AUG - 20th
Are your buildings and equipment in the best of health?
Contextual AI can let you know.
Across the globe, the focus of humans has shifted from illness management to wellness management. Gone are the reactive methods of waiting to fall sick. Today, people prefer staying updated on their health parameters through wearable gadgets and applications that track physical activity, food habits and more. In the future, we might be able to see our comprehensive health status on demand. Data models could start predicting when and why we might face a particular health issue, using multiple parameters such as food intake, sleep cycle, activity, medical history, family history, age, gender, race, local weather, altitude and more.
As we speak, this paradigm shift is already underway for building and equipment operations. Your AI/ML strategy can create data models that deliver various objectives of equipment by utilizing their unique context and reading their ‘symptoms’ correctly through a predictive platform. You can take proactive action to maintain or improve health, instead of reacting when failure occurs.
The main objective of any equipment in a building is ensuring compliance and comfort. The business processes around such equipment help the building maintain good health and operate with maximum efficiency at the lowest cost. Ideally, equipment data should be normalized separately for each of these objectives – despite the data being the same. An AI model for equipment health should be able to predict the four Ws of equipment failure – Whether the equipment is going to fail, When it might fail, What component(s) in the equipment might fail, and Why it/they might fail.
An AI model for compliance should normalize for completely unhealthy assets, but should cater to partially healthy assets while designing a constraint for efficient usage – since its objective would be to predict the best operational condition to achieve compliance.
To improve efficiency, two types of AI models can be considered. The first type involves improving the measurements that control the equipment to run efficiently while achieving its compliance objective. E.g. forecasting the calibration needs of important sensors like outside air temperature sensors, as they are impacted by roof radiation differently over a full day. The other type involves predicting the most optimized configuration to get the optimum balance between compliance and efficiency.
When dealing with a single site, AI/ML models can learn from the characteristics of the site. For most enterprises, however, multiple sites or multiple equipment of the same type are involved, e.g. a large pizza chain with restaurants across the country, or a large retail chain with similar roof top units on all of its stores. In such cases, the model design should recognize the commonality across sites and benefit from the experience of millions of equipment, but also include a component which recognizes the differences specific to the characteristics of individual sites. Thus, an intelligently designed AI/ML model learns from both – the shared learnings across hundreds of sites, and the unique learning from each site.
It’s time to adopt models that consider multiple perspectives with the right insights in context, and gain significant benefits in compliance, health and efficiency across buildings and locations. Diagnose the symptoms, act proactively with the right tools and do not allow your building and equipment operations to deteriorate.