Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAE

• ANFIS achieved R 2 of 0.99 for PV power forecasting under real-time operating conditions. • Combines fuzzy logic and neural networks to model PV systems’ nonlinear behaviors. • Demonstrates robust PV performance prediction under harsh weather conditions. • Integrates IoT-based real-time monitoring for system performance and fault detection. • Provides insights into optimizing PV output via irradiance and thermal management. This study addresses the fundamental challenge of accurately forecasting power generation from photovoltaic (PV) systems, which is crucial for effective grid integration and energy management. The intermittency and variability of solar power due to environmental factors pose significant difficulties in achieving reliable predictions. An adaptive neuro-fuzzy inference system (ANFIS) model is proposed for forecasting the performance of a 2.88 kW on-grid PV system in Sharjah, UAE. The model leverages extensive real-time data collected during the peak energy generation season to predict critical variables such as the maximum power point (MPP), voltage, and current. The ANFIS model achieves high prediction accuracy, with a Coefficient of Determination (R2) of 0.9967 for power generation, 0.9076 for voltage generation, and 0.9913 for current generation. These results highlight the model’s robustness in capturing the nonlinear dependencies between environmental factors and PV output. The study compares the ANFIS model with other established machine learning models, including Linear Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest. The ANFIS model outperforms these models in terms of prediction accuracy, demonstrating its superior generalization capabilities. The findings underscore the potential of the ANFIS model for robust forecasting and effective PV performance management, providing a reliable tool for early fault detection and system assessment. Future work will focus on integrating fault detection capabilities and extending model validation across different seasons to ensure a comprehensive investigation of the system dynamics under fluctuating weather conditions.