IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i1p789-d1022172.html
   My bibliography  Save this article

Pump Feature Construction and Electrical Energy Consumption Prediction Based on Feature Engineering and LightGBM Algorithm

Author

Listed:
  • Zhiqiang Yin

    (Big Data Research Laboratory of Process Industry, Computer and Artificial Intelligence, Alibaba Cloud Big Data College, Changzhou University, Changzhou 213000, China
    Current address: School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213000, China.)

  • Lin Shi

    (Big Data Research Laboratory of Process Industry, Computer and Artificial Intelligence, Alibaba Cloud Big Data College, Changzhou University, Changzhou 213000, China
    Current address: School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213000, China.)

  • Junru Luo

    (Big Data Research Laboratory of Process Industry, Computer and Artificial Intelligence, Alibaba Cloud Big Data College, Changzhou University, Changzhou 213000, China
    Current address: School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213000, China.)

  • Shoukun Xu

    (Big Data Research Laboratory of Process Industry, Computer and Artificial Intelligence, Alibaba Cloud Big Data College, Changzhou University, Changzhou 213000, China
    Current address: School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213000, China.)

  • Yang Yuan

    (Big Data Research Laboratory of Process Industry, Computer and Artificial Intelligence, Alibaba Cloud Big Data College, Changzhou University, Changzhou 213000, China
    Current address: School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213000, China.)

  • Xinxin Tan

    (College of Microelectronics and Control Engineering, Changzhou University, Changzhou 213000, China
    Current address: School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213000, China.)

  • Jiaqun Zhu

    (Big Data Research Laboratory of Process Industry, Computer and Artificial Intelligence, Alibaba Cloud Big Data College, Changzhou University, Changzhou 213000, China)

Abstract

In recent years, research on improving the energy consumption ratio of pumping equipment through control algorithms has improved. However, the actual behavior of pump equipment and pump characteristic information do not always correspond, resulting in deviations between the calculated energy consumption operating point and the actual operating point. This eventually results in wasted power. To solve this problem, the data from circulating pumping equipment in a large pumping facility are analyzed, and the necessary characteristics of pumping equipment electrical energy consumption are analyzed through a subset of mechanism expansion feature engineering using the Pearson correlation coefficient algorithm. Based on this, a pump energy consumption prediction method based on LightGBM is constructed and compared with other algorithm models. To improve the generalization ability of the model, rules applicable to pump power energy consumption prediction are proposed, and the model features and processes are reduced. Based on the mechanistic model, 18 features related to electric energy consumption are selected, and 6 necessary features of pump electric energy consumption are screened by feature engineering. The experimental results show that the LightGBM regression algorithm has a significant prediction effect with R 2 = 0.94 . After the importance analysis, three features that are strongly related to pump energy consumption are finally screened out. According to the prediction results, the feature engineering dataset was selected and the pump electrical energy consumption was predicted based on the LightGBM algorithm, which can significantly reduce the problem of deviation in the prediction of the electrical energy consumption of pumping equipment.

Suggested Citation

  • Zhiqiang Yin & Lin Shi & Junru Luo & Shoukun Xu & Yang Yuan & Xinxin Tan & Jiaqun Zhu, 2023. "Pump Feature Construction and Electrical Energy Consumption Prediction Based on Feature Engineering and LightGBM Algorithm," Sustainability, MDPI, vol. 15(1), pages 1-17, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:1:p:789-:d:1022172
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/789/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/1/789/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arun Shankar, Vishnu Kalaiselvan & Umashankar, Subramaniam & Paramasivam, Shanmugam & Hanigovszki, Norbert, 2016. "A comprehensive review on energy efficiency enhancement initiatives in centrifugal pumping system," Applied Energy, Elsevier, vol. 181(C), pages 495-513.
    2. Longo, S. & Mauricio-Iglesias, M. & Soares, A. & Campo, P. & Fatone, F. & Eusebi, A.L. & Akkersdijk, E. & Stefani, L. & Hospido, A., 2019. "ENERWATER – A standard method for assessing and improving the energy efficiency of wastewater treatment plants," Applied Energy, Elsevier, vol. 242(C), pages 897-910.
    3. Huican Luo & Peijian Zhou & Lingfeng Shu & Jiegang Mou & Haisheng Zheng & Chenglong Jiang & Yantian Wang, 2022. "Energy Performance Curves Prediction of Centrifugal Pumps Based on Constrained PSO-SVR Model," Energies, MDPI, vol. 15(9), pages 1-19, May.
    4. Johnson, Hilary A. & Simon, Kevin P. & Slocum, Alexander H., 2021. "Data analytics and pump control in a wastewater treatment plant," Applied Energy, Elsevier, vol. 299(C).
    5. Zhang, Chaobo & Li, Junyang & Zhao, Yang & Li, Tingting & Chen, Qi & Zhang, Xuejun & Qiu, Weikang, 2021. "Problem of data imbalance in building energy load prediction: Concept, influence, and solution," Applied Energy, Elsevier, vol. 297(C).
    6. Hieninger, Thomas & Schmidt-Vollus, Ronald & Schlücker, Eberhard, 2021. "Improving energy efficiency of individual centrifugal pump systems using model-free and on-line optimization methods," Applied Energy, Elsevier, vol. 304(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yichao Xie & Bowen Zhou & Zhenyu Wang & Bo Yang & Liaoyi Ning & Yanhui Zhang, 2023. "Industrial Carbon Footprint (ICF) Calculation Approach Based on Bayesian Cross-Validation Improved Cyclic Stacking," Sustainability, MDPI, vol. 15(19), pages 1-35, September.

