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    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.
      1
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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.
      3
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    A novel power output model for photovoltaic system
    (2017-10-19)
    Kittisontirak, Songkiate
    ;
    Dawan, Promphak
    ;
    Atiwongsangthong, Narin
    ;
    Titiroongruang, Wisut
    ;
    Chinnavornrungsee, Perawut
    This paper proposed the novel concept model for forecasting the PV power output. The model was used two meteorology to input model which are solar irradiance and module temperature. This model was improve the accuracy of model from simplified model is 1D5P by using the weight function. The proposed model was verified by comparison with measured data. The results showed that the model has high accuracy. The RMSE ranges from 0.02 to 0.07 and average RMSE is 0.04.
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    An Improved PV Output Forecasting Model by Using Weight Function: A Case Study in Cambodia
    (2016-01-01)
    Kittisontirak, Songkiate
    ;
    Bupi, Aekkawat
    ;
    Chinnavornrungsee, Perawut
    ;
    Sriprapha, Kobsak
    ;
    Thajchayapong, Pairash
    This paper proposes a new concept to improve accuracy of PV forecasting model. The model was implemented by MATLAB/Simulink software using solar irradiance and module temperature as measurement parameters for calculation. The model was developed by single-diode equivalent circuits (5-p model) for simulated PV module power output and compared with other software programs for validation which showed correct PV characteristics. To achieve high accuracy, the model was improved by weight function using one-year measured data. The accuracy of our developed model was verified by comparison with four commercial simulator software programs and the results from real system which were measured and recorded for 1 year. It was found that the model output was in a good agreement with the measured data. This research can be utilized in another area by adjusting the PV equation with weight function of that area.