Arega Getaneh Abate

Ruyu Liu, Dongxu Zhuang, Jianhua Zhang et al.

Building facades represent a significant untapped resource for solar energy generation in dense urban environments, yet assessing their photovoltaic (PV) potential remains challenging due to complex geometries and semantic com ponents. This study introduces SF-SPA (Semantic Facade Solar-PV Assessment), an automated framework that transforms street-view photographs into quantitative PV deployment assessments. The approach combines com puter vision and artificial intelligence techniques to address three key challenges: perspective distortion correction, semantic understanding of facade elements, and spatial reasoning for PV layout optimization. Our four-stage pipeline processes images through geometric rectification, zero-shot semantic segmentation, Large Language Model (LLM) guided spatial reasoning, and energy simulation. Validation across 80 buildings in four countries demonstrates ro bust performance with mean area estimation errors of 6.2% ± 2.8% compared to expert annotations. The auto mated assessment requires approximately 100 seconds per building, a substantial gain in efficiency over manual methods. Simulated energy yield predictions confirm the method's reliability and applicability for regional poten tial studies, urban energy planning, and building-integrated photovoltaic (BIPV) deployment. Code is available at: https:github.com/CodeAXu/Solar-PV-Installation

Arega Getaneh Abate, Xiufeng Liu, Ruyu Liu et al.

Photovoltaic (PV) operators face substantial uncertainty in generation and short-term electricity prices. Continuous intraday markets enable producers to adjust their positions in real time, potentially improving revenues and reducing imbalance costs. We propose a feature-driven reinforcement learning (RL) approach for PV intraday trading that integrates data-driven features into the state and learns bidding policies in a sequential decision framework. The problem is cast as a Markov Decision Process with a reward that balances trading profit and imbalance penalties and is solved with Proximal Policy Optimization (PPO) using a predominantly linear, interpretable policy. Trained on historical market data and evaluated out-of-sample, the strategy consistently outperforms benchmark baselines across diverse scenarios. Extensive validation shows rapid convergence, real-time inference, and transparent decision rules. Learned weights highlight the central role of market microstructure and historical features. Taken together, these results indicate that feature-driven RL offers a practical, data-efficient, and operationally deployable pathway for active intraday participation by PV producers.