Carlos Enrique Torres-Aguilar; Pedro Moreno-Bernal; Diego Gabriel Rossit; Sergio Nesmachnow; J. Arce; Edgar Vicente Macias-Melo · 10.3390/data11100258 https://doi.org/10.3390/data11100258 · 2026 · Building ventilation · Numerical / CFD
Data
A Data-Driven Validation Methodology for a Multi-City Solar Chimney Dataset in Smart-City Applications Carlos Enrique Torres-Aguilar; Pedro Moreno-Bernal; Diego Gabriel Rossit; Sergio Nesmachnow; J. Arce; Edgar Vicente Macias-Melo This article presents a methodology for a validated multi-city parametric dataset for data-driven assessment of single-channel solar chimneys as energy-efficient components of smart-city buildings. The dataset covers Mexico City, Hermosillo, Merida, Monterrey, Toluca, and Villahermosa across tropical, dry, and temperate Koppen–Garcia climates. The proposed dataset combines nine absorber-wall materials, four cardinal orientations, and 24 representative days (the warmest and coldest day of each of the twelve calendar months), generating 5184 CSV files (6×24×4×9=5184). A transient global energy balance model calculates heat conduction, natural convection, radiative exchange, and buoyancy-driven ventilation under real climatic conditions included in the dataset. The GEB model is experimentally validated against an aluminum-absorber prototype. The energy balance model discretizes massive and phase-change absorber walls into 21 layers and represents latent thermal storage through an effective specific heat formulation. Consequently, the ∼133.64 GB dataset records time-dependent temperatures, airflow rates, air changes per hour, instantaneous efficiency, and layer-resolved liquid fractions. A proposed open Python 3.12.7 toolkit also organizes the heterogeneous file hierarchy and enables reproducible, scalable retrieval of specific configurations. The resource supports big-data analytics, surrogate modeling, machine-learning training, predictive performance assessment, and data-driven decision-making for passive ventilation. Furthermore, the dataset supports future integration with IoT-based building monitoring systems for model calibration, operational prediction, and automated energy management. Overall, the proposed resource advances scalable, climate-responsive solutions for resilient, energy-efficient, and sustainable smart cities.
Building ventilation
Numerical / CFD
2026
Data
journal
other
no
2026-10-02 12:00:00 00001849
a data-driven validation methodology for a multi-city solar chimney dataset in smart-city applications
2026
Data
Carlos Enrique Torres-Aguilar; Pedro Moreno-Bernal; Diego Gabriel Rossit; Sergio Nesmachnow; J. Arce; Edgar Vicente Macias-Melo
10.3390/data11100258
Abstract
This article presents a methodology for a validated multi-city parametric dataset for data-driven assessment of single-channel solar chimneys as energy-efficient components of smart-city buildings. The dataset covers Mexico City, Hermosillo, Mérida, Monterrey, Toluca, and Villahermosa across tropical, dry, and temperate Köppen–García climates. The proposed dataset combines nine absorber-wall materials, four cardinal orientations, and 24 representative days (the warmest and coldest day of each of the twelve calendar months), generating 5184 CSV files (6×24×4×9=5184). A transient global energy balance model calculates heat conduction, natural convection, radiative exchange, and buoyancy-driven ventilation under real climatic conditions included in the dataset. The GEB model is experimentally validated against an aluminum-absorber prototype. The energy balance model discretizes massive and phase-change absorber walls into 21 layers and represents latent thermal storage through an effective specific heat formulation. Consequently, the ∼133.64 GB dataset records time-dependent temperatures, airflow rates, air changes per hour, instantaneous efficiency, and layer-resolved liquid fractions. A proposed open Python 3.12.7 toolkit also organizes the heterogeneous file hierarchy and enables reproducible, scalable retrieval of specific configurations. The resource supports big-data analytics, surrogate modeling, machine-learning training, predictive performance assessment, and data-driven decision-making for passive ventilation. Furthermore, the dataset supports future integration with IoT-based building monitoring systems for model calibration, operational prediction, and automated energy management. Overall, the proposed resource advances scalable, climate-responsive solutions for resilient, energy-efficient, and sustainable smart cities.
Abstract by the authors, reproduced under the CC BY 4.0 licence. Original publication via the link below.
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