Enhancing photovoltaic system efficiency using artificial intelligence techniques

Photovoltaic (PV) solar energy systems are key to achieving sustainable and renewable energy goals, yet their energy conversion efficiency remains constrained by environmental variability and hardware limitations. Recent advances demonstrate that integrating Artificial Intelligence (AI) with PV systems can substantially enhance performance across critical functions such as maximum power point tracking (MPPT), energy forecasting, and real time optimisation. For example, reinforcement learning based dual axis solar tracking has achieved up to 98 % tracking efficiency and increases annual energy yield by approximately 35 % compared to fixed tilt systems. AI enhanced MPPT algorithms have been shown to improve energy generation efficiency by up to 7.5 % over conventional methods in simulation studies, while ANN based predictors can achieve nearly 99 % accuracy in dynamic conditions. These results illustrate that AI driven strategies not only improve power extraction under fluctuating irradiance and temperature but also reduce system downtime through predictive maintenance and advanced control. This paper systematically reviews these AI applications and presents simulation analyses comparing conventional and AI based control methods, concluding that intelligent techniques offer significant gains in PV efficiency, reliability, and adaptability, which are critical for scalable renewable energy deployment.