集成深度学习与图像处理方法用于建模太阳能阵列阴影导致的能量损失

Abstract Shading poses a serious challenge to photovoltaic (PV) systems power generation, causing energy losses of up to 40 %, leading to power mismatches, hotspot formation, and accelerated module degradation. Accurate modelling and simulation of shading are critical for improving photovoltaic (PV) performance. This study develops two shadow‑detection pipelines: (i) the Classic Hough Transform (CHT) combined with K‑means segmentation and (ii) a new Deep Hough Transform (DHT) that learns semantic line features without the need for PV‑specific training data. A 1-kilowatt capacity solar array with planned shading devices was developed and used to perform the experimental analysis. The proposed methodology achieved an accuracy of 0.85, indicating a 32.81 % improvement in solar array detection compared to CHT methods. Computed energy losses due to shading were within 0.5 % to 1.9 % of System Advisor Model (SAM’s) simulated losses. Evaluation with transient shadows from a pedestrian, vehicle, and cloud showed an average mIoU of 81.8 % highlighting the methods advantage over existing 3D modeling-based simulation software. Statistical analysis confirmed the method’s consistency, yielding Dice = 0.857 (95 % CI 0.728–0.943) and mIoU = 0.771 (CI 0.595–0.893). The parametric analysis highlighted the time of day and number of obstructions as key factors influencing shading on solar arrays, with mornings and evenings experiencing over 6 % shading loss variations. Overall, this integrated approach develops robust, real-time modelling and simulation for optimizing large solar energy systems.