Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Salah Alghamdi, Waiching Tang (), Sittimont Kanjanabootra and Dariusz Alterman
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Salah Alghamdi: Department of Building Engineering, College of Architecture and Planning, Imam Abdulrahman Bin Faisal University, Dammam 31451, Saudi Arabia
Waiching Tang: School of Architecture and Built Environment, The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia
Sittimont Kanjanabootra: School of Architecture and Built Environment, The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia
Dariusz Alterman: School of Science, Technology and Engineering, The University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD 4556, Australia

Sustainability, 2024, vol. 16, issue 8, 1-18

Abstract: 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 R 2 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.

Keywords: artificial neural networks; architectural building design parameters; energy consumption; educational buildings; thermal comfort (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (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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