Optimizing daylight and energy consumption for climate change adaptation: integrating an artificial neural network model with a multi-objective optimization approach

Abd. Salam, Nur Nasuha and Lannon, Simon ORCID: https://orcid.org/0000-0003-4677-7184 2023. Optimizing daylight and energy consumption for climate change adaptation: integrating an artificial neural network model with a multi-objective optimization approach. Presented at: The 1st International Online Conference on Buildings, Online, 24–26 October 2023. Published in: Arditi, David ed. Proceedings of The 1st International Online Conference on Buildings. Engineering Proceedings. , vol.53 (1) MDPI, p. 37. 10.3390/IOCBD2023-15202

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Abstract

Machine learning models have been proven for their capability to improve the computational efficiency of building performance simulations. However, studies on their reliability to produce Pareto front solutions for multi-objective optimization are limited, particularly for climate adaptation studies. This study proposed a dependable workflow through which to integrate an artificial neural network (ANN) model with energy consumption and daylight multi-objective optimization for climate change adaptation. The trained ANN model attained high R2 scores with RMSE scores of 2.23 and 4.52 for UDI and cooling EUI, respectively. Statistical hypothesis analysis of the Pareto front solutions produced via conventional simulation-based and ANN-based optimization shows that the two models have no significant difference, indicating the reliability of the proposed workflow.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Architecture
Publisher:	MDPI
ISSN:	2673-4591
Date of First Compliant Deposit:	24 November 2023
Date of Acceptance:	24 November 2023
Last Modified:	28 Nov 2023 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/164352

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