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User repurchase behavior prediction for integrated energy supply stations based on the user profiling method

Author

Listed:
  • Cen, Xiao
  • Chen, Zengliang
  • Chen, Haifeng
  • Ding, Chen
  • Ding, Bo
  • Li, Fei
  • Lou, Fangwei
  • Zhu, Zhenyu
  • Zhang, Hongyu
  • Hong, Bingyuan

Abstract

Under the guidance of the “Dual Carbon” goal, integrated energy supply stations have gradually become an essential facility for the energy transition. Promoting user repurchase has become a vital marketing strategy for integrated energy supply station enterprises. This paper proposes a prediction method based on the user profiling method to predict user repurchase behavior accurately. First, using an improved RFM model and the K-means algorithm, this paper constructs user profiles by dividing 10,000 users into three clusters: general-value developmental users, high-value new users, and low-value loyal users. Next, this paper uses the random forest, light gradient boosting machine, and extreme gradient boosting to predict the repurchase behavior of non-clustered users and the three clusters and compares their prediction performance. In addition, this paper adopts the stacking method for model fusion to improve the prediction performance further. The results show that the accuracies of the best prediction models for the three clusters are 93.28 %, 93.68 %, and 92.84 %, respectively. Finally, this paper provides each cluster with the corresponding prediction model of user repurchase behavior and marketing strategy. For the application scenario of integrated energy supply stations, this study accurately predicts the repurchase behavior of each cluster with unique consumption characteristics. It helps to provide personalized services for new energy vehicle consumers, optimize their consumption experience, and facilitate sustainable consumption.

Suggested Citation

  • Cen, Xiao & Chen, Zengliang & Chen, Haifeng & Ding, Chen & Ding, Bo & Li, Fei & Lou, Fangwei & Zhu, Zhenyu & Zhang, Hongyu & Hong, Bingyuan, 2024. "User repurchase behavior prediction for integrated energy supply stations based on the user profiling method," Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223030190
    DOI: 10.1016/j.energy.2023.129625
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    References listed on IDEAS

    as
    1. Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    2. Wei, Xinxing & Ban, Shengnan & Shi, Xilin & Li, Peng & Li, Yinping & Zhu, Shijie & Yang, Kun & Bai, Weizheng & Yang, Chunhe, 2023. "Carbon and energy storage in salt caverns under the background of carbon neutralization in China," Energy, Elsevier, vol. 272(C).
    3. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    4. Song, Yulong & Wang, Haidan & Ma, Yuan & Yin, Xiang & Cao, Feng, 2022. "Energetic, economic, environmental investigation of carbon dioxide as the refrigeration alternative in new energy bus/railway vehicles’ air conditioning systems," Applied Energy, Elsevier, vol. 305(C).
    5. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).
    6. Weiwei Zhang & Mingyan Wang, 2021. "An improved deep forest model for prediction of e-commerce consumers’ repurchase behavior," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-16, September.
    7. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
    8. Kesriklioğlu, Esma & Oktay, Erkan & Karaaslan, Abdulkerim, 2023. "Predicting total household energy expenditures using ensemble learning methods," Energy, Elsevier, vol. 276(C).
    9. Li, Zhao & Luo, Zujiang & Wang, Yan & Fan, Guanyu & Zhang, Jianmang, 2022. "Suitability evaluation system for the shallow geothermal energy implementation in region by Entropy Weight Method and TOPSIS method," Renewable Energy, Elsevier, vol. 184(C), pages 564-576.
    10. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    11. Cheng-Ju Liu & Tien-Shou Huang & Ping-Tsan Ho & Jui-Chan Huang & Ching-Tang Hsieh, 2020. "Machine learning-based e-commerce platform repurchase customer prediction model," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-15, December.
    12. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Moreno, Sinvaldo Rodrigues & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2021. "A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting," Energy, Elsevier, vol. 216(C).
    13. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan K., 2021. "MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid," Applied Energy, Elsevier, vol. 285(C).
    Full references (including those not matched with items on IDEAS)

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