Dynamic Simulation Approach for Estimating Solar Energy Potential of the Building Environment in the Macedonian Context
マケドニアの文脈における建築環境の太陽エネルギー潜在能力を推定するための動的シミュレーションアプローチ (AI 翻訳)
Kire Stavrov, Strahinja Trpevski, Natasha Malijanska Andreevska
🤖 gxceed AI 要約
日本語
本研究は、LiDARデータ、GIS解析、動的日射シミュレーションを統合し、建築環境における太陽光発電の潜在能力を評価する方法論を開発。屋根の形状・方位・気候条件が太陽光ポテンシャルに強く影響し、動的シミュレーションがPV導入に適した表面の特定に有効であることを示した。都市・国家規模の太陽台帳の構築に貢献する。
English
This study develops an integrated methodology combining LiDAR data, GIS analysis, and dynamic solar simulation to assess solar energy potential in the built environment. It finds that roof geometry, orientation, and climate strongly influence solar potential, and that dynamic simulation reliably identifies suitable surfaces for PV integration. The work supports the creation of solar cadastres at urban and national scales.
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、再生可能エネルギー導入拡大とカーボンニュートラル目標達成に向け、都市部での太陽光発電ポテンシャル評価が重要。本手法はGISと動的シミュレーションを組み合わせ、自治体や企業の再エネ導入計画に活用可能。SSBJ開示における再エネ調達戦略の裏付けにも寄与する。
In the global GX context
Globally, this research aligns with the push for decentralized renewable energy and data-driven urban planning. The GIS-based solar cadastre approach supports cities and countries in meeting renewable energy targets and enhancing energy resilience. It offers a replicable framework for integrating solar potential assessment into climate action plans and transition finance strategies.
👥 読者別の含意
🔬研究者:Provides a robust methodology for combining LiDAR, GIS, and dynamic simulation for solar potential assessment, useful for urban energy planning research.
🏢実務担当者:Offers a practical tool for identifying suitable rooftops for PV installation, aiding corporate renewable procurement and energy cost reduction.
🏛政策担当者:Demonstrates a data-driven approach for developing solar cadastres, supporting renewable energy policy and urban planning.
📄 Abstract（原文）
This research develops an integrated methodological framework for assessing solar energy potential within the built environment through dynamic simulation. The approach combines LiDAR-derived spatial data, GIS-based analysis, and time-dependent solar radiation modelling to evaluate both geometric and energy-related characteristics of roof surfaces. The methodology incorporates detailed roof documentation alongside dynamic solar simulation to quantify spatial parameters and corresponding solar energy capacity at building level. The results demonstrate that solar potential is strongly influenced by roof geometry, orientation, and local climatic conditions, and that dynamic simulation provides a reliable basis for identifying suitable surfaces for photovoltaic (PV) integration. The study further highlights the role of geospatial data in enabling precise energy assessments and supporting data-driven decision-making. By linking spatial analysis with energy modelling, this research contributes to the development of a solar cadastre applicable to urban and national scales. The findings underline the importance of dynamic, GIS-based approaches in advancing renewable energy strategies and facilitating the transition toward sustainable and decentralized energy systems within the built environment.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.38027/iccaua2026en0332first seen 2026-08-07 04:51:24
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