IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i8p1931-d1377992.html
   My bibliography  Save this article

Energy Efficiency Assessment and Prediction Based on Indicator System, PSO + AHP − FCE Model and Regression Algorithm

Author

Listed:
  • Yan Bai

    (College of Electrical and Information Engineering, Beihua University, Jilin 132021, China)

  • Xingyi Ma

    (College of Electrical and Information Engineering, Beihua University, Jilin 132021, China)

  • Jing Zhang

    (College of Electrical and Information Engineering, Beihua University, Jilin 132021, China)

  • Lei Zhang

    (College of Electrical and Information Engineering, Beihua University, Jilin 132021, China)

  • Jing Bai

    (College of Electrical and Information Engineering, Beihua University, Jilin 132021, China)

Abstract

Energy-intensive enterprises lack a scientific and effective energy efficiency assessment framework and methodology. This lack leads to an inaccurate understanding of energy usage and its benefits. As a result, there is energy wastage and loss. This wastage and loss negatively affect product costs. They also present a challenge to effective energy management. To address these issues, this paper introduces a novel, comprehensive energy efficiency evaluation system. This system integrates both qualitative and quantitative measures. It proposes an evaluation model based on the Particle Swarm Optimization (PSO) combined with the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), wherein PSO is employed to optimize the weights determined by AHP, ensuring that the significance attributed to various indicators is scientific, objective, and rational. The FCE method is utilized to convert diverse factors affecting corporate energy efficiency, across different types and scales, into standardized 0–1 values, enabling a comparative analysis of the impact of each process and indicator on energy efficiency. Furthermore, the paper introduces an energy efficiency prediction model employing a multivariate linear regression algorithm, which demonstrates a good fit, facilitating the transition from retrospective energy efficiency evaluation to proactive improvements. Utilizing data on actual consumption of water, electricity, and steam from an enterprise, along with expert assessments on the implementation levels of new processes, technologies, equipment, personnel scheduling proficiency, steam recovery rates, and adherence to policies and assessments, a simulation experiment of the proposed model was conducted using Python. The evaluation yielded an energy efficiency score of 0.68; this is consistent with the real-world scenario of the studied enterprise. The predicted mean square error of 9.035416039503998 × 10 − 9 indicates a high model accuracy, validating the practical applicability and effectiveness of the proposed approach.

