Smart City Gnosys

Smart city article details

Title	Data-Driven Approach To Estimate Urban Heat Island Impacts On Building Energy Consumption
ID_Doc	17399
Authors	Tehrani A.A.; Sobhaninia S.; Nikookar N.; Levinson R.; Sailor D.J.; Amaripadath D.
Year	2025
Published	Energy, 316
DOI	http://dx.doi.org/10.1016/j.energy.2025.134508
Abstract	Urban heat island effects can significantly increase building energy consumption. Assessing the impact of the urban heat island on building energy use is challenging due to temperature variations. Scalable building models and cooling energy consumption data are essential for accurate load demand predictions. This paper presents a data-driven method to predict cooling load intensity (heat removed per unit floor area to maintain the setpoint in conditioned spaces), under urban heat island conditions in residential buildings in Phoenix, Arizona, using a synthetic dataset of 27,681 buildings. The approach incorporates physics-based parametric modeling using building geometrical features, urban heat island intensity simulation through the Urban Weather Generator, and cooling load intensity estimation via EnergyPlus and OpenStudio. Machine learning models, including Extreme Gradient Boosting, Gaussian Process Regression, Random Forest, Support Vector Regression, and Deep Neural Networks, are employed for cooling load predictions. Urban building energy performance analysis indicates that the Deep Neural Network model performs best in estimating cooling load intensity, achieving a high coefficient of determination of 0.98. These findings support informed decision-making to enhance building energy efficiency, reduce consumption, and facilitate large-scale smart city planning. © 2025 Elsevier Ltd
Author Keywords	Cooling load intensity; Energy consumption; Energy efficiency; Energy management; Machine learning; Urban heat island

Similar Articles

Id	Similarity	Authors	Title	Published
57240	0.891	Shafiq M.; Bhavani N.P.G.; Venkata Naga Ramesh J.; Veeresha R.K.; Talasila V.; Sulaiman Alfurhood B.	Thermal Modeling And Machine Learning For Optimizing Heat Transfer In Smart City Infrastructure Balancing Energy Efficiency And Climate Impact	Thermal Science and Engineering Progress, 54 (2024)
52403	0.88	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)
23280	0.879	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)
2170	0.879	Mohseni-Gharyehsafa B.; Hussain S.; Fahy A.; De Rosa M.; Pallonetto F.	A Hybrid Gaussian Process-Integrated Deep Learning Model For Retrofitted Building Energy Optimization In Smart City Ecosystems	Applied Energy, 388 (2025)
13039	0.871	Chaganti R.; Rustam F.; Daghriri T.; Díez I.D.L.T.; Mazón J.L.V.; Rodríguez C.L.; Ashraf I.	Building Heating And Cooling Load Prediction Using Ensemble Machine Learning Model	Sensors, 22, 19 (2022)
54322	0.871	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)
32444	0.868	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)
19567	0.867	Tyulepberdinova G.; Kunelbayev M.; Shiryayeva O.; Sakypbekova M.; Sarsenbayev N.; Bayandina G.	Development Of A Digital Twin Of A Network Of Heating Systems For Smart Cities On The Example Of The City Of Almaty	International Journal of Electrical and Computer Engineering, 14, 6 (2024)
17892	0.867	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)
762	0.864	Wu J.; Nguyen S.; Alahakoon D.; De Silva D.; Mills N.; Rathnayaka P.; Moraliyage H.; Jennings A.	A Comparative Analysis Of Machine Learning-Based Energy Baseline Models Across Multiple Building Types	Energies, 17, 6 (2024)