arXiv:2404.11716v2 Announce Type: replace Abstract: Building Energy Management (BEM) is central to reducing energy use and CO2 emissions in the building sector. Although IoT technologies now provide extensive operational data, heterogeneous data models, device descriptions, and contextual representations continue to limit semantic interoperability, limiting the development of generalisable, autonomous, context-aware BEM applications. Ontologies address this challenge by providing structured, machine-interpretable representations of building data, systems, and operational context. This survey e
The proliferation of IoT data in buildings is enabling more sophisticated energy management, while the urgency of climate change pushes for greater efficiency.
Improving semantic interoperability in Building Energy Management is crucial for developing generalizable AI applications that can reduce energy consumption and CO2 emissions significantly.
The adoption of ontological approaches will standardize diverse building data, enabling more autonomous, context-aware BEM solutions and unlocking greater efficiency gains.
- · Smart building technology providers
- · Real estate developers
- · Energy management software companies
- · AI developers
- · Legacy energy management systems
- · High-energy-consuming commercial buildings
Standardized data models will lead to more effective AI-driven building automation.
Reduced energy consumption in buildings will contribute to lower operational costs and decreased carbon footprints.
Widespread adoption of semantic BEM could accelerate smart city development and grid optimization through dynamic energy demand management.
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Read at arXiv cs.AI