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Forecasting solar still performance from conventional weather data variation by machine learning method

Chinese Physics B 2023年第4期32卷 19-25页

作者：高文杰沈乐山孙森山彭桂龙申震王云鹏 AbdAllah Wagih Kandeal 骆周扬 A.E.Kabeel 张坚群鲍华杨诺 State Key Laboratory of Coal Combustion and School of Energy and Power EngineeringHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Baima Lake Laboratory Co. Ltd.Hangzhou 31121China Zhejiang Energy Group R&D Institute Co.Ltd.Hangzhou 311121China Zhejiang Zheneng Yueqing Electric Power Generation Co. Ltd.Yueqing 325609China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong UniversityShanghai 200240China Mechanical Engineering Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh 33516Egypt Mechanical Power Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Faculty of Engineering Delta University for Science and TechnologyGamasaEgypt

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 are obtained from daily measurements lasting9 months.To validate the accuracy model,the determination coefficients of two types of solar stills are calculated as 0.935and 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 applying the model,we predicted 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.

关键词： solar still production forecasting forecasting model weather data random forest

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A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

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Computers, Materials & Continua 2022年第3期70卷 4725-4743页

作者： Jhonn Pablo Rodríguez David Griol Zoraida Callejas Juan Carlos Corrales Telematics Engineering Group University of CaucaPopayán190002Colombia INRAE University of ToulouseUMR 1248 AGIRF-31326Castanet TolosanFrance Department of Software Engineering University of GranadaGranada18071Spain

Coffee plays a key role in the generation of rural employment in Colombia.More than 785,000 workers are directly employed in this activity,which represents the 26%of all jobs in the agricultural sector.Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities,and resources(number of workers,required infrastructures),anticipating negotiations,estimating,price,and foreseeing losses of coffee production in a specific territory.These important processes can be affected by several factors that are not easy to predict(e.g.,weather variability,diseases,or plagues.).In this paper,we propose a non-destructive time series model,based on weather and crop management information,that estimate coffee production allowing coffee growers to improve their management of agricultural activities such as flowering calendars,harvesting seasons,definition of irrigation methods,nutrition calendars,and programming the times of concentration of production to define the amount of personnel needed for harvesting.The combination of time series and machine learning algorithms based on regression trees(XGBOOST,TR and RF)provides very positive results for the test dataset collected in real conditions for more than a year.The best results were obtained by the XGBOOST model(MAE=0.03;RMSE=0.01),and a difference of approximately 0.57%absolute to the main harvest of 2018.

关键词： Cherry coffee production estimation learner approaches time series weather data crop management data

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基于气象数据的关联规则挖掘

基于气象数据的关联规则挖掘

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第十七届全国数据库学术会议

作者：张宇鹏王丽珍周丽华云南大学计算机科学系

1引言根据气象专家提供的信息,在气象数据中虽然蕴藏有大量气象规律,但难以发现的原因主要有两个:一是数据量巨大,难以做全面细致

关键词： Association rule data variation data mining weather data

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