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A Method to Improve the Accuracy of Simulation Models: A Case Study on Photovoltaic System Modelling

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Listed:
  • Aekkawat Bupi

    (Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Rd, Ladkrabang, Bangkok 10520, Thailand)

  • Songkiate Kittisontirak

    (National 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, Thailand)

  • Perawut Chinnavornrungsee

    (National 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, Thailand)

  • Sasiwimon Songtrai

    (National 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, Thailand)

  • Phassapon Manosukritkul

    (King Mongkut’s Institute of Technology Ladkrabang Prince of Chumphon Campus, Chum Kho, Pathio District, Chumphon 86160, Thailand)

  • Kobsak Sriprapha

    (National 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, Thailand)

  • Wisut Titiroongruang

    (Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Rd, Ladkrabang, Bangkok 10520, Thailand)

  • Surasak Niemcharoen

    (Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Rd, Ladkrabang, Bangkok 10520, Thailand)

Abstract

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.

Suggested Citation

  • Aekkawat Bupi & Songkiate Kittisontirak & Perawut Chinnavornrungsee & Sasiwimon Songtrai & Phassapon Manosukritkul & Kobsak Sriprapha & Wisut Titiroongruang & Surasak Niemcharoen, 2021. "A Method to Improve the Accuracy of Simulation Models: A Case Study on Photovoltaic System Modelling," Energies, MDPI, vol. 14(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:372-:d:478556
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    References listed on IDEAS

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    1. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
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