The effect of dataset size and the process of big data mining for investigating solar-thermal desalination by using machine learning
It provides a standard process for studying solar-thermal desalination with machine learning, addressing a domain-specific problem for researchers in renewable energy and desalination, though it is incremental in nature.
This study tackled data shortage and inconsistent analysis in solar-thermal desalination by developing an optimized dataset collection process that reduced collection time by 83.3% and gathered over 1,000 datasets, finding that Random Forest is effective for large datasets and machine learning achieves high accuracy with a minimum mean relative prediction error of around 4%.
Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.