Forecasting Solar Still Performance From Conventional Weather Data Variation By Machine Learning Method - Info and Reading Options
By 高文杰
“Forecasting Solar Still Performance From Conventional Weather Data Variation By Machine Learning Method” Metadata:
- Title: ➤ Forecasting Solar Still Performance From Conventional Weather Data Variation By Machine Learning Method
- Author: 高文杰
“Forecasting Solar Still Performance From Conventional Weather Data Variation By Machine Learning Method” Subjects and Themes:
- Subjects: ➤ ChinaXiv - 中国科学院科技论文预发布平台 - 能源科学 - 能源（综合） - Solar still - Production forecasting - Forecasting model - Weather data - Random forest
Edition Identifiers:
- Internet Archive ID: ChinaXiv-202205.00175V1
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The Internet Archive:
<div class="hd"> <h1> Forecasting solar still performance from conventional weather data variation by machine learning method </h1> <div class="flex" style="border:0px;padding-bottom:0px;"> </div> <div class="bd" style="border-bottom:1px solid #ced6e0;"> <ul style="margin-top:0px;"> <li> <b> 作者： </b> <a href="https://chinaxiv.org/user/search.htm?field=author&value=%E9%AB%98%E6%96%87%E6%9D%B0" style="color:#3060cc;" rel="nofollow"> 高文杰 </a> <sup style="margin-left:0px;"> 1 </sup> </li> <li> <b> 作者单位： </b> <div style="margin-left:70px;margin-top:-26px;"> <div> 1. <a href="https://chinaxiv.org/user/search.htm?field=affication&value=%E5%8D%8E%E4%B8%AD%E7%A7%91%E6%8A%80%E5%A4%A7%E5%AD%A6" style="color:#3060cc;" rel="nofollow"> 华中科技大学 </a> </div> </div> </li> <li> <b> 提交时间： </b> 2022-05-30 </li> </ul> </div> <div class="bd" style="margin-top:15px;"> <p style="color:#333;"> <b> 摘要: </b> 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. </p> <div class="brdge"> <span class="spankwd"> <a href="https://chinaxiv.org/user/search.htm?field=keywords&value=Solar%20still" rel="nofollow"> Solar still </a> </span> <span class="spankwd"> <a href="https://chinaxiv.org/user/search.htm?field=keywords&value=%20Production%20forecasting" rel="nofollow"> Production forecasting </a> </span> <span class="spankwd"> <a href="https://chinaxiv.org/user/search.htm?field=keywords&value=%20Forecasting%20model" rel="nofollow"> Forecasting model </a> </span> <span class="spankwd"> <a href="https://chinaxiv.org/user/search.htm?field=keywords&value=%20Weather%20data" rel="nofollow"> Weather data </a> </span> <span class="spankwd"> <a href="https://chinaxiv.org/user/search.htm?field=keywords&value=%20Random%20forest" rel="nofollow"> Random forest </a> </span> </div> <ul> <li> <b> 来自： </b> 孙森山 </li> <li> <b> 分类： </b> <a href="https://chinaxiv.org/user/search.htm?field=domain&value=425" rel="nofollow"> 能源科学 </a> >> <a href="https://chinaxiv.org/user/search.htm?field=subject&value=431" rel="nofollow"> 能源（综合） </a> </li> <li> <b> 引用： </b> <a href="https://chinaxiv.org/abs/202205.00175" rel="nofollow"> <font color="#0000FF"> ChinaXiv:202205.00175 </font> </a> (或此版本 <a href="https://chinaxiv.org/abs/202205.00175v1" rel="nofollow"> <font color="#0000FF"> ChinaXiv:202205.00175V1 </font> </a> ) <br /> <a style="margin-left:45px;" rel="nofollow"> doi:10.12074/202205.00175 </a> <br /> <a href="https://www.cstr.cn/CSTR:32003.36.ChinaXiv.202205.00175.V1" style="margin-left:45px;" rel="nofollow"> <font color="#0000FF"> CSTR:32003.36.ChinaXiv.202205.00175.V1 </font> </a> </li> <li> <b> 推荐引用方式： </b> <span> 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] </span> </li> </ul> </div> <div class="ft"> <h4> <span> 版本历史 </span> </h4> <table style="font-size:14px;"> <tr> <td> <b> [V1] </b> </td> <td> 2022-05-30 15:52:33 </td> <td> ChinaXiv:202205.00175V1 </td> <td> <a class="btn" href="https://chinaxiv.org/user/download.htm?id=35187" rel="nofollow"> 下载全文 </a> </td> </tr> </table> </div> </div>
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