Deep learning of temporal renewable energy patterns for optimizing Power-to-X processes
時系列再生可能エネルギーパターンの深層学習によるPower-to-Xプロセスの最適化 (AI 翻訳)
Collin Smith, Huey Xin Loong, Oliver Cook, Han You Low, L. Torrente‐Murciano
🤖 gxceed AI 要約
日本語
本論文では、深層学習（CNN・RNN）を用いて太陽光・風力発電の時系列パターンを学習し、Power-to-Xプロセス（グリーンアンモニア製造）の最適設計を実現する。設備容量（太陽光パネル、電解槽、水素貯蔵）をコスト最小化の観点から決定し、5-15%の誤差で経済的最適設計を予測可能。これにより、立地選定の初期スクリーニングが迅速化される。
English
This paper demonstrates that deep convolutional and recurrent neural networks can learn temporal patterns of solar and wind power generation to optimize Power-to-X processes, specifically green ammonia production. The networks predict optimal capacities for solar panels, electrolysers, and hydrogen storage to minimize production cost, achieving 5-15% error. This enables rapid screening of locations for economic feasibility, laying the foundation for more complex optimizations.
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
日本ではグリーンアンモニアを中心としたPower-to-Xが注目されている。本手法を用いることで、国内の各地域の再生可能エネルギーパターンに基づき、迅速な経済性評価が可能となり、投資判断や政策立案に貢献できる。
In the global GX context
Globally, Power-to-X is critical for decarbonizing hard-to-abate sectors. This AI-driven approach significantly reduces the time and cost for initial feasibility assessments, accelerating the deployment of green hydrogen and ammonia projects worldwide.
👥 読者別の含意
🔬研究者:Provides a novel application of deep learning to process engineering, showing 5-15% prediction error for optimal design.
🏢実務担当者:Enables rapid screening of locations for Power-to-X projects, reducing upfront feasibility study costs.
🏛政策担当者:Offers a tool to assess renewable energy resource potential for Power-to-X, informing subsidy and zoning policies.
📄 Abstract（原文）
The inherently intermittent nature of renewable energy and its geographic variations make the optimisation of Power-to-X processes dependent on the local temporal characteristics of solar and wind energy generation profiles. By using green ammonia production as a case study, this paper demonstrates that deep convolutional and recurrent neural networks can successfully learn key characteristics of renewable power profiles (capacity production, peak supply and intermittency) and accordingly determine the capacity of the solar panels/wind turbines, electrolysers and hydrogen storage to minimise the cost of green ammonia production. By learning implicit cost relationships, deep neural networks can also predict the economically optimal process design with an error of 5-15%. As a result, neural networks can be implemented for rapid screening of locations for initial economic feasibility of Power-to-X processes, providing the foundations for more challenging optimizations such as those considering decades of weather-based renewable power profiles and complex energy system models.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.18573/jae.54first seen 2026-07-03 05:44:56
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