systems, and industrial activities. Before investing in solar absorbers, companies may use the SDLM to calculate their prospective thermal energy production.

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