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A data-driven model for a liquid desiccant regenerator equipped with an evacuated tube solar collector: Random forest regression, support vector regression and artificial neural network

Author

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  • Daghigh, Roonak
  • Arshad, Siamand Azizi
  • Ensafjoee, Koosha
  • Hajialigol, Najmeh
Abstract
The application of a solar-assisted liquid desiccant air-conditioning system equipped with evacuated tubes, focusing on the assessment and comparison of various artificial intelligence (AI) models is investigated. Specifically, Support Vector Regression (SVR), Multi-Layer Perceptron Artificial Neural Network (MLP-ANN), and Random Forest Regression (RFR) models are assessed for predicting key performance indicators: mass removal rate, efficiency, and effectiveness. Additionally, different optimizers within the Artificial Neural Network (ANN) framework—such as Adam, Stochastic Gradient Descent (SGD), and RMSprop—are systematically examined and tuned. The study encompasses the selection of the most suitable AI model for each target variable, considering parameters such as ambient temperature, solar radiation, timestamp, airflow rate, and initial solution concentration as influential factors in the modeling process. It is found that the best predictor model for effectiveness is SVR with RBF kernel. For MRR and efficiency, it is MLP-ANN with respectively AdamW and NAdam optimizers. The disparity of prediction of the MRR, efficiency and effectiveness target are respectively 0.72%, 1.07% and 0.5%, on average, indicating a precise prediction. Furthermore, including timestamps as model inputs significantly boosts accuracy, an aspect often neglected in prior research, leading to a noticeable minimum 5% rise in the R2 score.

Suggested Citation

  • Daghigh, Roonak & Arshad, Siamand Azizi & Ensafjoee, Koosha & Hajialigol, Najmeh, 2024. "A data-driven model for a liquid desiccant regenerator equipped with an evacuated tube solar collector: Random forest regression, support vector regression and artificial neural network," Energy, Elsevier, vol. 295(C).
    • Handle: RePEc:eee:energy:v:295:y:2024:i:c:s0360544224007047
    DOI: 10.1016/j.energy.2024.130932
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    References listed on IDEAS

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