Optimization of Solar PV Performance Using Predictive Power Modeling and Real-Time Data Analysis: A Case Study of Kano State, Nigeria - Jibril, Ibrahim; Garba Abdullahi, Musa; Uba, Shuaibu; Aliyu Abubakar, Maimuna; Adam Abubakar, Nasir; Usman, Sakinat; Yusuf Muhammad, Nasir (FUDMA Journal of Sciences, 2026)
The world demand of power combined with the performance degradation of solar energy by the environmental factors necessitate new optimization solutions. Solar PV systems experience intensive performance issues that can be attributed to heat by temperature variation and humidity change that leads to unreliable production of energy. The study addresses the best solar PV performance optimization using predictive power model and live data analysis with Kano State Nigeria being the prime study region. The study adopts a two-fold methodology that relates predictor analytics using machine learning to real-time data tracking functions to enhance the functionality of the PV systems. The 10-year average that was 12 months revealed that May had the highest temperature of 35.0 C and January had lowest temperature of 22.5 C and August had the highest humidity of 82  and March the least humidity of 15 . It has been demonstrated that the efficiency of power generation is maximum in temperatures of 25 C - 30 C but high humidity has harmful consequences to PV efficiency. Predictive analytics has demonstrated improvement in accuracy of forecasting and system reliability as per the comparison with previous studies. The barriers to implementation along with the computational complexity became some of the impediments in the course of the study. Integration based on predictive models results in efficient PV operation since they reduce the system inefficiencies and maximize the amount of power generated.
Keywords: Photovoltaic System Performance; Energy Forecasting; Optimization; Predictive Monitoring; Modeling