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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Summary: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 geographical coordinates such as production capacity, consumption capacity, latitude, and longitude from the European Hydrogen Observatory, was analyzed using advanced statistical techniques to ensure robust preprocessing and feature selection. It encompasses hydrogen consumption data from various production pathways (e.g., electrolysis and steam reforming), as reported by the European Clean Hydrogen Observatory, although specific production methods are not individually labeled. The study evaluates model performance using standard regression metrics, including mean absolute error, mean squared error, root mean squared error, coefficient of determination, and median absolute error. The random forest regressor led with 0.9789 and low error metrics (mean absolute error = 0.0010, mean squared error = 0.0030, and root mean squared error = 0.0034). However, to further improve prediction accuracy, a stacking ensemble model was developed by combining random forest regressor, multi-layer perceptron, support vector regressor, and gradient boosting regressor as base learners, with Ridge regression serving as the meta-learner. The stacked model significantly outperformed all individual models, achieving a coefficient of determination score of 0.9963, with a reduction in error metrics (mean absolute error = 0.0009, mean squared error = 0.0002, and root mean squared error = 0.0014). To gain deeper insights into feature importance, SHapley Additive Explanations analysis was conducted on the stacked model. The results indicate that latitude, longitude, and production capacity are the most influential factors affecting hydrogen consumption. This paper indicates that the stacked model can effectively anticipate hydrogen usage, helping researchers and policymakers optimize hydrogen distribution and consumption strategies for sustainable energy planning.
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ISSN:2509-4238
2509-4246
DOI:10.1007/s41660-025-00539-2