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

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Title: Dynamic Multi-Objective Optimization for Enterprise Electricity Consumption with Time-Varying Carbon Emission Factors.
Authors: Chen, Jie1 (AUTHOR), Sun, Dexing1,2 (AUTHOR), Li, Feiwei1 (AUTHOR), Zhang, Junwei1,2 (AUTHOR), Wang, Zihao1 (AUTHOR), Lin, Guo1 (AUTHOR), Zhang, Xiaoshun2 (AUTHOR) zhangxiaoshun@mail.neu.edu.cn
Source: Energies (19961073). May2026, Vol. 19 Issue 9, p2073. 25p.
Subject Terms: *Multi-objective optimization, *Industrial energy consumption, *Greenhouse gases, *Long short-term memory, *Load forecasting (Electric power systems), *Carbon emissions, *Energy management, *Electricity pricing
Geographic Terms: China
Abstract: 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. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Data: Dynamic Multi-Objective Optimization for Enterprise Electricity Consumption with Time-Varying Carbon Emission Factors.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Jie%22">Chen, Jie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Dexing%22">Sun, Dexing</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Feiwei%22">Li, Feiwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Junwei%22">Zhang, Junwei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Zihao%22">Wang, Zihao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Guo%22">Lin, Guo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaoshun%22">Zhang, Xiaoshun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zhangxiaoshun@mail.neu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 9, p2073. 25p.
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  Data: *<searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Industrial+energy+consumption%22">Industrial energy consumption</searchLink><br />*<searchLink fieldCode="DE" term="%22Greenhouse+gases%22">Greenhouse gases</searchLink><br />*<searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br />*<searchLink fieldCode="DE" term="%22Load+forecasting+%28Electric+power+systems%29%22">Load forecasting (Electric power systems)</searchLink><br />*<searchLink fieldCode="DE" term="%22Carbon+emissions%22">Carbon emissions</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+management%22">Energy management</searchLink><br />*<searchLink fieldCode="DE" term="%22Electricity+pricing%22">Electricity pricing</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.3390/en19092073
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 25
        StartPage: 2073
    Subjects:
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Industrial energy consumption
        Type: general
      – SubjectFull: Greenhouse gases
        Type: general
      – SubjectFull: Long short-term memory
        Type: general
      – SubjectFull: Load forecasting (Electric power systems)
        Type: general
      – SubjectFull: Carbon emissions
        Type: general
      – SubjectFull: Energy management
        Type: general
      – SubjectFull: Electricity pricing
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Dynamic Multi-Objective Optimization for Enterprise Electricity Consumption with Time-Varying Carbon Emission Factors.
        Type: main
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            NameFull: Chen, Jie
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            NameFull: Sun, Dexing
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            NameFull: Li, Feiwei
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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            – Type: issn-print
              Value: 19961073
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              Value: 19
            – Type: issue
              Value: 9
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            – TitleFull: Energies (19961073)
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