Enhancing Hydrogen Energy Consumption Prediction Based on Stacked Machine Learning Model with Shapley Additive Explanations

Enhancing hydrogen-based energy systems requires accurate hydrogen consumption forecast. This paper compares random forest regressor, multi-layer perceptron, support vector regressor, and gradient boosting regressor to forecast hydrogen consumption, using production and consumption capacity and geog...

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Bibliographic Details
Published in:	Process integration and optimization for sustainability Vol. 9; no. 5; pp. 1847 - 1868
Main Authors:	Elshewey, Ahmed M., Hassan, Samah A. Z., Youssef, Rasha Y., El-Bakry, Hazem M., Osman, Ahmed M.
Format:	Journal Article
Language:	English
Published:	Singapore Springer Nature Singapore 01.11.2025 Springer Nature B.V
Subjects:	Accuracy Algorithms Alternative energy sources Deep learning Economics and Management Efficiency Electrolysis Energy consumption Energy management Energy Policy Engineering Error reduction Fuel cells Geographical coordinates Green hydrogen Hydrogen Hydrogen production Hydrogen-based energy Industrial and Production Engineering Industrial Chemistry/Chemical Engineering Latitude Localization Longitude Machine learning Multilayer perceptrons Multilayers Nanocomposites Neural networks Optimization Original Research Paper Production methods Reforming Regression analysis Renewable resources Root-mean-square errors Statistical analysis Sustainable Development Sustainable energy Trends Waste Management/Waste Technology Zinc oxides Renewable energy systems Hydrogen consumption SHAP analysis Stacking model Hydrogen energy consumption prediction Machine learning
ISSN:	2509-4238, 2509-4246
Online Access:	Get full text
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