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Predictions of solar potential for these systems’ production are important, whether they ensure sound activity or the perfect control of an energy discharge heading to the solar system. It is important to base the prediction on solar irradiance before predicting solar systems performance. The measurement of solar radiation elements is a very significant criterion for applications of solar energy. Several globalized solar radiation prediction modes can be done in the two major categories: cloud imagery with physical models and machine learning techniques are correlated. In this paper, the methods used to predict solar radiation are explained with machine learning algorithms.

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A data acquisition method to obtain force measurement on an end effector of a serial manipulator is carried out using LabVIEW Data Dashboard to obtain results wirelessly developing an integrated, system between the serial manipulator, a computer and a remote device. A strut array was manufactured with a 4x4 grid and 8 strain gauges were attached on the parallel sides of the 4 rectangular struts based on strain analysis run theoretically using ANSYS to locate position of maximum strain. Two full bridge circuits from the array were then connected to a NI 9219 analog I/O module set up on a NI cDAQ 9174 chassis to measure the force on the struts whenever a load is applied. Tabular results of strain were obtained which varied linearly with uniformly appreciating loads, graphically displayed using LabVIEW GUI. LabVIEW is further connected wirelessly to the NI Data Dashboard app on a mobile via a shared variable engine which generates the results as a shared variable which can then be displayed on other remote devices. Therefore we can say that an integrated wireless system to remote monitor force applied on an end effector of the serial manipulator IRB1410 has been developed successfully.