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Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality

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  • Zhihui Zhao

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Key Laboratory of Pollution Control for Port-Petrochemical Industry, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Minjuan Wang

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Key Laboratory of Pollution Control for Port-Petrochemical Industry, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Jin Wei

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Key Laboratory of Pollution Control for Port-Petrochemical Industry, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Xiao Cen

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Key Laboratory of Pollution Control for Port-Petrochemical Industry, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Shengnan Du

    (College of Mechanical and Automotive Engineering, Ningbo University of Technology, Ningbo 315211, China)

  • Ziwen Wu

    (National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing 102249, China)

  • Huanying Liu

    (Sinopec Sales Co., Ltd. Zhejiang Hangzhou Petroleum Branch, Hangzhou 310013, China)

  • Weiqiang Wang

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Key Laboratory of Pollution Control for Port-Petrochemical Industry, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

Abstract

The transition towards carbon neutrality and sustainable urban development necessitates innovative strategies for managing multi-energy supply stations (MESS), which integrate oil, gas, electricity, and hydrogen to support diversified energy demands. Existing revenue prediction models for MESS lack interpretability and multi-energy adaptability, hindering actionable insights for sustainable operations. This study proposes a novel Shapley additive explanations (SHAP)-driven machine learning framework for multi-energy supply station revenue forecasting. By leveraging real-world consumption data from Hangzhou West Lake Tanghe Station, we constructed a dataset with nine critical parameters, including energy types, transaction frequency, and temporal features. Four machine learning models—decision tree regression, random forest (RF), support vector regression, and multilayer perceptron—were evaluated using MAE, MSE, and R 2 metrics. The RF model achieved an R 2 of 0.98, demonstrating superior accuracy in predicting hourly gross transaction values. SHAP analysis further identified consumption volume and transaction frequency as the most influential factors, providing actionable insights for operational optimization. This research not only advances the scientific management of MESS but also contributes to carbon emission reduction by enabling data-driven resource allocation. The proposed framework offers policymakers and industry stakeholders a scalable tool to accelerate urban energy transitions under carbon neutrality goals, bridging the gap between predictive analytics and sustainable infrastructure planning.

Suggested Citation

  • Zhihui Zhao & Minjuan Wang & Jin Wei & Xiao Cen & Shengnan Du & Ziwen Wu & Huanying Liu & Weiqiang Wang, 2025. "Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality," Energies, MDPI, vol. 18(7), pages 1-25, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1624-:d:1619304
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    References listed on IDEAS

    as
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