AI-powered HVAC in educational buildings: A net digital impact use case

Overview

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AI-powered HVAC systems offer a promising solution for sustainable energy use, addressing the significant energy consumption and CO2 emissions from HVAC systems, which account for 35-65% of building energy consumption.
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Our study, based on real-world data from 87 educational properties, provides quantified evidence demonstrating the impacts of AI-powered HVAC systems on energy efficiency and decarbonization.
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This study serves as a foundation for future research and development in this rapidly evolving field, paving the way for more sustainable and efficient building management practices for SISAB and similar organizations.

Key findings

Significant Positive Impacts

AI-Powered HVAC systems demonstrate substantial energy savings and carbon emission reductions, with a favorable 1:60 carbon cost-benefit ratio per year. Between 2019 and 2023, the study observed a 3.12% reduction in district heating consumption and an 8.93% decrease in electricity consumption.
Comprehensive Meta-Study

The study serves as a valuable meta-analysis, providing a robust reference point for estimating potential benefits of AI-powered HVAC systems in diverse settings. With an average yearly carbon saving of 64.8 tCO2e, these findings lay the groundwork for future research on AI's role in enhancing HVAC efficiency and sustainability.
Enhanced Carbon Reduction Potential

The research reveals opportunities for even greater carbon reductions in environments with more demanding heating, cooling, or air conditioning requirements. A comparative analysis between Stockholm and Boston showed that implementing the same solution in Boston could yield carbon emission savings 7 times higher or 604% greater than in Stockholm.
System-Wide Integration Challenges

Key challenges in integrating AI systems with legacy HVAC infrastructure were identified, including memory usage, computational intensity, and lifecycle limitations. This insight encourages a more comprehensive approach to AI implementation in HVAC systems, potentially leading to more efficient and adaptable building management solutions.