Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure

The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system.