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Author: search.filters.author.Chinnavornrungsee, PerawutStart date: 2020

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    Item type:Publication,
    Enhancing PV system modeling accuracy with the irradiance intensity selection technique
    (2026-06-01)
    Songtrai, Sasiwimon
    ;
    Chinnavornrungsee, Perawut
    ;
    Sriprapha, Kobsak
    ;
    Kobayashi, Tomonao
    ;
    Niemcharoen, Surasak
    This study is a method to improve the accuracy of photovoltaic (PV) power modeling by the irradiance intensity segmentation. The developed model was compared with 2 years of data from PV system in Thailand and the original 1D5P and weight function models to determine accuracy. The results show that by applying the irradiance intensity selection technique with a 1D5P equivalent circuit model improved the accuracy of the proposed model. The proposed method achieved a significantly lower %root mean square error (%RMSE) compared with other models yielding %RMSE values of 0.26% under clear-sky and 2.80% under cloudy conditions. Seasonal evaluation further demonstrated improved prediction accuracy, with the lowest deviation observed during the rainy period, attributed to reduced dust accumulation. A 2 years comparison confirmed the proposed model exhibited the lowest deviation of 0.69%, outperforming conventional PV simulation programs. These findings indicate that irradiance segmentation enhances forecasting performance and provides accurate PV output estimation under real environmental conditions.
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    A method to improve the accuracy of simulation models: A case study on photovoltaic system modelling
    (2021-01-02)
    Bupi, Aekkawat
    ;
    Kittisontirak, Songkiate
    ;
    Chinnavornrungsee, Perawut
    ;
    Songtrai, Sasiwimon
    ;
    Manosukritkul, Phassapon
    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.
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