Suggested Citation

  • Yan Bai & Xingyi Ma & Jing Zhang & Lei Zhang & Jing Bai, 2024. "Energy Efficiency Assessment and Prediction Based on Indicator System, PSO + AHP − FCE Model and Regression Algorithm," Energies, MDPI, vol. 17(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1931-:d:1377992
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/8/1931/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/8/1931/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, vol. 187(C), pages 203-215.
    2. Pöschl, Martina & Ward, Shane & Owende, Philip, 2010. "Evaluation of energy efficiency of various biogas production and utilization pathways," Applied Energy, Elsevier, vol. 87(11), pages 3305-3321, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huopana, Tuomas & Song, Han & Kolehmainen, Mikko & Niska, Harri, 2013. "A regional model for sustainable biogas electricity production: A case study from a Finnish province," Applied Energy, Elsevier, vol. 102(C), pages 676-686.
    2. Mohamed Elhefnawy & Ahmed Ragab & Mohamed-Salah Ouali, 2023. "Polygon generation and video-to-video translation for time-series prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 261-279, January.
    3. Xu, Yang & Li, Ming-Jia & Zheng, Zhang-Jing & Xue, Xiao-Dai, 2018. "Melting performance enhancement of phase change material by a limited amount of metal foam: Configurational optimization and economic assessment," Applied Energy, Elsevier, vol. 212(C), pages 868-880.
    4. Thompson, T.M. & Young, B.R. & Baroutian, S., 2020. "Pelagic Sargassum for energy and fertiliser production in the Caribbean: A case study on Barbados," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    5. Bekkering, J. & Hengeveld, E.J. & van Gemert, W.J.T. & Broekhuis, A.A., 2015. "Will implementation of green gas into the gas supply be feasible in the future?," Applied Energy, Elsevier, vol. 140(C), pages 409-417.
    6. Ciliberti, Carlo & Jordaan, Sarah M. & Smith, Stephen V. & Spatari, Sabrina, 2016. "A life cycle perspective on land use and project economics of electricity from wind and anaerobic digestion," Energy Policy, Elsevier, vol. 89(C), pages 52-63.
    7. Zeng, Zhichen & Ni, Dong & Xiao, Gang, 2022. "Real-time heliostat field aiming strategy optimization based on reinforcement learning," Applied Energy, Elsevier, vol. 307(C).
    8. Santagata, R. & Ripa, M. & Ulgiati, S., 2017. "An environmental assessment of electricity production from slaughterhouse residues. Linking urban, industrial and waste management systems," Applied Energy, Elsevier, vol. 186(P2), pages 175-188.
    9. Si, Tong & Wang, Chunbo & Liu, Ruiqi & Guo, Yusheng & Yue, Shuang & Ren, Yujie, 2020. "Multi-criteria comprehensive energy efficiency assessment based on fuzzy-AHP method: A case study of post-treatment technologies for coal-fired units," Energy, Elsevier, vol. 200(C).
    10. Bacenetti, Jacopo & Sala, Cesare & Fusi, Alessandra & Fiala, Marco, 2016. "Agricultural anaerobic digestion plants: What LCA studies pointed out and what can be done to make them more environmentally sustainable," Applied Energy, Elsevier, vol. 179(C), pages 669-686.
    11. Höhn, J. & Lehtonen, E. & Rasi, S. & Rintala, J., 2014. "A Geographical Information System (GIS) based methodology for determination of potential biomasses and sites for biogas plants in southern Finland," Applied Energy, Elsevier, vol. 113(C), pages 1-10.
    12. Massimiliano Boccarossa & Martina Di Addario & Adele Folino & Fabio Tatàno, 2021. "Scenarios of Bioenergy Recovery from Organic Fraction of Residual Municipal Waste in the Marche Region (Italy)," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    13. Herz, Gregor & Reichelt, Erik & Jahn, Matthias, 2017. "Design and evaluation of a Fischer-Tropsch process for the production of waxes from biogas," Energy, Elsevier, vol. 132(C), pages 370-381.
    14. Seckin, Candeniz & Bayulken, Ahmet R., 2013. "Extended Exergy Accounting (EEA) analysis of municipal wastewater treatment – Determination of environmental remediation cost for municipal wastewater," Applied Energy, Elsevier, vol. 110(C), pages 55-64.
    15. Uddin, Md Mosleh & Simson, Amanda & Wright, Mark Mba, 2020. "Techno-economic and greenhouse gas emission analysis of dimethyl ether production via the bi-reforming pathway for transportation fuel," Energy, Elsevier, vol. 211(C).
    16. Wang, Kun & He, Ya-Ling & Zhu, Han-Hui, 2017. "Integration between supercritical CO2 Brayton cycles and molten salt solar power towers: A review and a comprehensive comparison of different cycle layouts," Applied Energy, Elsevier, vol. 195(C), pages 819-836.
    17. Katinas, Vladislovas & Marčiukaitis, Mantas & Perednis, Eugenijus & Dzenajavičienė, Eugenija Farida, 2019. "Analysis of biodegradable waste use for energy generation in Lithuania," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 559-567.
    18. Ma, Zhao & Li, Ming-Jia & Zhang, K. Max & Yuan, Fan, 2021. "Novel designs of hybrid thermal energy storage system and operation strategies for concentrated solar power plant," Energy, Elsevier, vol. 216(C).
    19. Ma, Teng & Li, Ming-Jia & Xu, Jin-Liang & Cao, Feng, 2019. "Thermodynamic analysis and performance prediction on dynamic response characteristic of PCHE in 1000 MW S-CO2 coal fired power plant," Energy, Elsevier, vol. 175(C), pages 123-138.
    20. Nur Izzah Hamna A. Aziz & Marlia M. Hanafiah & Shabbir H. Gheewala & Haikal Ismail, 2020. "Bioenergy for a Cleaner Future: A Case Study of Sustainable Biogas Supply Chain in the Malaysian Energy Sector," Sustainability, MDPI, vol. 12(8), pages 1-24, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1931-:d:1377992. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.