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

**信任**是一项可行性研究，旨在加快人工智能(AI)在建筑管理中的应用。尽管人工智能具有潜在的影响，但由于对其有效性和可靠性缺乏信任，建筑行业的决策者对采用人工智能犹豫不决。在医院、银行和学校等故障可能造成可怕后果的建筑中，情况尤其如此。这些类型的建筑通常更大，如果人工智能有效地工作，会提供更大的好处。决策者主要担心的是缺乏对可靠性和投资回报的保证。如果没有这些保证，决策者就不愿意通过集成通常不同的技术来实现自动化，或者以高昂的前期成本安装新的建筑管理系统(BMS)。系统集成商也不想因为向客户推荐未经验证的技术而冒着声誉的风险。尽管存在风险，但在建成环境中投资人工智能的潜在回报是巨大的。随着能源价格的上涨，企业现在越来越担心控制能源成本，而不是出于可持续性的考虑。其他运营效率也是可以实现的，从空间利用到人力。**信任**是一项研发计划，旨在展示人工智能在建筑管理中的好处，特别是在英国，尽管智能建筑行业是AI/ML创新的领先者，但在英国，智能建筑行业在采用方面落后于人。这项第一阶段可行性研究将建立一个联合体，并开发一个系统，该系统在第二阶段利用传感器和其他数据的洞察力，以人工智能和机器学习为回路中的专家来控制选定的建筑管理系统。这些技术在现有建筑中的第二阶段实际应用将在现场演示、案例研究、科学论文和文章中进行评估和强调，以增加人们对在建筑管理中使用人工智能的信任，并减少行业采用的障碍。

**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.