Optimal 6E design of an integrated solar energy-driven polygeneration and CO2 capture system: A machine learning approach

Khani, Nastaran; Manesh, Mohammad Hasan Khoshgoftar; Onishi, Viviani C.

doi:10.1016/j.tsep.2023.101669

Cited by 9 publications

(1 citation statement)

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“…Furthermore, continued use of fossil fuels depletes resources over time. Thus, it is critical to combine fossil fuel resources with renewables and other energy storage solutions in order to lessen civilization's dependency on fossil fuels [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Model-prediction of Efficiency of a Parabolic Trough Collector Using Data-Driven Soft Computing

Nguyen,

Paramasivam

2024

COMIEN

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The study highlights the need to use suitable modeling techniques to accurately predict the efficiency of parabolic trough collectors in light of their significance in renewable energy. An examination of prediction models using the Linear Regression (LR), Support Vector Regression (SVR), and Decision Tree (DT) algorithms for the efficiency of parabolic trough collectors provides insightful information about how well they work. In terms of prediction accuracy and precision, the Decision Tree model regularly performs better than its rivals throughout the training and testing phases. What sets it apart from SVR and LR models is its ability to identify minute relationships within the data. SVR performs better than LR, although it is not as exact or accurate as the DT model. Among the three models, Linear Regression has the lowest performance, underscoring its limitations in terms of capturing non-linear relationships. Given its exceptional performance, the Decision Tree model may prove to be a crucial instrument in encouraging the design and construction of solar energy systems, hence advancing the growth of sustainable development projects and renewable energy technology.

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Section: Introductionmentioning

confidence: 99%