A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types

Jinrong Wu, Su Nguyen, Damminda Alahakoon (), Daswin De Silva, Nishan Mills, Prabod Rathnayaka, Harsha Moraliyage and Andrew Jennings
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Jinrong Wu: Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Su Nguyen: Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Damminda Alahakoon: Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Daswin De Silva: Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Nishan Mills: Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Prabod Rathnayaka: Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Harsha Moraliyage: Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Andrew Jennings: Infrastructure and Operations Group, La Trobe University, Bundoora, VIC 3086, Australia

Energies, 2024, vol. 17, issue 6, 1-18

Abstract: Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems.

Keywords: measurement and verification (M&V); energy baseline modeling; building energy consumption; interpretable machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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