ベイズ不確実性下における波力エネルギー変換器の物理情報活用型および機械学習による出力予測：化石燃料発電との炭素フットプリント比較評価

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

🔬研究者:物理モデルとMLサロゲートをベイズUQで統合する手法は、他の再エネ技術の炭素削減評価にも応用可能な枠組みを提供する。

🏢実務担当者:海洋再エネプロジェクトの炭素削減効果を不確実性付きで定量化する手法として、投資家向け説明や環境価値報告に活用できる。

🏛政策担当者:海洋エネルギーの脱炭素ポテンシャルを監査可能な形で示すことで、導入支援策やインセンティブ設計の根拠となり得る。

AbstractOcean wave energy is a large, largely untapped renewable resource, but quantitative, uncertainty-aware comparisons of its climate benefit against fossil-fuel generation remain scarce. This study develops a combined physical and machine-learning (ML) modelling framework, wrapped in a Bayesian uncertainty-quantification (UQ) layer, to estimate the annual energy production (AEP) and life-cycle carbon-footprint mitigation of a reference two-body point-absorber wave energy converter (WEC), using the PacWave South (Oregon, USA) test site as a case study. A deterministic wave-to-wire physical model (linear wave power flux, a resonance-shaped capture-width response, and rated-power saturation) is cross-validated against a data-driven Gaussian Process Regression (GPR) surrogate trained on a synthetic power-performance dataset. The physical model's hydrodynamic parameters are calibrated with a four-chain random-walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) sampler (Gelman-Rubin R-hat <= 1.01 for all parameters), while the GPR supplies an independent, non-parametric posterior-predictive uncertainty. The two approaches converge on a closely comparable AEP (physical-Bayesian: 500.8 MWh yr⁻¹; ML-Bayesian: 453.5 MWh yr⁻¹), which are propagated, together with literature-based life-cycle carbon-intensity distributions for the WEC and for coal- and gas-fired generation, through a 100,000-draw Monte Carlo simulation. The device is estimated to avoid 376 t CO₂-eq yr⁻¹ (95% credible interval, CrI: 287-479) relative to an equivalent coal-fired generator (93.3% reduction) and 220 t CO₂-eq yr⁻¹ relative to natural gas (89.1% reduction), equivalent to roughly 9.4 kt CO₂ over a 25-year design life relative to coal. These results demonstrate that combining physically grounded and data-driven models within a rigorous Bayesian framework yields credible, auditable uncertainty bounds on the decarbonisation potential of wave energy, supporting evidence-based marine-energy policy and investment appraisal.

• openalex http://journals.tultech.eu/index.php/eil/article/view/703first seen 2026-10-06 05:05:11