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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
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Kittisontirak, Songkiate
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Chinnavornrungsee, Perawut
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Songtrai, Sasiwimon
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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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Comparison of power output forecasting on the photovoltaic system using adaptive neuro-fuzzy inference systems and particle swarm optimization-artificial neural network model
(2020-01-01)
Dawan, Promphak
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Sriprapha, Kobsak
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Kittisontirak, Songkiate
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Boonraksa, Terapong
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Junhuathon, Nitikorn
The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.
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Performance of the Module Temperature Model in Forecasting the Power Output of Photovoltaic Systems
(2018-07-02)
Dawan, Promphak
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Worranetsuttikul, Kaweepoj
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Kittisontirak, Songkiate
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Sriprapha, Kobsak
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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.
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Comparison of Performance of 1D5P with 2 Input and 1 Input Model in Forecasting of Power Output for Photovoltaic Systems
(2018-07-02)
Dawan, Promphak
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Kittisontirak, Songkiate
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Sriprapha, Kobsak
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Muanglua, Rangson
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Titiroongruang, Wisut
This paper presents the comparison on forecasting performance of power output for photovoltaic system type 1D \pmb{5}\mathbf{P}, which used 1 input parameter being solar irradiance and 2 input parameters consisting of solar irradiance and the temperature model. The result shows that the forecasting of power output for photovoltaic system type 1D5P, which used 1 input parameter, provided root-mean-square deviation (RMSE) at 0.04 and for 2 input parameters provided RMSE at 0.05.
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A novel power output model for photovoltaic system
(2017-10-19)
Kittisontirak, Songkiate
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Dawan, Promphak
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Atiwongsangthong, Narin
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Titiroongruang, Wisut
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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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A simplified model for the estimation of energy production of PV Module
(2017-10-19)
Bupi, Aekkawat
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Kittisontirak, Songkiate
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Sriprapha, Kobsak
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Siriwongrungsan, Wilailak
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Titiroongruang, Wisut
This paper describes the objective of the model to produce electricity from solar cell applications MATLAB / SIMULINK Compared with the data of electricity from photovoltaic power systems in Cambodia. Using models 1D5P (equivalent circuit analysis of one of the diodes, solar cells based on 5 parameters are the main factors in the equation that represents the equivalent circuit). 2 parameters are used to simulate the solar irradiance and module temperature from the retention of a calculation to determine the solar farm to generate electricity. And compared with the actual electricity. By comparing the results of the simulation with high accuracy. And display of the RMSE range of 0.01 to 0.18 and with an average of RMSE is 0.09.
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Comparison of PV estimation model with measured PV power output
(2017-10-19)
Kittisontirak, Songkiate
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Niemcharoen, Surasak
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Titiroongruang, Wisut
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Limmanee, Amornrat
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Sitthiphol, Nopphadol
This paper describes a comparison of proposed model with simulation software and measurement data of PV power plant in Cambodia. The proposed model is based on behavior of PV module at a particular site. The parameters which affected to PV power output as solar irradiance and module temperature were used as input in this model. Weight function technics were used to improve accuracy of single diode five parameters (1D5P). It was found that the proposed model can improve the accuracy of PV power output estimation model.
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Comparison of PV module power output measurements
(2017-10-19)
Bupi, Aekkawat
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Kittisontirak, Songkiate
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Sriprapha, Kobsak
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Suriyaammaranon, Chabar
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Titiroongruang, Wisut
This paper describes the objective of the model to produce electricity from solar program. MATLAB / SIMULINK Compared with actual production data from electricity plants. This information is taken from the program PVSYST (crawlers electricity from real factories). The models created from an analysis of the equivalent circuit model of a diode, solar cell, with 5 parameters are the main factors used in the calculations were called 1 D5 P. The comparison takes into account the impact on solar irradiance and module temperature. The result of the comparison. The model has high accuracy and is close to the actual data to generate electricity.
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An Improved PV Output Forecasting Model by Using Weight Function: A Case Study in Cambodia
(2016-01-01)
Kittisontirak, Songkiate
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Bupi, Aekkawat
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Chinnavornrungsee, Perawut
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Sriprapha, Kobsak
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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.

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