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.

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.

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.

This research presents the invention of a connector unlocking device. The purpose of this invention is to create a connector unlocking device which allows the user to unlock the connector faster and reduce the chance of injury. The composition of the device according to this invention are following. The unlocking part which has one end attached to the arm for unlocking the connector. The arm which has one end for mounting the unlocking part, and the other end which is connected to the pivot point for connecting between the unlocking part and the pivot point. The pivot point is the connector between the two arms. The unlocking part has a geometric shape with one side touching the locking lever and the other side touching the guide. The fore that handle to the device can push the locking lever for unlocking the connecter faster and reduce the chance of injury.