In the ever-evolving landscape of smart buildings and energy efficiency, a groundbreaking review is set to redefine how we manage indoor environmental conditions (IEC). Sadegh Haghighat, a leading researcher from the Myers-Lawson School of Construction at Virginia Tech, has published a comprehensive review in the journal *Indoor Environments* (translated to English as “Indoor Environments”), shedding light on the transformative potential of integrating deep learning and digital twin technologies.
Haghighat’s review, which analyzed 136 papers published between 2018 and 2024, offers a dual perspective on the current state and future possibilities of this integration. “The synergy between deep learning and digital twins is not just about improving energy efficiency; it’s about creating occupant-centric environments that adapt and respond in real-time,” Haghighat explains. This fusion of technologies promises to revolutionize how we approach indoor climate control, air quality, acoustics, and lighting.
The review highlights several key areas where this integration can have a significant impact. For instance, deep learning models can predict and adapt to changing environmental conditions, while digital twins provide a virtual replica of the physical space, enabling continuous monitoring and optimization. “This is not just about making buildings smarter; it’s about making them more responsive to the needs of the people who occupy them,” Haghighat notes.
One of the most compelling aspects of this research is its potential to drive commercial impacts in the energy sector. By leveraging deep learning and digital twins, buildings can achieve unprecedented levels of energy efficiency, reducing costs and environmental impact. This integration can also enhance occupant comfort and productivity, making it a win-win for both building owners and occupants.
The review also identifies current gaps in the body of knowledge and outlines future research directions. Haghighat emphasizes the need for further exploration of generative AI approaches and continuous learning strategies within digital twins. “As we move forward, it’s crucial to address these gaps and push the boundaries of what’s possible,” he says.
This research is poised to shape future developments in the field, offering a unified understanding of how deep learning enhances digital twin capabilities. As the energy sector continues to evolve, the insights from this review will be invaluable in driving innovation and achieving sustainable, occupant-centric indoor environments.