    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. Li, Wei & Yang, Qiaoyue & Yang, Yi & Ji, Leilei & Shi, Weidong & Agarwal, Ramesh, 2024. "Optimization of pump transient energy characteristics based on response surface optimization model and computational fluid dynamics," Applied Energy, Elsevier, vol. 362(C).
    2. Wenqiang Zhou & Peijian Zhou & Chun Xiang & Yang Wang & Jiegang Mou & Jiayi Cui, 2023. "A Review of Bionic Structures in Control of Aerodynamic Noise of Centrifugal Fans," Energies, MDPI, vol. 16(11), pages 1-24, May.
    3. Fernández Oro, J.M. & Barrio Perotti, R. & Galdo Vega, M. & González, J., 2023. "Effect of the radial gap size on the deterministic flow in a centrifugal pump due to impeller-tongue interactions," Energy, Elsevier, vol. 278(PA).
    4. Johnson, Hilary A. & Simon, Kevin P. & Slocum, Alexander H., 2021. "Data analytics and pump control in a wastewater treatment plant," Applied Energy, Elsevier, vol. 299(C).
    5. Diaz, Cesar & Ruiz, Fredy & Patino, Diego, 2017. "Modeling and control of water booster pressure systems as flexible loads for demand response," Applied Energy, Elsevier, vol. 204(C), pages 106-116.
    6. Xu, Wei & Chen, Genglin & Shi, Huijin & Zhang, Pengcheng & Chen, Xuemei, 2023. "Research on operational characteristics of coal power centrifugal fans at off-design working conditions based on flap-angle adjustment," Energy, Elsevier, vol. 284(C).
    7. Meng Wang & Junqi Yu & Meng Zhou & Wei Quan & Renyin Cheng, 2023. "Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM," Sustainability, MDPI, vol. 15(24), pages 1-23, December.
    8. Filipe, Jorge & Bessa, Ricardo J. & Reis, Marisa & Alves, Rita & Póvoa, Pedro, 2019. "Data-driven predictive energy optimization in a wastewater pumping station," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. José Ignacio Sarasúa & Guillermo Martínez-Lucas & Carlos A. Platero & José Ángel Sánchez-Fernández, 2018. "Dual Frequency Regulation in Pumping Mode in a Wind–Hydro Isolated System," Energies, MDPI, vol. 11(11), pages 1-17, October.
    10. Gan, Xingcheng & Pavesi, Giorgio & Pei, Ji & Yuan, Shouqi & Wang, Wenjie & Yin, Tingyun, 2022. "Parametric investigation and energy efficiency optimization of the curved inlet pipe with induced vane of an inline pump," Energy, Elsevier, vol. 240(C).
    11. Michał Napierała, 2022. "A Study on Improving Economy Efficiency of Pumping Stations Based on Tariff Changes," Energies, MDPI, vol. 15(3), pages 1-17, January.
    12. Hao Wang & Peijian Zhou & Ting Chen & Jiegang Mou & Jiayi Cui & Huiming Zhang, 2023. "Optimization of Liquid−Liquid Mixing in a Novel Mixer Based on Hybrid SVR-DE Model," Energies, MDPI, vol. 16(4), pages 1-17, February.
    13. Chen, Ruijun & Tsay, Yaw-Shyan & Zhang, Ting, 2023. "A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective," Energy, Elsevier, vol. 262(PA).
    14. Safarbek Oshurbekov & Vadim Kazakbaev & Vladimir Prakht & Vladimir Dmitrievskii, 2021. "Improving Reliability and Energy Efficiency of Three Parallel Pumps by Selecting Trade-Off Operating Points," Mathematics, MDPI, vol. 9(11), pages 1-19, June.
    15. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
    16. Chengshuo Wu & Jun Yang & Shuai Yang & Peng Wu & Bin Huang & Dazhuan Wu, 2023. "A Review of Fluid-Induced Excitations in Centrifugal Pumps," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
    17. Vadim Kazakbaev & Vladimir Prakht & Vladimir Dmitrievskii & Safarbek Oshurbekov & Dmitry Golovanov, 2020. "Life Cycle Energy Cost Assessment for Pump Units with Various Types of Line-Start Operating Motors Including Cable Losses," Energies, MDPI, vol. 13(14), pages 1-15, July.
    18. Fan, Cheng & Chen, Ruikun & Mo, Jinhan & Liao, Longhui, 2024. "Personalized federated learning for cross-building energy knowledge sharing: Cost-effective strategies and model architectures," Applied Energy, Elsevier, vol. 362(C).
    19. Liu, Jiahong & Wang, Jia & Ding, Xiangyi & Shao, Weiwei & Mei, Chao & Li, Zejin & Wang, Kaibo, 2020. "Assessing the mitigation of greenhouse gas emissions from a green infrastructure-based urban drainage system," Applied Energy, Elsevier, vol. 278(C).
    20. Torregrossa, Dario & Hansen, Joachim & Hernández-Sancho, Francesc & Cornelissen, Alex & Schutz, Georges & Leopold, Ulrich, 2017. "A data-driven methodology to support pump performance analysis and energy efficiency optimization in Waste Water Treatment Plants," Applied Energy, Elsevier, vol. 208(C), pages 1430-1440.

    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:jsusta:v:15:y:2023:i:1:p:789-:d:1022172. 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.