Machine learning approaches for real-time forecasting of solar still distillate output
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Machine learning approaches for real-time forecasting of solar still distillate output
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| Автор |
Murugan, D. K.
Said, Z. Panchal, H. Gupta, N. K. Subramani, S. Kumar, A. Sadasivuni, K. K. |
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| Тематика |
DECISION TREE MODELLING
MACHINE LEARNING TECHNIQUES PREDICTIVE MODELS PRODUCTIVITY ESTIMATION SOLAR STILL |
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| Описание |
Solar stills provide a promising avenue for freshwater production in regions grappling with water scarcity, especially remote locales. However, their efficiency is often constrained by the variable climatic conditions. Conventional prediction methods fall short in consistently forecasting the yield, leaving a significant gap in optimizing solar still operations. Recognizing this, the introduction of machine learning becomes pivotal. With a robust predictive model, operators can avoid inefficiencies, inconsistent outputs, and sub-optimal resource utilization. The primary objective of this research is to determine the most suitable machine learning model tailored for predicting solar still output under specific environmental conditions. This research work assessed various machine learning models, including linear regression, decision trees, random forest, support vector machines, and multilayer perceptron. Evaluation metrics encompassed Mean Absolute Error (MAE), cross-validation, grid search, and randomized search techniques. Our results identified the Decision Tree model, registering a MAE of 5.43 and 5.74 through random and grid search methods, respectively, as the preeminent predictor for our dataset. This machine learning-centric methodology elevates the precision of solar still output predictions and paves the way for enhanced solar still designs and superior optimization of solar energy conversion mechanisms. © 2023 The Author(s)
Qatar National Research Fund, QNRF: MME03-1226-210042 This work was supported by Qatar National Research Fund under the grant no. MME03-1226-210042 . The statements made herein are solely the responsibility of the authors. |
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| Дата |
2024-04-05T16:34:42Z
2024-04-05T16:34:42Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Murugan, DK, Said, Z, Panchal, H, Gupta, N, Subramani, S, Kumar, A & Sadasivuni, K 2023, 'Machine learning approaches for real-time forecasting of solar still distillate output', Environmental Challenges, Том. 13, 100779. https://doi.org/10.1016/j.envc.2023.100779
Murugan, D. K., Said, Z., Panchal, H., Gupta, N., Subramani, S., Kumar, A., & Sadasivuni, K. (2023). Machine learning approaches for real-time forecasting of solar still distillate output. Environmental Challenges, 13, [100779]. https://doi.org/10.1016/j.envc.2023.100779 2667-0100 Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174639403&doi=10.1016%2fj.envc.2023.100779&partnerID=40&md5=55e0dd5bd9ce1a544c4612df77ac5691 https://doi.org/10.1016/j.envc.2023.100779 http://elar.urfu.ru/handle/10995/130871 10.1016/j.envc.2023.100779 85174639403 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Elsevier B.V.
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| Источник |
Environmental Challenges
Environmental Challenges |
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