Balancing geometric efficiency and meteorological optimality in rooftop photovoltaic layout planning

Accurate assessment of rooftop photovoltaic (PV) potential is critical for urban energy transitions, yet complex rooftop morphologies and fragmented obstacles often compromise estimation accuracy. Most existing approaches focus on selecting module tilt and azimuth to improve per-module yield based on meteorological conditions, while overlooking how mounting parameters affect geometric feasibility, particularly packing density and layout efficiency on irregular rooftops. To address this gap, this study proposes a Deep Learning-based Morphology-Aware Layout (DL-MAL) framework for obstacle-aware PV layout planning on complex rooftops. First, a DeepLabV3+ model with an Xception backbone was trained on a custom three-class high-resolution imagery dataset to extract pixel-level rooftop availability, excluding static obstacles and defining realistic installation zones. Then, an adaptive layout algorithm (ALA) evaluates predefined azimuth–tilt configurations while dynamically adjusting inter-row spacing to avoid shading, generating feasible layouts and corresponding capacity estimates. The reported best-performing configurations are identified within the predefined discrete azimuth–tilt search space under the adopted no-inter-row-shading constraint. Case studies demonstrate that the framework achieves reliable rooftop availability segmentation and significantly improves assessment accuracy, reducing the mean absolute relative error from 25.3% to 7.2% compared with the empirical benchmark. Results further indicate that geometric constraints can dominate in dense urban environments, where configurations maximizing packing density may yield higher total energy output than conventional meteorological reference configurations. Overall, DL-MAL provides a robust and scalable approach for translating image-based rooftop analysis into engineering-feasible PV layouts, supporting high-precision urban PV planning.