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Published VersionCC BY V4.0, Open Access
Journal article
Optimising Building Energy and Comfort Predictions with Intelligent Computational Model
Sustainability, Vol.16(8), pp.1-18
2024
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 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.
Details
- Title
- Optimising Building Energy and Comfort Predictions with Intelligent Computational Model
- Authors
- Salah Alghamdi - Imam Abdulrahman Bin Faisal UniversityWaiching Tang (Corresponding Author) - University of Newcastle AustraliaSittimont Kanjanabootra - University of Newcastle AustraliaDariusz Alterman - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Sustainability, Vol.16(8), pp.1-18
- Publisher
- MDPI AG
- Date published
- 2024
- DOI
- 10.3390/su16083432
- ISSN
- 2071-1050
- Copyright note
- © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Data Availability
- Dataset available on request from the authors.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991026546702621
- Output Type
- Journal article
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