How AI and ML can help reduce energy usage and carbon emissions in buildings

Dani Stern
Additional contact information
Dani Stern: Honeywell Building Automation, USA

Corporate Real Estate Journal, 2024, vol. 14, issue 1, 66-74

Abstract: Commercial buildings play a significant role in global energy consumption and carbon emissions, making their sustainability imperative. This paper explores how artificial intelligence (AI) and machine learning (ML) technologies offer innovative solutions to reduce energy usage and carbon footprint in buildings. By leveraging AI and ML algorithms, building automation systems can optimise energy consumption, enhance occupant comfort and predict future energy demands. The integration of AI and ML into existing building controls requires managing and integrating diverse data sources, followed by cloud-based processing for advanced analytics. The benefits include increased energy efficiency, reduced utility costs and improved occupant experience. Readers will gain insights into: 1) understanding AI and ML’s role in optimising energy usage and reducing carbon emissions in buildings; 2) integrating AI and ML into building automation systems for predictive energy management; 3) overcoming challenges in data integration and cybersecurity for effective implementation; 4) leveraging AI-driven analytics for real-time monitoring and decision making; and 5) achieving sustainability goals and enhancing building resilience through AI-powered solutions. By implementing AI and ML technologies, building owners and operators can not only improve energy efficiency but also strengthen their competitive edge, attract environmentally conscious tenants and contribute to global sustainability efforts.

Keywords: artificial intelligence; machine learning; energy efficiency; building automation; sustainability; occupant comfort (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://hstalks.com/article/8733/download/ (application/pdf)
https://hstalks.com/article/8733/ (text/html)
Requires a paid subscription for full access.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aza:crej00:y:2024:v:14:i:1:p:66-74

Access Statistics for this article

More articles in Corporate Real Estate Journal from Henry Stewart Publications
Bibliographic data for series maintained by Henry Stewart Talks ().