Evaluation of Random Forest-Genetic Algorithm Hybrid Model in Estimating Daily Solar Radiation
Solar energy is the most important source of renewable energy, in other words, the main source of energy on Earth. Therefore, estimating the solar radiation parameter with high accuracy is very important. In this regard, in the present study, meteorological data of 3 meteorological stations of Ardab...
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| Format: | Article |
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Iranian Rainwater Catchment Systems Association
2022-09-01
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| Series: | محیط زیست و مهندسی آب |
| Subjects: | |
| Online Access: | http://www.jewe.ir/article_143222_208ef1a9ea94cd3cb40349fc0c7db4a2.pdf |
| _version_ | 1797783460546347008 |
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| author | Sajjad Hashemi Saeed Samadianfard Ali Ashraf Sadraddini |
| author_facet | Sajjad Hashemi Saeed Samadianfard Ali Ashraf Sadraddini |
| author_sort | Sajjad Hashemi |
| collection | DOAJ |
| description | Solar energy is the most important source of renewable energy, in other words, the main source of energy on Earth. Therefore, estimating the solar radiation parameter with high accuracy is very important. In this regard, in the present study, meteorological data of 3 meteorological stations of Ardabil province, including Meshginshahr, Germi, and Nir for a period of 2 years (2017-2018) on a daily scale were used. Then, the intensity of daily solar radiation in each of the mentioned stations was estimated using random forest and random forest methods-genetic algorithm. The meteorological variables used included minimum, maximum and average temperature, relative humidity, and wind speed, which in eight different combinations were considered as input data in the model calculations. The obtained results were compared with each other using statistical parameters and the best models were selected. By comparing the results, the models of Nir, Meshginshahr, and Germi stations were ranked from highest to lowest modeling accuracy, respectively; So that the GA-RF-V model in Nir station with the root mean square error of 0.346 MJ/m2d and Kling-Gupta efficiency of 0.687 with the least error was introduced as the best model in this study. Also, the results showed that the genetic algorithm has helped to increase the accuracy of all utilized models. |
| first_indexed | 2024-03-13T00:26:21Z |
| format | Article |
| id | doaj.art-99af78d8e22b46b393d90f90defd07eb |
| institution | Directory Open Access Journal |
| issn | 2476-3683 |
| language | fas |
| last_indexed | 2024-03-13T00:26:21Z |
| publishDate | 2022-09-01 |
| publisher | Iranian Rainwater Catchment Systems Association |
| record_format | Article |
| series | محیط زیست و مهندسی آب |
| spelling | doaj.art-99af78d8e22b46b393d90f90defd07eb2023-07-11T04:48:24ZfasIranian Rainwater Catchment Systems Associationمحیط زیست و مهندسی آب2476-36832022-09-018363665310.22034/jewe.2022.312038.1654143222Evaluation of Random Forest-Genetic Algorithm Hybrid Model in Estimating Daily Solar RadiationSajjad Hashemi0Saeed Samadianfard1Ali Ashraf Sadraddini2PhD Scholar, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, IranAssoc. Professor, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, IranProfessor, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, IranSolar energy is the most important source of renewable energy, in other words, the main source of energy on Earth. Therefore, estimating the solar radiation parameter with high accuracy is very important. In this regard, in the present study, meteorological data of 3 meteorological stations of Ardabil province, including Meshginshahr, Germi, and Nir for a period of 2 years (2017-2018) on a daily scale were used. Then, the intensity of daily solar radiation in each of the mentioned stations was estimated using random forest and random forest methods-genetic algorithm. The meteorological variables used included minimum, maximum and average temperature, relative humidity, and wind speed, which in eight different combinations were considered as input data in the model calculations. The obtained results were compared with each other using statistical parameters and the best models were selected. By comparing the results, the models of Nir, Meshginshahr, and Germi stations were ranked from highest to lowest modeling accuracy, respectively; So that the GA-RF-V model in Nir station with the root mean square error of 0.346 MJ/m2d and Kling-Gupta efficiency of 0.687 with the least error was introduced as the best model in this study. Also, the results showed that the genetic algorithm has helped to increase the accuracy of all utilized models.http://www.jewe.ir/article_143222_208ef1a9ea94cd3cb40349fc0c7db4a2.pdfardabilintelligent modelsoptimizationsolar energy |
| spellingShingle | Sajjad Hashemi Saeed Samadianfard Ali Ashraf Sadraddini Evaluation of Random Forest-Genetic Algorithm Hybrid Model in Estimating Daily Solar Radiation محیط زیست و مهندسی آب ardabil intelligent models optimization solar energy |
| title | Evaluation of Random Forest-Genetic Algorithm Hybrid Model in Estimating Daily Solar Radiation |
| title_full | Evaluation of Random Forest-Genetic Algorithm Hybrid Model in Estimating Daily Solar Radiation |
| title_fullStr | Evaluation of Random Forest-Genetic Algorithm Hybrid Model in Estimating Daily Solar Radiation |
| title_full_unstemmed | Evaluation of Random Forest-Genetic Algorithm Hybrid Model in Estimating Daily Solar Radiation |
| title_short | Evaluation of Random Forest-Genetic Algorithm Hybrid Model in Estimating Daily Solar Radiation |
| title_sort | evaluation of random forest genetic algorithm hybrid model in estimating daily solar radiation |
| topic | ardabil intelligent models optimization solar energy |
| url | http://www.jewe.ir/article_143222_208ef1a9ea94cd3cb40349fc0c7db4a2.pdf |
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