グリーン低炭素データセンターのエネルギー消費に関するLSTM時系列予測：二元炭素データ要素管理の視点から
LSTM-Based Time-Series Forecasting for Green Low-Carbon Data Center Energy Consumption: A Dual-Carbon Data Element Management Perspective (原題)
Y. D. Bao, Yiyi Lu, C. Liu, L. Duan, K. Chen, Y. S. Nie, L. Liu, Y. W. Wang
🤖 gxceed AI 要約
日本語
データセンターの電力消費をLSTMで予測し、二酸化炭素排出削減（デュアルカーボン）目標達成を支援する枠組みを提案。注意機構付きスタックLSTMとPUE同時予測により、ARIMAなどと比較して予測誤差を最大34.6%削減。炭素データ管理パイプラインとCBAM対応への応用も議論。
English
This paper proposes an LSTM-based forecasting framework for data center energy consumption, integrated into a dual-carbon data management pipeline. The model, featuring attention pooling and multi-task PUE prediction, reduces RMSE by up to 34.6% over ARIMA. It also discusses applications for carbon accounting and CBAM exposure.
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
日本ではデータセンターの電力消費増加が課題であり、省エネ法やGXリーグの枠組みでのエネルギー管理に貢献。予測技術は再生可能エネルギー調達やカーボンニュートラル目標の達成に有用。
In the global GX context
Globally, data centers are under pressure to reduce carbon footprints. This forecasting approach supports operational efficiency and aligns with disclosure frameworks like TCFD and CSRD, aiding in carbon accounting and CBAM compliance.
👥 読者別の含意
🔬研究者:LSTMと注意機構の組み合わせが予測精度に与える影響と、マルチタスク学習の有効性を示す実証的知見。
🏢実務担当者:データセンターのエネルギー管理と炭素会計のための予測ツールとして活用可能。
🏛政策担当者:データセンターの省エネ政策やカーボンプライシング設計に示唆を与える。
📄 Abstract（原文）
Data centers already draw a fast-growing slice of global electricity, and operators cannot chase carbon peaking and carbon neutrality (“dual-carbon”) targets without knowing, ahead of time, how much power a facility is about to use. This paper builds an LSTM-based forecasting framework for data center energy consumption and places it inside a full-cycle dual-carbon data element management pipeline, one that tracks how operational data is collected, cataloged, shared across institutions, and eventually consumed by carbon-accounting services downstream. The forecaster itself is fairly simple to describe: a sliding-window preprocessing stage feeds a stacked LSTM encoder with attention-weighted temporal pooling, and a lightweight regression head predicts total facility power draw together with Power Usage Effectiveness (PUE) one to twenty-four steps ahead. We test it on a twelve-month, five-minute-resolution operational dataset built to reproduce typical hyperscale load and cooling patterns, benchmarking against ARIMA, Support Vector Regression (SVR), a Gated Recurrent Unit (GRU) network, and a vanilla single-layer LSTM. One-step-ahead Root Mean Square Error (RMSE) drops by 34.6% against ARIMA, 24.1% against SVR, 15.7% against GRU, and 9.8% against vanilla LSTM; an ablation study confirms that both attention pooling and multi-task PUE co-prediction contribute real, separable gains rather than overlapping ones. We also work through how this forecasting module behaves as a governed data product inside a cross-institution carbon data catalog, including downstream uses such as estimating carbon tariff exposure under the EU Carbon Border Adjustment Mechanism (CBAM). Taken together, the results suggest that pairing accurate short-horizon forecasting with structured data governance pays off on two fronts at once: operational efficiency and the auditability that low-carbon data center management increasingly requires.
🔗 Provenance — このレコードを発見したソース
- openalex https://www.aemjournal.org/index.php/AEM/article/view/4158first seen 2026-08-26 04:38:34
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