An Empirical Performance Comparison Between Linear Regression and Linear Interpolation Algorithms for a Dual-Axes Tracking-Type PV Modules

The need for renewable resource growth stems from the world's ever-growing energy consumption rate, finite supply of fossil fuels, and pollution. In order to accomplish the 2030 agenda and the sustainable development goals (SDGs) in Africa, renewable energy sources like photovoltaic (PV) are crucial. PV devices' limited ability to harvest solar energy is the primary obstacle facing the solar industry. PV modules have, for the most part, been installed quickly all over the world. However, the installations usually use a fixed-system with pre-specified angles obtained using global positioning system's (GPS) location. Conse-quently, the electricity produced by PV modules drastically drops in accordance with changes in sun's irradiation and position during the day and the seasons of the year. This paper presents the empirical results and performance comparison between the two effective automated dual-axis solar tracking algorithms (linear regression and linear interpolation). The algorithms were developed to automatically track the sun's position in the sky in order to optimize the output power from the PV modules. The LabVIEW user interface program from National Instruments (NI) was used to construct the algorithms. It was established that the output power from the linear interpolation algorithm was 2.83 % greater than that of the linear regression algorithm for one day and 3 % for a period of four months. Given that the study was carried out in one of the most polluted cities in South Africa, Johannesburg (Gauteng province), it is essential to test the algorithms in the other provinces to see if the findings would still hold true.