Enhancing building energy consumption prediction introducing novel occupant behavior models with sparrow search optimization and attention mechanisms: A case study for forty-five buildings in a university community

Zhang, Chengyu, Luo, Zhiwen ORCID: https://orcid.org/0000-0002-2082-3958, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 and Zhao, Tianyi 2024. Enhancing building energy consumption prediction introducing novel occupant behavior models with sparrow search optimization and attention mechanisms: A case study for forty-five buildings in a university community. Energy 294 , 130896. 10.1016/j.energy.2024.130896 Item availability restricted.

PDF - Accepted Post-Print Version Restricted to Repository staff only until 11 March 2025 due to copyright restrictions. Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (3MB)

Abstract

The escalating energy and environmental crises underline the imperative for sustainable cities and societies. For effective and real-time energy management, this study proposes an enhanced building energy consumption prediction system. It introduces a novel concept named region-wide occupant energy-use behavior probability and incorporates it into the input system, which better reflects real-time and complex energy-occupant-environment interactions in buildings. In addition, it integrates the squeeze-and-excitation attention mechanism, sparrow search algorithm, and convolutional neural network processes for optimizing data processing and hyperparameter selection. Validation in seven sample buildings demonstrates the proposed prediction system has a better balance between time and accuracy, reducing 36.32% MAPE and 31.20% CV-RMSE on average compared to all other prediction systems, only with 118.354s of extra time consumption increase compared to the least time-consuming method. Furthermore, this study discusses methods for selecting suitable input systems and algorithms based on building type, data collection conditions, accuracy, and time consumption. Finally, the enhanced prediction is applied to forty-five buildings in a university community, yielding a 12.35% MAPE and a 0.1707 CV-RMSE on average, reaffirming its superiority and practicality.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Architecture Engineering
Additional Information:	License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher:	Elsevier
ISSN:	0360-5442
Date of First Compliant Deposit:	14 March 2024
Date of Acceptance:	1 March 2024
Last Modified:	19 Mar 2024 20:02
URI:	https://orca.cardiff.ac.uk/id/eprint/167252

Actions (repository staff only)

Edit Item