Author: Bo Nørregaard Jørgensen (University of Southern Denmark) - Smart buildings harness AI-driven Energy Management Systems (EMS) to optimize HVAC, lighting, and appliances, significantly reducing energy consumption and costs while maintaining occupant comfort. These systems rely on IoT sensor data and machine learning, but also raise privacy and ethical concerns due to extensive data collection on occupants.
In the EU, the forthcoming AI Act will classify AI applications by risk, imposing transparency, explainability, and human oversight requirements on high-risk systems that significantly affect building functions. Non-compliant black-box models could face severe penalties. Meanwhile, the GDPR mandates strict handling of personal data, including storage, minimization, and purpose limitation. Sensors tracking occupancy or preferences can qualify as personal data once aggregated, making compliance complex.
Developers face trade-offs between compliance and performance: restricting data collection and ensuring explainability can reduce predictive accuracy. However, meeting these requirements can foster trust, improve user acceptance, and avert legal risks. “Compliance by design” strategies—such as privacy-preserving machine learning, data anonymization, or federated approaches—can uphold occupant rights while preserving optimization potential. Collaboration between regulators, researchers, and industry stakeholders is vital. By balancing legal obligations with technical innovations, AI-driven EMS can provide sustainable energy solutions aligned with privacy, transparency, and accountability. Such alignment fosters trust, innovation, and sustainability.