データ希薄地域における風力エネルギーポテンシャルの解禁：ベトナム中央高原における戦略的気候行動のための深層学習フレームワーク

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:深層学習と気候モデルを組み合わせた高解像度ダウンスケーリング手法の実証例として参考になる。

🏢実務担当者:データが乏しい地域での風力事業開発において、投資リスク評価やサイト選定に活用できる。

🏛政策担当者:国家エネルギー計画の立案や土地利用計画において、科学的根拠に基づく風力ポテンシャル評価の重要性を示す。

As Vietnam accelerates its transition toward a sustainable energy future under ambitious national strategies like the Power Development Plan VIII (PDP8), a critical barrier persists: the lack of high-fidelity resource maps for strategic planning. This is particularly acute in data-scarce regions with high potential, such as Vietnam’s Central Highlands. This study aims to address this strategic gap by developing and validating a robust deep learning framework to produce an enhanced and detailed high-resolution wind energy map for this region, providing a conservative proof-of-concept for regional energy planning. The framework integrates coarse-resolution daily wind speed data from an ensemble of 11 global climate models with long-term observational records (1985–2023) from a sparse network of meteorological stations. To preserve synoptic extremes critical for wind power, the downscaling was conducted at a daily temporal resolution. A suite of advanced machine learning and deep learning models were systematically trained. To address the limitations of spatial interpolation in complex terrain, the downscaled 10 m wind speeds were extrapolated to a 149 m commercial hub-height using a terrain-aware power law, integrating high-resolution surface roughness data. A technical potential map (GWh/km²/year) was then generated by combining the turbine power curve (Enercon E-101) with a GIS-based availability index (AI), factoring in constraints such as elevation, slope, land cover and urban buffers. Deep learning architectures, particularly the convolutional neural network and XGBoost, significantly outperformed traditional models, achieving R-Pearson correlation coefficients exceeding 0.85 on daily predictions. Validation against ERA5 reanalysis data demonstrated that our ML-downscaled outputs substantially reduced severe negative biases present in ERA5 over complex terrain. The framework generated one of the most detailed wind energy maps for the region, identifying specific “hotspots” capable of generating significant yields (>6.0 GWh/km²/year) while strictly masking physically constrained zones. Projections under future shared socioeconomic pathways indicate that this wind resource potential is expected to remain robust. This study could provide a powerful, replicable methodological blueprint for de-risking renewable energy investment in data-scarce regions. By embedding high-resolution spatial covariates (DEM, roughness) into the vertical extrapolation rather than relying solely on 2D interpolation, it delivers an early, scientifically rigorous tool to guide national energy policy, optimize land-use planning and accelerate the sustainable expansion of wind energy for other regions in the country and abroad.

• semanticscholar https://www.emerald.com/ijccsm/article-pdf/18/1/629/11899373/ijccsm-09-2025-0332en.pdffirst seen 2026-10-12 05:27:18 · last seen 2026-10-14 05:56:36