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

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

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...

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Bibliographic Details
Main Authors: Zhihui Zhao, Minjuan Wang, Jin Wei, Xiao Cen, Shengnan Du, Ziwen Wu, Huanying Liu, Weiqiang Wang
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Energies
Subjects:
multi-energy supply station
revenue forecasting
data-driven model
SHAP algorithm
interpretability
Online Access:https://www.mdpi.com/1996-1073/18/7/1624
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