KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/kmitl/1

Browse

Settings

Author: search.filters.author.Sriprapha, KobsakSubject: search.filters.subject.Photovoltaic

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Performance of the Module Temperature Model in Forecasting the Power Output of Photovoltaic Systems
    (2018-07-02)
    Dawan, Promphak
    ;
    Worranetsuttikul, Kaweepoj
    ;
    Kittisontirak, Songkiate
    ;
    Sriprapha, Kobsak
    ;
    Titiroongruang, Wisut
    This paper is the study the performance of the temperature model in forecasting the power output of photovoltaic systems. The prominent point of the solar power forecasting model was to use only one input parameter (solar irradiance). Solar irradiance was injected into the temperature model using mathematic equations. The output of temperature model and the solar irradiance was used by the solar power forecasting model for forecasting the power output of the photovoltaic systems. The results shows that the effectiveness of the temperature model had a discrepancy at 4.03%, which is acceptable.