TRUST - Improving TRUST in artificial intelligence and machine learning for critical building management

**TRUST** is a feasibility study that aims to accelerate the adoption of Artificial Intelligence (AI) in building management. Despite its potential impact, decision makers in the building industry are hesitant to adopt AI due to a lack of trust in its effectiveness and reliability. This is particularly true in buildings where failures could have dire consequences, such as hospitals, banks and schools. These types of buildings are typically larger and offer even greater benefits if the AI were to work effectively.

The primary concern for decision makers is the lack of guarantees for reliability and return on investment. Without these guarantees, decision makers are not willing to enable automation by integrating often disparate technologies, or fitting new building management systems (BMS) at significant upfront cost. System integrators also do not want to risk their reputation by recommending unproven technologies to their customers.

Despite the risks, the potential payback for investment in AI in the built environment is significant. With rising energy prices, businesses are now increasingly concerned about controlling energy costs beyond sustainability reasons. Other operational efficiencies are also achievable, from space utilisation to manpower.

**TRUST** is a research and development initiative that aims to demonstrate the benefits of AI in building management, particularly in the United Kingdom where the smart building industry lags behind in adoption despite being a leader in AI/ML innovation. This Phase 1 feasibility study will establish a consortium and develop a system that in Phase 2 utilises insights from sensor and other data with AI and machine learning as the expert in the loop to control selected building management systems. The Phase 2 real-world application of these technologies in an existing building will be evaluated and highlighted in live demonstrations, case studies, scientific papers, and articles to increase trust in the use of AI in building management and reduce barriers to adoption in the industry.