Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Alghamdi, Salah; Tang, Waiching; Kanjanabootra, Sittimont; Alterman, Dariusz

Title: Optimising Building Energy and Comfort Predictions with Intelligent Computational Model
Creator: Alghamdi, Salah; Tang, Waiching; Kanjanabootra, Sittimont; Alterman, Dariusz
Relation: Sustainability Vol. 16, Issue 8, no. 3432
Publisher Link: http://dx.doi.org/10.3390/su16083432
Publisher: MDPI AG
Resource Type: journal article
Date: 2024
Description: 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.
Subject: artificial neural networks; architectural building design parameters; energy consumption; educational buildings; thermal comfort
Identifier: http://hdl.handle.net/1959.13/1503403
Identifier: uon:55326
Identifier: ISSN:2071-1050
Language: eng
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