Energy-Smart Buildings: A Conceptual Framework to Improve Buildings' Energy Performance

Seresht, Nima Gerami

Abstract

Buildings contribute to nearly 40% of the carbon dioxide emissions in the United Kingdom, and a significant proportion of this energy is consumed to control the indoor environment (i.e., heating, cooling, and lighting). Several efforts have been undertaken to reduce the energy consumption of buildings. However, existing approaches often fail to capture a comprehensive image of the buildings and their occupants and, consequently, fail to forecast their energy consumption accurately. This paper aims to address this gap by proposing a novel framework for forecasting occupants' energy behaviour based on real-time video data processing and agent-based modelling (ABM) and, consequently, predicting buildings' energy consumption. The proposed framework is expected to improve the accuracy of energy simulation techniques by capturing the most realistic features of the building and its occupants through a mix of data-and law-driven techniques. The architecture of the proposed framework is presented in this paper as a proof of concept, and the feasibility of this framework is discussed.

Citation

Seresht, N. G. (2022, September). Energy-Smart Buildings: A Conceptual Framework to Improve Buildings' Energy Performance. Presented at Association of Researchers in Construction Management 38th Annual Conference, Glasgow

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Association of Researchers in Construction Management 38th Annual Conference
• Start Date: Sep 1, 2022
• Acceptance Date: May 1, 2022
• Online Publication Date: Jun 1, 2022
• Publication Date: Jun 1, 2022
• Deposit Date: Feb 19, 2025
• Journal: 38 th Annual ARCOM Conference
• Peer Reviewed: Peer Reviewed
• Pages: 622-631
• Book Title: Association of Researchers in Construction Management 38th Annual Conference Proceedings
• ISBN: 9780995546363
• Keywords: low carbon; energy behaviour; multi-agent modelling; energy simulation
• Public URL: https://durham-repository.worktribe.com/output/2994167