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Dynamic Multi-Objective Optimization for Enterprise Electricity Consumption with Time-Varying Carbon Emission Factors

時間変動炭素排出係数を考慮した企業電力消費の動的多目的最適化 (AI 翻訳)

Jiehui Chen, Dexing Sun, Feiwei Li, Junwei Zhang, Zihao Wang, Guo Lin, Xiaoshun Zhang

Energies📚 査読済 / ジャーナル2026-04-24#AI×ESGOrigin: CN経営インパクト: コスト削減対象セクター: manufacturing
DOI: 10.3390/en19092073
原典: https://doi.org/10.3390/en19092073

🤖 gxceed AI 要約

日本語

本論文は、高精度な負荷予測と最適スケジューリングを統合した産業用電力消費の動的多目的最適化フレームワークを提案する。改良型LSTMモデル（DGO-TA-LSTM）で負荷予測を行い、NSGA-IIとIGTDを用いて電力コスト、炭素排出量、負荷偏差の3目的を最適化する。中国南部の高エネルギー産業企業の実データを用いたケーススタディでは、電力コスト1.9%削減、炭素排出量30%削減を達成した。

English

This paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. An improved LSTM model (DGO-TA-LSTM) is used for forecasting, and NSGA-II with IGTD optimizes three objectives: electricity cost, carbon emissions, and load deviation. A case study on a high-energy industrial enterprise in southern China achieved a 1.9% reduction in electricity cost and a 30% reduction in carbon emissions.

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

日本の産業界では、電力コスト削減と脱炭素の両立が急務であり、本手法は工場のエネルギー管理システム（EMS）に組み込むことで、SSBJ開示や省エネ法対応にも貢献できる。時間変動排出係数とピークシフトを考慮した最適化は、再エネ導入拡大に伴う需給調整にも有用である。

In the global GX context

Globally, this work aligns with the growing need for operational decarbonization in energy-intensive industries. The integration of load forecasting with multi-objective optimization under time-varying carbon factors offers a scalable approach for corporate climate action, complementing disclosure frameworks like TCFD and CSRD by providing concrete emission reduction strategies.

👥 読者別の含意

🔬研究者:Provides a novel integration of LSTM-based forecasting with NSGA-II for multi-objective industrial energy optimization, offering a benchmark for future research.

🏢実務担当者:Offers a practical framework for industrial facilities to reduce electricity costs and carbon emissions simultaneously, which can be integrated into existing energy management systems.

🏛政策担当者:Demonstrates the potential of AI-driven optimization in achieving industrial decarbonization targets, informing policies that encourage adoption of such technologies.

📄 Abstract（原文）

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a 1.9% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises.

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