Smart City Gnosys

Smart city article details

Title	A Comparative Analysis Of Machine Learning-Based Energy Baseline Models Across Multiple Building Types
ID_Doc	762
Authors	Wu J.; Nguyen S.; Alahakoon D.; De Silva D.; Mills N.; Rathnayaka P.; Moraliyage H.; Jennings A.
Year	2024
Published	Energies, 17, 6
DOI	http://dx.doi.org/10.3390/en17061285
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. © 2024 by the authors.
Author Keywords	building energy consumption; energy baseline modeling; interpretable machine learning; measurement and verification (M&V)

Similar Articles

Id	Similarity	Authors	Title	Published
23280	0.929	Huang J.; Algahtani M.; Kaewunruen S.	Energy Forecasting In A Public Building: A Benchmarking Analysis On Long Short-Term Memory (Lstm), Support Vector Regression (Svr), And Extreme Gradient Boosting (Xgboost) Networks	Applied Sciences (Switzerland), 12, 19 (2022)
54322	0.904	Ardabili S.; Abdolalizadeh L.; Mako C.; Torok B.; Mosavi A.	Systematic Review Of Deep Learning And Machine Learning For Building Energy	Frontiers in Energy Research, 10 (2022)
36066	0.902	Ajagunsegun T.; Li J.; Bamisile O.; Ohakwe C.	Machine Learning-Based System For Managing Energy Efficiency Of Public Buildings: An Approach Towards Smart Cities	2022 4th Asia Energy and Electrical Engineering Symposium, AEEES 2022 (2022)
57903	0.894	Daniel H.; Mantha B.R.K.; Soto B.G.D.	Towards A Review Of Building Energy Forecast Models	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019)
52403	0.891	Roth J.; Bailey A.; Choudhary S.; Jain R.K.	Spatial And Temporal Modeling Of Urban Building Energy Consumption Using Machine Learning And Open Data	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019)
32444	0.884	Nijim M.; Kanumuri V.; Albetaineh H.; Goyal A.	Intelligent Monitoring And Management Of Smart Buildings Using Machine Learning: Optimizing User Behavior And Energy Efficiency	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 (2023)
17892	0.881	Moveh S.; Merchán-Cruz E.A.; Abuhussain M.; Dodo Y.A.; Alhumaid S.; Alhamami A.H.	Deep Learning Framework Using Transformer Networks For Multi Building Energy Consumption Prediction In Smart Cities	Energies, 18, 6 (2025)
54013	0.881	Alymani M.; Mengash H.A.; Aljebreen M.; Alasmari N.; Allafi R.; Alshahrani H.; Elfaki M.A.; Hamza M.A.; Abdelmageed A.A.	Sustainable Residential Building Energy Consumption Forecasting For Smart Cities Using Optimal Weighted Voting Ensemble Learning	Sustainable Energy Technologies and Assessments, 57 (2023)
24850	0.878	Shahrabani M.M.N.; Apanaviciene R.	Evaluation Of Smart Building Integration Into A Smart City By Applying Machine Learning Techniques	Buildings, 15, 12 (2025)
13024	0.878	Ardabili S.; Mosavi A.; Várkonyi-Kóczy A.R.	Building Energy Information: Demand And Consumption Prediction With Machine Learning Models For Sustainable And Smart Cities	Lecture Notes in Networks and Systems, 101 (2020)