Advancing predictive modeling in conventional solar stills: a deep learning approach leveraging data augmentation and convolutional neural networks
[Display omitted] •Gaussian noise-based augmentation generated six synthetic samples per input, boosting data efficiency.•An optimized CNN-1D (5-128-128-128-1) was developed through systematic hyperparameter tuning.•Augmented CNN-1D achieved R2 = 0.97, RMSE = 0.04, MAE = 0.03 , outperforming baselin...
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| Veröffentlicht in: | Energy conversion and management Jg. 346; S. 120565 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
15.12.2025
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| ISSN: | 0196-8904 |
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| Abstract | [Display omitted]
•Gaussian noise-based augmentation generated six synthetic samples per input, boosting data efficiency.•An optimized CNN-1D (5-128-128-128-1) was developed through systematic hyperparameter tuning.•Augmented CNN-1D achieved R2 = 0.97, RMSE = 0.04, MAE = 0.03 , outperforming baseline CNN-1D and SVR.•Diagnostic tests confirmed homoscedastic residuals, minimal bias, and strong generalization.•Findings provide tools for predictive monitoring and optimization of solar desalination systems.
Accurate forecasting of freshwater productivity from conventional single-slope solar stills is crucial for enhancing operational efficiency and minimizing capital costs. A persistent challenge in this domain is the scarcity of experimental data, which limits the training of reliable predictive models. This study proposes a data-efficient forecasting framework that integrates a one-dimensional convolutional neural network (CNN-1D) with time-series data augmentation. Gaussian noise sampled from N(0, 0.012) was applied exclusively to the training set, generating six augmented samples per instance. Both the augmentation factor (six) and the look-back window (seven days) were selected through systematic optimization, ensuring preservation of temporal dependencies. The CNN-1D architecture comprised three convolutional layers with 128 filters, ReLU activations, a flattening stage, and a dense regression output layer. Hyperparameters—including learning rate, batch size, kernel size, and regularization strength—were fine-tuned using Tree-structured Parzen Estimator (TPE) optimization with a maximum of 50 trials, where the best-performing configuration achieved the lowest loss. Model training employed a feed-forward backpropagation algorithm with 365 daily observations to predict freshwater yield (Pstd, L/day). Benchmarking against an optimized support vector regression (SVR) model with a radial basis function kernel revealed that the augmented CNN-1D achieved superior performance (RMSE = 0.04, MAE = 0.03, OIMP = 0.97), consistently outperforming both the baseline CNN-1D and the optimized SVR. Residual analyses confirmed its robustness, minimal bias, and strong generalization across unseen data. These findings demonstrate that combining augmentation with hierarchical feature extraction enables a scalable and computationally efficient predictive tool for solar still performance, offering significant potential for sustainable freshwater management in arid and data-constrained regions. |
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| AbstractList | [Display omitted]
•Gaussian noise-based augmentation generated six synthetic samples per input, boosting data efficiency.•An optimized CNN-1D (5-128-128-128-1) was developed through systematic hyperparameter tuning.•Augmented CNN-1D achieved R2 = 0.97, RMSE = 0.04, MAE = 0.03 , outperforming baseline CNN-1D and SVR.•Diagnostic tests confirmed homoscedastic residuals, minimal bias, and strong generalization.•Findings provide tools for predictive monitoring and optimization of solar desalination systems.
Accurate forecasting of freshwater productivity from conventional single-slope solar stills is crucial for enhancing operational efficiency and minimizing capital costs. A persistent challenge in this domain is the scarcity of experimental data, which limits the training of reliable predictive models. This study proposes a data-efficient forecasting framework that integrates a one-dimensional convolutional neural network (CNN-1D) with time-series data augmentation. Gaussian noise sampled from N(0, 0.012) was applied exclusively to the training set, generating six augmented samples per instance. Both the augmentation factor (six) and the look-back window (seven days) were selected through systematic optimization, ensuring preservation of temporal dependencies. The CNN-1D architecture comprised three convolutional layers with 128 filters, ReLU activations, a flattening stage, and a dense regression output layer. Hyperparameters—including learning rate, batch size, kernel size, and regularization strength—were fine-tuned using Tree-structured Parzen Estimator (TPE) optimization with a maximum of 50 trials, where the best-performing configuration achieved the lowest loss. Model training employed a feed-forward backpropagation algorithm with 365 daily observations to predict freshwater yield (Pstd, L/day). Benchmarking against an optimized support vector regression (SVR) model with a radial basis function kernel revealed that the augmented CNN-1D achieved superior performance (RMSE = 0.04, MAE = 0.03, OIMP = 0.97), consistently outperforming both the baseline CNN-1D and the optimized SVR. Residual analyses confirmed its robustness, minimal bias, and strong generalization across unseen data. These findings demonstrate that combining augmentation with hierarchical feature extraction enables a scalable and computationally efficient predictive tool for solar still performance, offering significant potential for sustainable freshwater management in arid and data-constrained regions. |
| ArticleNumber | 120565 |
| Author | Migaybil, Hashim H. Gopaluni, Bhushan |
| Author_xml | – sequence: 1 givenname: Hashim H. orcidid: 0009-0005-3761-3593 surname: Migaybil fullname: Migaybil, Hashim H. email: hmigaybil@kau.edu.sa organization: Department of Chemical and Materials Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia – sequence: 2 givenname: Bhushan surname: Gopaluni fullname: Gopaluni, Bhushan organization: Department of Chemical and Biological Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, BC V6T1Z3, Canada |
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| Keywords | Data augmentation Solar still Support vector regression (SVR) Convolutional neural networks (CNNs) Look-back window Distillate water |
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