Artificial Intelligence-Based Urban Rooftop Photovoltaic Potential Assessment: A Scoping Review
人工知能を用いた都市の屋上太陽光発電ポテンシャル評価：スコーピングレビュー (AI 翻訳)
Ran Tian, Zongwu Xu, Jun Han, Jing Li
🤖 gxceed AI 要約
日本語
本スコーピングレビューは、屋上太陽光発電（RPV）ポテンシャル評価における人工知能（AI）の活用方法を体系的に整理した。524件の論文から48件を選定し、機械学習から深層学習への移行や、ハイブリッドワークフローの出現を明らかにした。ジオメトリベース、パラメータベース、エンドツーエンド推定、ハイブリッドの4つのワークフローを類型化し、それぞれの自動化・拡張性・解釈可能性・物理的現実性のバランスを評価した。今後の課題として、転移可能性、ベンチマークの不均一性、不確実性伝播、データ依存性を指摘し、より汎用的で物理的に整合した都市エネルギー計画への方向性を示した。
English
This scoping review systematically examines the methodologies and workflow evolution of AI-based urban rooftop photovoltaic (RPV) potential assessment. From 524 initial articles, 48 studies were selected, revealing a shift from conventional machine learning to deep learning, multimodal learning, and hybrid workflows. Four workflow paradigms were identified: geometry-based, parameter-based, end-to-end estimation, and hybrid, each balancing automation, scalability, interpretability, and physical realism. Challenges include transferability, benchmarking heterogeneity, uncertainty propagation, and data dependency. The study provides a comparative framework to guide future generalizable and physically informed urban energy modeling for solar-integrated planning.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、建築物の省エネ基準や再エネ導入義務化が進む中、本レビューのワークフロー類型は自治体や不動産事業者が屋上太陽光ポテンシャルを効率的に把握する際に参考となる。特に、地理的条件や建物形状が多様な日本の都市において、AIによる簡易評価手法の導入は、FIT/FIP制度との連携や、地域エネルギー計画の高度化に貢献しうる。
In the global GX context
Globally, this review offers a timely synthesis as cities worldwide accelerate solar deployment to meet net-zero targets. The workflow taxonomy helps practitioners and researchers navigate the fragmented landscape of AI-based rooftop PV assessment, highlighting trade-offs between accuracy and scalability. It underscores the need for standardized benchmarks and physically constrained models, which are critical for credible urban energy planning and integration with building codes and grid infrastructure.
👥 読者別の含意
🔬研究者:Identifies key gaps and future directions for developing more generalizable and physically informed AI models for rooftop solar assessment.
🏢実務担当者:Provides a structured overview of available AI methods and workflows to help urban planners and solar developers select appropriate assessment tools for local contexts.
🏛政策担当者:Offers insights on the current capabilities and limitations of AI-driven solar potential mapping, informing decisions on integrating such tools into urban energy planning and incentive programs.
📄 Abstract（原文）
Urban rooftop photovoltaic (RPV) systems are crucial for energy transition in the built environment. Although artificial intelligence (AI) has been widely adopted in this domain, existing studies remain methodologically fragmented and lack a workflow-oriented comparative synthesis. This study conducts a scoping review to systematically examine the methodological development and workflow evolution of AI-based urban RPV potential assessment. A total of 524 articles were initially retrieved from Web of Science and Scopus. In total, 48 peer-reviewed studies were selected through a structured screening process. The results reveal a clear transition from conventional machine learning toward deep learning, multimodal learning, and increasingly integrated hybrid workflows. Geometry-based, parameter-based, end-to-end estimation, and hybrid workflows were identified as the dominant workflow paradigms, reflecting different balances between automation, scalability, interpretability, and physical realism. The review further highlights challenges related to transferability, benchmarking heterogeneity, uncertainty propagation, and data dependency under heterogeneous urban conditions. Overall, this study provides a workflow-oriented synthesis and comparative analytical framework of AI-based urban RPV potential assessment through a workflow taxonomy perspective highlights future directions toward more generalizable, physically informed, and adaptive urban energy modelling frameworks for solar-integrated urban planning and built-environment decarbonization, and intelligent urban energy system development across heterogeneous urban contexts.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/buildings16112226first seen 2026-06-03 05:08:29
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。