Thermal performance of historic buildings in Mexico: An analysis of passive systems under the influence of climate change

Vázquez-Torres, Claudia Eréndira; Bienvenido-Huertas, David; Beizaee, Arash; Bassam, A.; Torres, M. Jiménez

doi:10.1016/j.esd.2022.12.002

Cited by 9 publications

(1 citation statement)

References 31 publications

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“…Heat gain or loss through active and passive strategies involving ABDPs (e.g., cooling and heating set-point temperature, walls, windows, and mechanical ventilation rate per area) are critical factors in regulating the indoor thermal environment of buildings [40,41]. The EC required for heating and cooling, in order to maintain appropriate levels of thermal comfort, is consequently influenced by the indoor thermal environment.…”

Section: Determination Of Architectural Building Design Parameters An...mentioning

confidence: 99%

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Alghamdi,

Tang,

Kanjanabootra

et al. 2024

Sustainability

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Building performance prediction is a significant area of research, due to its potential to enhance the efficiency of building energy management systems. Its importance is particularly evident when such predictions are validated against field data. This paper presents an intelligent computational model combining Monte Carlo analysis, Energy Plus, and an artificial neural network (ANN) to refine energy consumption and thermal comfort predictions. This model addresses various combinations of architectural building design parameters and their distributions, effectively managing the complex non-linear relationships between the response variables and predictors. The model’s strength is demonstrated through its alignment with R2 values exceeding 0.97 for both thermal discomfort hours and energy consumption during the training and testing phases. Validation with field investigation data further confirms its accuracy, demonstrating average relative errors below 2.0% for total energy consumption and below 1.0% for average thermal discomfort hours. In particular, an average underestimation of −12.5% in performance discrepancies is observed when comparing the building energy simulation model with field data, while the intelligent computational model presented a smaller overestimation error (of +8.65%) when validated against the field data. This discrepancy highlights the model’s potential and reliability for the simulation of real-world building performance metrics, marking it as a valuable tool for practitioners and researchers in the field of building sustainability.

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Section: Determination Of Architectural Building Design Parameters An...mentioning

confidence: 99%

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Alghamdi,

Tang,

Kanjanabootra

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

Cluster Analysis as a Tool for the Territorial Categorization of Energy Consumption in Buildings Based on Weather Patterns

May Tzuc,

Jiménez Torres,

Rodriguez

et al. 2023

Studies in Computational Intelligence

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Green Roofs and Other Nature-Based Solutions in Barcelona: Environmental Benefits, and Physical and Mental Well-Being

Bosch¹,

Palacio²,

Alcántara³

et al. 2023

Lecture Notes in Civil Engineering

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Thermal performance of historic buildings in Mexico: An analysis of passive systems under the influence of climate change

Cited by 9 publications

References 31 publications

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Cluster Analysis as a Tool for the Territorial Categorization of Energy Consumption in Buildings Based on Weather Patterns

Green Roofs and Other Nature-Based Solutions in Barcelona: Environmental Benefits, and Physical and Mental Well-Being

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