Integrating Fuzzy Logic with LDR Sensors for Optimized Solar Energy Harvesting in Sun-Tracking Systems

This study explores the integration of Computer Science and AI with sustainable energy solutions, specifically focusing on optimizing the energy output of photovoltaic (PV) arrays using intelligent tracking systems. The research introduces a dual-axis solar tracking mechanism that utilizes fuzzy logic for real-time analysis of sunlight intensity, thereby enhancing the efficiency of solar arrays. An array consisting of four Light Dependent Resistor (LDR) sensors as well as a UNO Arduino microprocessor are at the centre of the apparatus for assessing the appropriate concentration of incoming sunlight. The system regulates the position of the photovoltaic cells continuously via two motorised axes—horizontal & vertical guided by input from the LDRs. The data from the sensors are interpreted by the fuzzy logic controller in order to figure out the exact motions needed by the solar panels to remain in the perfect alignment with the sun's rays. The novel application of fuzzy mathematics enables sophisticated making choices, emulating rational thought & allowing the machine to respond to various degrees of light. This is great as it increases the power collection ability for the solar array with respect to the PV cell having the optimum potential angle exposure throughout the day. Not only does this enhance the efficient use of energy, but it also shows that there can be saving on the needless usage of a tracking device itself. This research fills the gap between green energy technology and smart autonomy, showing how artificial intelligence leads to higher production of solar electricity generation.