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Forecasting solar still performance from conventional weather data variation by machine learning method 作者： 高文杰 1 作者单位： 1. 华中科技大学 提交时间： 2022-05-30 摘要: Solar stills are considered an effective method to solve the scarcity of drinkable water. However, it is still missing a way to forecast its production. Herein, it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data. The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm. The required data to train the model is obtained from daily measurements lasting 9 months. To validate the accuracy model, the determination coefficients of two types of solar stills are calculated as 0.935 and 0.929, respectively, which are much higher than the value of both multiple linear regression (0.767) and the traditional models (0.829 and 0.847). Moreover, by appling the model, it is predicted that the freshwater production of four cities in China. The predicted production is approved to be reliable by a high value of correlation (0.868) between the predicted production and the solar insolation. With the help of the forecasting model, it would greatly promote the global application of solar stills. Solar still Production forecasting Forecasting model Weather data Random forest 来自： 孙森山 分类： 能源科学 >> 能源（综合） 引用： ChinaXiv:202205.00175 (或此版本 ChinaXiv:202205.00175V1 ) doi:10.12074/202205.00175 CSTR:32003.36.ChinaXiv.202205.00175.V1 推荐引用方式： 高文杰.(2022).Forecasting solar still performance from conventional weather data variation by machine learning method.中国科学院科技论文预发布平台.[ChinaXiv:202205.00175] 版本历史 [V1] 2022-05-30 15:52:33 ChinaXiv:202205.00175V1 下载全文