A Method to Improve the Accuracy of Simulation Models: A Case Study on Photovoltaic System Modelling
This research presents a method to improve data accuracy for the more efficient data management of the studied applications. The data accuracy was improved using the preciseness function learning model (PFL model). It contains a database in which the amount of data is more or less dependent on all o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/2/372 |
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| author | Aekkawat Bupi Songkiate Kittisontirak Perawut Chinnavornrungsee Sasiwimon Songtrai Phassapon Manosukritkul Kobsak Sriprapha Wisut Titiroongruang Surasak Niemcharoen |
| author_facet | Aekkawat Bupi Songkiate Kittisontirak Perawut Chinnavornrungsee Sasiwimon Songtrai Phassapon Manosukritkul Kobsak Sriprapha Wisut Titiroongruang Surasak Niemcharoen |
| author_sort | Aekkawat Bupi |
| collection | DOAJ |
| description | This research presents a method to improve data accuracy for the more efficient data management of the studied applications. The data accuracy was improved using the preciseness function learning model (PFL model). It contains a database in which the amount of data is more or less dependent on all of the possible behavior of the studied application. The proposed model improves data with functions obtained by optimizing curves to represent the data at each point, which estimate the database’s diffusion behavior, and functions can be built around all of the various forms of databases. The proposed model always updates its database after processing. It has been learning to optimize the processing precision. In order to verify the precision of the proposed model through its application to a PV system simulation model, the process’s database should contain at least one year. This is because the overall behavior of the PV power output in Thailand depends on the seasonal weather; Thailand has three seasons in a period of one year. The testing was performed by comparing the PV power output. The simulation results with the actual measurement data (12 MW PV system) can be divided into two conditions: the daily comparison and the seasonal PV power output. As a result, the proposed model can accurately simulate the PV power output despite the sudden daily climate change. The average nRMSE (normalized RMSE) of the proposed model is very low (1.23%), and ranges from 0.30% to 2.26%. Therefore, it has been proven that this model is very accurate. |
| first_indexed | 2024-03-09T05:11:27Z |
| format | Article |
| id | doaj.art-db70a48604bd4ced8ee52addb99cdb66 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T05:11:27Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-db70a48604bd4ced8ee52addb99cdb662023-12-03T12:49:37ZengMDPI AGEnergies1996-10732021-01-0114237210.3390/en14020372A Method to Improve the Accuracy of Simulation Models: A Case Study on Photovoltaic System ModellingAekkawat Bupi0Songkiate Kittisontirak1Perawut Chinnavornrungsee2Sasiwimon Songtrai3Phassapon Manosukritkul4Kobsak Sriprapha5Wisut Titiroongruang6Surasak Niemcharoen7Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Rd, Ladkrabang, Bangkok 10520, ThailandNational Energy Technology Center (ENTEC), National Science and Technology Development Agency (NSTDA), 112 Thailand Science Park, Thanon Phahonyotin, Tambon Klong Nueng, Amphoe Klong Luang, Pathum Thani 12120, ThailandNational Energy Technology Center (ENTEC), National Science and Technology Development Agency (NSTDA), 112 Thailand Science Park, Thanon Phahonyotin, Tambon Klong Nueng, Amphoe Klong Luang, Pathum Thani 12120, ThailandNational Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA), 112 Thailand Science Park, Thanon Phahonyotin, Tambon Klong Nueng, Amphoe Klong Luang, Pathum Thani 12120, ThailandKing Mongkut’s Institute of Technology Ladkrabang Prince of Chumphon Campus, Chum Kho, Pathio District, Chumphon 86160, ThailandNational Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA), 112 Thailand Science Park, Thanon Phahonyotin, Tambon Klong Nueng, Amphoe Klong Luang, Pathum Thani 12120, ThailandFaculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Rd, Ladkrabang, Bangkok 10520, ThailandFaculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Rd, Ladkrabang, Bangkok 10520, ThailandThis research presents a method to improve data accuracy for the more efficient data management of the studied applications. The data accuracy was improved using the preciseness function learning model (PFL model). It contains a database in which the amount of data is more or less dependent on all of the possible behavior of the studied application. The proposed model improves data with functions obtained by optimizing curves to represent the data at each point, which estimate the database’s diffusion behavior, and functions can be built around all of the various forms of databases. The proposed model always updates its database after processing. It has been learning to optimize the processing precision. In order to verify the precision of the proposed model through its application to a PV system simulation model, the process’s database should contain at least one year. This is because the overall behavior of the PV power output in Thailand depends on the seasonal weather; Thailand has three seasons in a period of one year. The testing was performed by comparing the PV power output. The simulation results with the actual measurement data (12 MW PV system) can be divided into two conditions: the daily comparison and the seasonal PV power output. As a result, the proposed model can accurately simulate the PV power output despite the sudden daily climate change. The average nRMSE (normalized RMSE) of the proposed model is very low (1.23%), and ranges from 0.30% to 2.26%. Therefore, it has been proven that this model is very accurate.https://www.mdpi.com/1996-1073/14/2/372preciseness function learning model (PFL model)learningphotovoltaicsolar irradiancemodule temperature |
| spellingShingle | Aekkawat Bupi Songkiate Kittisontirak Perawut Chinnavornrungsee Sasiwimon Songtrai Phassapon Manosukritkul Kobsak Sriprapha Wisut Titiroongruang Surasak Niemcharoen A Method to Improve the Accuracy of Simulation Models: A Case Study on Photovoltaic System Modelling Energies preciseness function learning model (PFL model) learning photovoltaic solar irradiance module temperature |
| title | A Method to Improve the Accuracy of Simulation Models: A Case Study on Photovoltaic System Modelling |
| title_full | A Method to Improve the Accuracy of Simulation Models: A Case Study on Photovoltaic System Modelling |
| title_fullStr | A Method to Improve the Accuracy of Simulation Models: A Case Study on Photovoltaic System Modelling |
| title_full_unstemmed | A Method to Improve the Accuracy of Simulation Models: A Case Study on Photovoltaic System Modelling |
| title_short | A Method to Improve the Accuracy of Simulation Models: A Case Study on Photovoltaic System Modelling |
| title_sort | method to improve the accuracy of simulation models a case study on photovoltaic system modelling |
| topic | preciseness function learning model (PFL model) learning photovoltaic solar irradiance module temperature |
| url | https://www.mdpi.com/1996-1073/14/2/372 |
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